Recommended medical content methods, procedures, products, devices, equipment, and storage media

Abstract

This disclosure provides a method, program product, apparatus, device, and storage medium for recommending medical content, comprising: obtaining a first set of medical content, including at least one piece of medical content describing medical viewpoints, wherein the at least one piece of medical content has a content information density score and a description depth rating; obtaining historical data of users, including a rating of the user's level of understanding of the corresponding medical viewpoints; determining a rating score for at least one piece of medical content based on the level of understanding rating and the description depth rating; determining a score for the user's associated interactive behavior towards at least one piece of medical content; determining a first weight for the rating score and a second weight for the associated interactive behavior score based on the information density score; obtaining a ranking score for at least one piece of medical content; and presenting at least one piece of medical content to the user.

Description

TECHNICAL FIELD

[0001] The present application relates to recommendation algorithms, and in particular to a method, program product, apparatus, device, storage medium for recommending medical content. BACKGROUND

[0002] Existing medical content recommendation algorithms generally mainly rely on two types of basic data: the first type is basic user behavior data, such as whether a user has read certain content; the second type is basic content labels, such as domain (e.g., different disease domains, different departments, etc.), type (e.g., academic conference invitations, paper content), and onboarding time, etc. meta information to which the content belongs. With the support of these data, the currently widely used medical content recommendation techniques mainly include Collaborative Filtering and Content-based Filtering. Collaborative Filtering recommends similar user-liked content by mining group behavior patterns in user-item interaction records; Content-based Filtering recommends content similar to the user's historical interests according to the attribute characteristics of the item itself.

[0003] However, although these methods have achieved remarkable results in e-commerce, news, and short video scenarios, they have obvious shortcomings when facing academic cultivation scenarios. On the one hand, the existing recommendation mechanism lacks a comprehensive grasp of the user's academic growth path and cannot dynamically adjust according to the user's learning goals at different stages. The existing recommendation logic still aims at "more clicks" and "longer stay", rather than being oriented towards mastering knowledge and expanding cognitive boundaries. On the other hand, such algorithms are prone to exacerbate the "echo chamber" effect, i.e., users constantly receive familiar or preferred content, leading to limited vision and difficulty in accessing cross-disciplinary or challenging new materials, which is particularly detrimental to academic innovation and systematic knowledge construction.

[0004] In addition, existing methods generally ignore the differences in users' mastery of academic content. For beginners, directly recommending high-difficulty papers may be counterproductive; for advanced users, repeatedly recommending basic content will waste cognitive resources. Such recommendation without hierarchy and progression not only reduces learning efficiency, but also fails to support the long-term academic development of users.

[0005] Therefore, it is desirable to have a medical recommendation content that can ensure its attractiveness while promoting users' understanding and learning of medical perspectives, thereby achieving the goal of academic cultivation of users. SUMMARY

[0006] The present disclosure provides a method for recommending medical content, comprising: obtaining a first medical content set, the first medical content set comprising at least one medical content describing a medical viewpoint, the at least one medical content having a content information density score and a description depth ranking of the medical viewpoint, wherein the information density score indicates a richness of the at least one medical content; obtaining historical data of a user, the historical data comprising a level of understanding ranking of the user for a corresponding medical viewpoint; determining a ranking score of the at least one medical content based on the level of understanding ranking of the user for the medical viewpoint described by the at least one medical content and the description depth ranking of the medical viewpoint by the at least one medical content; and determining at least one attribute parameter of the at least one medical content, determining a relevant interaction behavior score of the user for the at least one medical content based on the at least one attribute parameter and the historical data, wherein the historical data further comprises a relevant interaction behavior of the user for the at least one attribute parameter previously; determining a first weight of the ranking score and a second weight of the relevant interaction behavior score based on the information density score; obtaining a ranking score of the at least one medical content based on the ranking score, the first weight, the relevant interaction behavior score, and the second weight; and presenting the at least one medical content to the user in descending order of the ranking score of the at least one medical content.

[0007] The method according to the embodiments of the present disclosure, wherein the determining the ranking score of the at least one medical content comprises: determining a difference between the level of understanding ranking of the user for the medical viewpoint described by the at least one medical content and the description depth ranking of the medical viewpoint by the at least one medical content; and configuring the ranking score of the at least one medical content based on the difference.

[0008] The method according to the embodiments of the present disclosure, wherein the determining the ranking score of the at least one medical content comprises: determining whether the difference is within a predetermined difference range; configuring a first ranking score for the at least one medical content in response to the difference being within the predetermined difference range; and configuring a second ranking score for the at least one medical content in response to the difference not being within the predetermined difference range, wherein the second ranking score is lower than the first ranking score.

[0009] The method according to the embodiments of the present disclosure, wherein the configuring the first ranking score for the at least one medical content comprises: adjusting the first ranking score based on the level of understanding ranking of the user for the medical viewpoint described by the at least one medical content.

[0010] The method according to the embodiments of the present disclosure further comprises: in response to one medical content in the at least one medical content describing a plurality of medical viewpoints, determining a plurality of hierarchical scores for the one medical content respectively; and determining an average value of the plurality of hierarchical scores as a hierarchical score of the one medical content.

[0011] The method according to the embodiments of the present disclosure, wherein the understanding level rating is obtained through an interaction control provided in the medical content previously read by the user, and wherein the interaction control is configured to: in response to the user selecting the interaction control, present one or more interaction contents for a medical viewpoint to the user, and determine the understanding level rating of the user for the medical viewpoint based on the interaction of the user with the one or more interaction contents.

[0012] The method according to the embodiments of the present disclosure, wherein the interaction control is configured to: in response to the user obtaining a plurality of understanding level ratings for a first medical viewpoint in a plurality of medical viewpoints by selecting a plurality of interaction controls for the first medical viewpoint, determine an average value of the plurality of understanding level ratings as the understanding level rating of the user for the first medical viewpoint.

[0013] The method according to the embodiments of the present disclosure, wherein the presenting the at least one medical content to the user in descending order of the ranking scores of the at least one medical content comprises: in response to the ranking scores of the at least one medical content being the same, presenting the at least one medical content to the user in descending order of reading amounts of the at least one medical content.

[0014] The method according to the embodiments of the present disclosure, wherein the at least one attribute parameter comprises one or more of a drug name and a drug brand.

[0015] The method according to the embodiments of the present disclosure, wherein the obtaining the ranking score of the at least one medical content comprises: adding a product of the hierarchical score multiplied by the first weight, a product of the associated interaction behavior score multiplied by the second weight, and the information density score to obtain the ranking score of the at least one medical content.

[0016] The method according to the embodiments of the present disclosure, wherein the associated interaction behavior comprises a direct behavior, and the method further comprises: in response to determining through the historical data that the user has the direct behavior for the at least one attribute parameter, directly adjusting the associated interaction behavior score of the medical content corresponding to the at least one attribute parameter.

[0017] The method according to an embodiment of the present disclosure, wherein the associated interaction behavior comprises an indirect behavior, the method further comprising: in response to determining, by the historical data, that the user has the indirect behavior for the at least one attribute parameter, determining an interaction degree of the indirect behavior, adjusting the associated interaction behavior score of the medical content corresponding to the at least one attribute parameter based on the interaction degree of the indirect behavior.

[0018] The method according to an embodiment of the present disclosure, wherein the determining the associated interaction behavior score of the user for the at least one medical content comprises: determining, based on the historical data, a time interval from occurrence time of the associated interaction behavior to current time, and attenuating the associated interaction behavior score based on the time interval.

[0019] The method according to an embodiment of the present disclosure, wherein the at least one medical content included in the first medical content set comprises medical content unread by the user, the method further comprising: obtaining a second medical content set comprising at least one medical content read by the user, and setting the second medical content set after the first medical content set for presentation to the user.

[0020] The method according to an embodiment of the present disclosure, wherein the method further comprises: determining a publication time of at least one medical content read by the user, and presenting the at least one medical content read by the user to the user in a descending order of the publication time.

[0021] The method according to an embodiment of the present disclosure, wherein the size of the information density score is positively correlated with the size of the first weight.

[0022] The method according to an embodiment of the present disclosure, wherein the size of the information density score is negatively correlated with the size of the second weight.

[0023] An apparatus for recommending medical content according to an embodiment of the present disclosure comprises: a medical content module configured to obtain a first medical content set comprising at least one medical content describing a medical viewpoint, the at least one medical content having a depth level of description of the medical viewpoint; a historical data module configured to obtain historical data of a user, the historical data comprising a level of understanding of the user for a corresponding medical viewpoint; a level score module configured to determine a level score of the at least one medical content based on the level of understanding of the user for the medical viewpoint described by the at least one medical content and the depth level of description of the medical viewpoint by the at least one medical content; and a content presentation module configured to sort the at least one medical content based at least in part on the level score for presentation to the user.

[0024] Embodiments of the present disclosure provide an apparatus for recommending medical content, comprising: one or more processors; and one or more memories having computer executable programs stored therein, which, when executed by the processors, perform the method as described above.

[0025] Embodiments of the present disclosure provide a computer program product comprising computer programs or instructions, which, when executed by a processor, implement the method as described above.

[0026] Embodiments of the present disclosure provide a computer readable storage medium having computer executable instructions stored thereon, which, when executed by a processor, implement the method as described above.

[0027] The method for recommending medical content according to the present disclosure can ensure that the recommended content is attractive while promoting the user's understanding and learning of medical viewpoints, thereby achieving the goal of academic cultivation of the user. For example, the method not only focuses on the user's immediate browsing preferences, but also takes into account the goal of academic cultivation, thereby recommending medical content that describes medical viewpoints suitable for the user's current cognitive level and has heuristic properties. Therefore, the method can not only maintain the user's engagement, but also effectively promote their understanding and long-term accumulation of complex medical content, thereby achieving the transition from "recommending information" to "cultivating academic ability". BRIEF DESCRIPTION OF DRAWINGS

[0028] The above and other aspects, features and advantages of certain embodiments of the present disclosure will become more apparent from the following description taken in conjunction with the accompanying drawings, in which:

[0029] Figure 1 is a schematic diagram of the flow of the method for recommending medical content according to embodiments of the present disclosure.

[0030] Figure 2 is a schematic diagram of the flow of the method for recommending medical content according to embodiments of the present disclosure.

[0031] Figure 3 An apparatus for recommending medical content according to embodiments of the present disclosure is shown.

[0032] Figure 4 A schematic diagram of an apparatus 400 for recommending medical content according to embodiments of the present disclosure is shown.

[0033] Figure 5 A computing device for recommending medical content according to embodiments of the present disclosure is shown. DETAILED DESCRIPTION

[0034] Before undertaking a detailed description of the principles underlying the present disclosure, it can be advantageous to set forth definitions of certain words and phrases so that the patent application can be understood in the broadest context possible. The terms "including," "includes," "comprising," "comprise," "comprises," and "containing," "contain," are inclusive, open-ended transition terms with that term encompassing the same open-ended meaning as the term "comprising" i.e., these terms will be understood to encompass the terms "consisting of" and "consisting essentially of." The phrase "consisting essentially of" means the composition as a whole with additional components not named. When the phrase is "consisting essentially of" it denotes a composition that includes substantially all the active ingredients of the composition. The phrase "at least one of" follows the same rules as "one or more of" i.e., "at least one of A and B" will mean A or B or A and B.

[0035] Definitions for other certain words and phrases are provided throughout this disclosure. Those of ordinary skill in the art will understand that in many, if not most instances, such definitions apply to prior and future uses of such defined words and phrases.

[0036] The principles of the present disclosure described in this patent application document are illustrated by various embodiments in the following description and drawings. These embodiments are only meant to be illustrative and should not be construed as limiting the scope of the present disclosure in any way. Those of ordinary skill in the art will understand that the principles of the present disclosure can be implemented in any suitably arranged system or device. In some cases, the actions described in the present disclosure can be performed in different orders and still achieve the desired results. Moreover, the processes depicted in the figures do not necessarily require the particular order shown, or sequential order, to achieve the desired results. In certain implementations, multi-tasking and parallel processing can be advantageous.

[0037] The text and drawings are provided only as examples of the present disclosure. They should not be construed as limiting the scope of the claims appended to this patent application in any way. Throughout the drawings, like reference numerals generally refer to like elements. Although certain embodiments and examples have been provided, based on the content of this disclosure, it will be clear to those skilled in the art that changes can be made to the embodiments and examples shown without departing from the scope of the present disclosure.

[0038] Figure 1 is a schematic diagram of a flow of a method for recommending medical content according to an embodiment of the present disclosure. As shown in Figure 1 the method 1000 can include steps S1110-S1400.

[0039] At step S1100, a first set of medical content can be obtained, the first set of medical content including at least one medical content describing a medical viewpoint, the at least one medical content having a content information density score and a description depth ranking of the medical viewpoint.

[0040] In one embodiment, the first medical content set can include or can be a set of medical content for display to a user. The user can include doctors of different departments or different specialties. The medical viewpoints can include different brands. The first medical content set can include at least one medical content describing a medical viewpoint. For example, the medical contents included in the first medical content set can each describe one or more medical viewpoints. The description depth hierarchy of the medical viewpoints can include a plurality of levels, for example, a low level (e.g., level 1) describing a beginner knowledge for the medical viewpoint, suitable for a user who does not understand the medical viewpoint, a middle level (e.g., level 2) describing an advanced knowledge for the medical viewpoint, suitable for a user who slightly understands the medical viewpoint, a high level (e.g., level 3) describing a complex knowledge for the medical viewpoint, suitable for a user who is proficient in the medical viewpoint, and the like, but the present disclosure is not limited thereto, and the description depth hierarchy of the medical viewpoints can include more or less levels.

[0041] The information density score can indicate the richness of the at least one medical content. The information density score can be a static attribute pre-calculated for the at least one medical content. Therefore, the information density score can represent the potential value of the at least one medical content. In one embodiment, the information density score can be determined based on the number of key messages associated with the at least one medical content included in the at least one medical content. In one embodiment, in response to the number of associated key messages included in the at least one medical content being greater than or equal to a first number (e.g., greater than or equal to 4, the present disclosure is not limited thereto), the information density score of the at least one medical content can be determined as a high information density score (e.g., 3 points, the present disclosure is not limited thereto). In response to the number of associated key messages included in the at least one medical content being less than the first number and greater than a second number (e.g., less than 4, greater than 1, for example, 2 or 3, the present disclosure is not limited thereto), the information density score of the at least one medical content can be determined as a medium information density score (e.g., 2 points, the present disclosure is not limited thereto). In response to the number of associated key messages included in the at least one medical content being less than or equal to the second number (e.g., less than or equal to 1, the present disclosure is not limited thereto), the information density score of the at least one medical content can be determined as a low information density score (e.g., 1 point, the present disclosure is not limited thereto). In step S1200, the historical data of the user can be obtained, the historical data including the user's understanding level hierarchy of the corresponding medical viewpoint.

[0042] In an embodiment, the historical data can be input by the user previously. For example, the historical data can be obtained based on information input by the user when performing a registration operation; the historical data can be obtained based on input by the user when performing a questionnaire survey, but the present disclosure is not limited thereto, and other ways of obtaining the historical data are also possible. The level of understanding of the medical viewpoint of the user can include multiple levels, for example, a low level (e.g., level 1-2) indicating that the user does not understand the medical viewpoint, a medium level (e.g., level 3) indicating that the user understands the medical viewpoint slightly, a high level (e.g., level 4-5) indicating that the user is proficient in the medical viewpoint, etc., but the present disclosure is not limited thereto, and the level of understanding of the medical viewpoint can include more or fewer levels.

[0043] In step S1300, a ranking score of the at least one medical content can be determined based on the level of understanding of the medical viewpoint of the user described by the at least one medical content and the depth of description of the medical viewpoint by the at least one medical content.

[0044] In an embodiment, the ranking score of the at least one medical content can be determined based on a matching between the level of understanding of the medical viewpoint of the user described by the at least one medical content and the depth of the medical viewpoint by the at least one medical content. For example, a difference between the level of understanding of the medical viewpoint of the user described by the at least one medical content and the depth of description of the medical viewpoint by the at least one medical content can be determined. In an embodiment, the depth of description of the medical viewpoint by the at least one medical content can be subtracted from the level of understanding of the medical viewpoint of the user described by the at least one medical content to obtain the difference. Based on the difference, the ranking score of the at least one medical content can be configured.

[0045] In step S1400, at least one attribute parameter of the at least one medical content can be determined, and a correlation interactive behavior score of the user for the at least one medical content can be determined based on the at least one attribute parameter and historical data, wherein the historical data further includes a correlation interactive behavior of the user for the at least one attribute parameter previously. For example, the correlation interactive behavior includes at least one of a direct behavior and an indirect behavior. The correlation interactive behavior score of the user for the at least one medical content can be determined based on the at least one attribute parameter of the at least one medical content and the correlation interactive behavior of the user for the at least one attribute parameter previously.

[0046] In step S1500, a first weight of the ranking score and a second weight of the correlation interactive behavior score can be determined based on the information density score. According to an embodiment of the present disclosure, the information density score can have a correlation relationship with the ranking score and the correlation interactive behavior score. The first weight applied to the ranking score and the second weight applied to the correlation interactive behavior score can be determined based on the information density score.

[0047] At step S1600, a ranking score of the at least one medical content can be obtained based on the hierarchical score, the first weight, the associated interactive behavior score, and the second weight. According to an embodiment of the present disclosure, the first weight can be applied to the hierarchical score, and the second weight can be applied to the associated interactive behavior score, thereby obtaining the ranking score of the at least one medical content.

[0048] At step S1700, the at least one medical content can be presented to the user in descending order of the ranking score of the at least one medical content.

[0049] In one embodiment, the medical contents included in the first set of medical contents can each obtain different ranking scores. The at least one medical content included in the first set of medical contents can be ranked in descending order based at least in part on the ranking scores, thereby being presented to the user. In this way, the user's understanding and learning of medical viewpoints can be promoted while ensuring that the recommended content is attractive, thereby achieving the goal of academic cultivation of the user.

[0050] Figure 2 is a schematic diagram of a flow of a method for recommending medical content according to an embodiment of the present disclosure. As shown in Figure 2 , the method 2000 can include steps S2110-S2600. Figure 2 in the same or similar steps as Figure 1 in the same or similar steps as

[0051] At step S2200, historical data of the user can be obtained, the historical data including a level of understanding of a corresponding medical viewpoint by the user.

[0052] In one embodiment, the level of understanding can be obtained through an interactive control provided in a medical content previously read by the user. For example, an interactive control for performing a questionnaire can be embedded in the medical content. The interactive control can be configured to present one or more interactive contents for a medical viewpoint to the user in response to the user selecting the interactive control. The one or more interactive contents can be in the form of a questionnaire. For example, one or more questions for a medical viewpoint can be presented through the interactive control, and one or more answers to the above questions can be presented through the interactive control for the user to select. For example, one or more questions for a medical viewpoint can be presented through the interactive control, and one or more answer areas for the above questions can be presented through the interactive control for the user to input answers. Based on the user's interaction with the one or more interactive contents, the level of understanding of the user for the medical viewpoint can be determined. For example, by analyzing the user's answers to one or more questions for a medical viewpoint, the level of understanding of the user for the corresponding medical viewpoint can be determined.

[0053] In one embodiment, in response to the user obtaining a plurality of understanding level ratings for a first medical perspective among the plurality of medical perspectives by selecting a plurality of interaction controls for the first medical perspective, an average of the plurality of understanding level ratings is determined as the understanding level rating of the user for the first medical perspective. For example, the user can answer a questionnaire for the same medical perspective multiple times, and can obtain a plurality of understanding level ratings for the same medical perspective. An average of the plurality of understanding level ratings can be determined as the final understanding level rating of the user for the medical perspective.

[0054] At step S2300, a rating score of the at least one medical content can be determined based on the understanding level rating of the user for the medical perspective described by the at least one medical content and the depth of description of the medical perspective by the at least one medical content.

[0055] In one embodiment, the rating score of the at least one medical content can be determined based on a matching between the understanding level of the user for the medical perspective described by the at least one medical content and the depth of the medical perspective by the at least one medical content. For example, a difference between the understanding level rating of the user for the medical perspective described by the at least one medical content and the depth of description of the medical perspective by the at least one medical content can be determined.

[0056] In one embodiment, the depth of description of the medical perspective by the at least one medical content can be subtracted from the understanding level rating of the user for the medical perspective described by the at least one medical content to obtain the difference. It can be determined whether the difference is within a predetermined difference range, a first rating score is configured for the at least one medical content in response to the difference being within the predetermined difference range, and a second rating score is configured for the at least one medical content in response to the difference not being within the predetermined difference range, wherein the second rating score is lower than the first rating score.

[0057] For example, the depth level of the medical opinion description can include five levels, such as level 1-level 5, representing the depth of the medical opinion description from shallow to deep. The level of the user's understanding of the medical opinion can include five levels, such as level 1-level 5, representing the level of the user's understanding of the medical opinion from shallow to deep. According to an embodiment of the present disclosure, if a medical content describes a medical opinion with a low level of the user's understanding (e.g., level 1 or level 2), and the difference between the depth of the medical opinion description in the medical content and the level of the user's understanding of the medical opinion is within a predetermined range (e.g., the difference is between 0 and 1), the medical content obtains a first level score of 3 points for the medical opinion. For example, if a medical content describes a medical opinion with a low level of the user's understanding (e.g., level 1 or level 2), and the difference between the depth of the medical opinion description in the medical content and the level of the user's understanding of the medical opinion is not within the predetermined range (e.g., the difference is not between 0 and 1), the medical content obtains a second level score of 0 points for the medical opinion. That is, the method according to the present disclosure can recommend to the user a medical content with a description depth level of the medical opinion slightly higher than the level of the user's understanding of the medical opinion, thereby facilitating the user's understanding of the medical opinion and improving the academic training goal.

[0058] In one embodiment, the first level score can be adjusted based on the level of the user's understanding of the medical opinion described by the at least one medical content. That is, the difference between the level of the user's understanding and the depth level of the medical content description can be within the predetermined range for different medical opinions, but the first level score corresponding to different medical opinions can be different due to the different levels of the user's understanding. For example, in the case where the difference can be within the predetermined range, the lower the level of the user's understanding of the medical opinion, the higher the level score obtained.

[0059] According to an embodiment of the present disclosure, if a medical content describes a medical opinion with a low level of the user's understanding (e.g., level 1 or level 2), and the difference between the depth of the medical opinion description in the medical content and the level of the user's understanding of the medical opinion is within a predetermined range (e.g., the difference is between 0 and 1), the medical content obtains a first level score of 3 points for the medical opinion. For example, if a medical content describes a medical opinion with a low level of the user's understanding (e.g., level 1 or level 2), and the difference between the depth of the medical opinion description in the medical content and the level of the user's understanding of the medical opinion is not within the predetermined range (e.g., the difference is not between 0 and 1), the medical content obtains a second level score of 0 points for the medical opinion. In this way, it can be attempted to influence the concept of the user with a low level of understanding first, thereby achieving the academic training goal.

[0060] According to embodiments of the present disclosure, if one medical content describes a medical opinion that the user's level of understanding is graded as a medium level (e.g., level 3), and the difference between the depth of the medical opinion described in the medical content and the user's level of understanding of the medical opinion is within a predetermined range (e.g., the difference is between 0 and 1), the medical content obtains a first level score of 1 for the medical opinion. For example, if one medical content describes a medical opinion that the user's level of understanding is graded as a medium level (e.g., level 3), and the difference between the depth of the medical opinion described in the medical content and the user's level of understanding of the medical opinion is not within a predetermined range (e.g., the difference is not between 0 and 1), the medical content obtains a second level score of 0 for the medical opinion. In this way, a safety exploration can be made to try to further influence the user's concept of a medium level of understanding.

[0061] According to embodiments of the present disclosure, if one medical content describes a medical opinion that the user's level of understanding is graded as a high level (e.g., level 4 or level 5), and the difference between the depth of the medical opinion described in the medical content and the user's level of understanding of the medical opinion is within a predetermined range (e.g., the difference is between 0 and 1), the medical content obtains a first level score of 0.2 for the medical opinion. For example, if one medical content describes a medical opinion that the user's level of understanding is graded as a high level (e.g., level 4 or level 5), and the difference between the depth of the medical opinion described in the medical content and the user's level of understanding of the medical opinion is not within a predetermined range (e.g., the difference is not between 0 and 1), the medical content obtains a second level score of 0 for the medical opinion. In this way, the user's cognition of a high level of understanding can be consolidated, and the user's concept of a high level of understanding can be maintained.

[0062] By the above method, when a medical content describes a medical opinion that the user is relatively unfamiliar with, and the depth of the medical opinion described in the medical content matches the user's level of understanding of the medical opinion, a higher level score can be configured for the medical opinion, thereby promoting the user's academic cultivation goal for the medical opinion. When a medical content describes a medical opinion that the user is relatively familiar with, and the depth of the medical opinion described in the medical content matches the user's level of understanding of the medical opinion, a lower level score can be configured for the medical opinion, thereby having an opportunity to recommend other medical contents including medical opinions that the user is relatively unfamiliar with to the user.

[0063] According to an embodiment of the present disclosure, if one medical content describes a medical viewpoint whose understanding level classification of the user is unknown (e.g., the understanding level classification of the user for the medical viewpoint cannot be obtained based on the historical data), the medical content obtains a first classification score of 0.5 for the medical viewpoint. In this way, a mild exploration can be made for the unknown field, so as to find the potential academic cultivation target of the user.

[0064] In one embodiment, a single medical content of the at least one medical content can describe a plurality of medical viewpoints. Therefore, a plurality of classification scores can be respectively determined for the single medical content with respect to the plurality of medical viewpoints. An average value of the plurality of classification scores can be determined as the classification score of the single medical content.

[0065] In step S2400, at least one attribute parameter of the at least one medical content can be determined, and a related interaction behavior score of the user for the at least one medical content is determined based on the at least one attribute parameter and the historical data, wherein the historical data further includes a related interaction behavior of the user for the at least one attribute parameter previously. The at least one attribute parameter includes one or more of a drug name and a drug brand. For example, the medical content can describe drugs of different names of different drug brands.

[0066] In one embodiment, the historical data further includes a related interaction behavior of the user for the at least one attribute parameter previously. For example, the related interaction behavior includes at least one of a direct behavior and an indirect behavior.

[0067] For example, in response to determining that the user has a direct behavior for the at least one attribute parameter through the historical data, the related interaction behavior score of the medical content corresponding to the at least one attribute parameter is directly adjusted. The direct behavior can include that the user has ever attended a conference about the drug name or the drug brand described in the medical content. In response to the user having a direct behavior for the at least one attribute parameter, the direct related interaction behavior score of the medical content corresponding to the at least one attribute parameter can be directly determined as 1. In this way, the medical content of interest of the user can be recommended, so as to promote the user to read.

[0068] For example, the interaction degree of the indirect behavior can be determined by determining, through historical data, that the user has indirect behavior for the at least one attribute parameter. The indirect behavior can include that the user has ever collected, shared and / or liked an article about a drug name or a drug brand described in the medical content, or has ever deeply read an article about a drug name or a drug brand described in the medical content. Deep reading can refer to a reading behavior with a reading duration longer than a predetermined time (for example, 10 seconds, or longer or shorter, the present disclosure is not limited thereto). In an embodiment, the interaction degree can include the number of articles about a drug name or a drug brand described in the medical content that the user has collected, shared and / or liked. In an embodiment, the interaction degree can include the number of articles about a drug name or a drug brand described in the medical content that the user has deeply read.

[0069] For example, the associated interaction behavior score of the medical content corresponding to the at least one attribute parameter can be adjusted based on the interaction degree of the indirect behavior. For example, if the number of articles about a drug name or a drug brand described in the medical content that the user has collected, shared and / or liked is 0, a first associated interaction behavior score of 0 points can be obtained for the medical content. For example, if the number of articles about a drug name or a drug brand described in the medical content that the user has collected, shared and / or liked is equal to 1, a first associated interaction behavior score of 0.5 points can be obtained for the medical content. For example, if the number of articles about a drug name or a drug brand described in the medical content that the user has collected, shared and / or liked is greater than or equal to 2, a first associated interaction behavior score of 1 point can be obtained for the medical content. For example, if the number of articles about a drug name or a drug brand described in the medical content that the user has deeply read is equal to 0, a second associated interaction behavior score of 0 points can be obtained for the medical content. For example, if the number of articles about a drug name or a drug brand described in the medical content that the user has deeply read is equal to 1, a second associated interaction behavior score of 0.5 points can be obtained for the medical content. For example, if the number of articles about a drug name or a drug brand described in the medical content that the user has deeply read is greater than or equal to 2, a second associated interaction behavior score of 1 point can be obtained for the medical content. The average of the first associated interaction behavior score and the second associated interaction behavior score can be determined as the indirect associated interaction behavior score of the medical content.

[0070] In an embodiment, a time interval from occurrence of the associated interaction behavior to current time can be determined based on historical data, and the associated interaction behavior score can be decayed based on the time interval. For example, if the associated interaction behavior occurs within 1 week from current time, the corresponding associated interaction behavior score is not decayed. For example, if the associated interaction behavior occurs within 2 weeks from current time, the corresponding associated interaction behavior score is multiplied by a decay coefficient of 70%. For example, if the associated interaction behavior occurs within 3 weeks from current time, the corresponding associated interaction behavior score is multiplied by a decay coefficient of 40%. For example, if the associated interaction behavior occurs more than 3 weeks from current time, the corresponding associated interaction behavior score is multiplied by a decay coefficient of 20%. In this way, the timeliness of the recommended content can be enhanced.

[0071] In an embodiment, the product of the direct associated interaction behavior score and the decay coefficient can be summed with the product of the indirect associated interaction behavior score and the decay coefficient to obtain the associated interaction behavior score of the medical content.

[0072] In step S2500, a first weight of the hierarchical score and a second weight of the associated interaction behavior score can be determined based on the information density score. The information density score can cause the at least one medical content to be ranked according to the amount of information thereof. According to an embodiment of the present disclosure, the size of the information density score can be positively correlated with the size of the first weight. Since the medical content with a large information density score has more associated key information, in order to make the medical content highly match the cognitive level and advancement target of the user, a higher first weight can be configured for the medical content. According to an embodiment of the present disclosure, the size of the information density score can be negatively correlated with the size of the second weight. Since the medical content with a low information density score has less associated key information, in order to make the medical content able to attract the user and maintain the interaction, a higher second weight can be configured for the medical content. Specifically, the relationship among the information density score, the first weight, and the second weight can refer to the following table:

[0073]

[0074] In step S2600, the product of the hierarchical score multiplied by the first weight, the product of the associated interaction behavior score multiplied by the second weight, and the information density score can be added to obtain a ranking score of the at least one medical content. Specifically, the ranking score can be calculated according to the following formula:

[0075] Ranking score = Information density score + (First weight * Hierarchical score) + (Second weight * Associated interaction behavior score)

[0076] That is, according to embodiments of the present disclosure, the information density score can not only provide a base score, and also be used to control the weight of the hierarchical score and the associated interaction behavior score. That is, for complex medical content, the academic cultivation goal can be more biased; for simple medical content, the user interest can be more biased. In addition, the number of key information can be very easily obtained, which can usually be directly calculated from the medical content tag system without any manual intervention.

[0077] The at least one medical content can be presented to the user in descending order of the ranking score of the at least one medical content. In response to the ranking scores of the at least one medical content being the same, the at least one medical content can be presented to the user in descending order of the reading amount of the at least one medical content. In this way, it can be ensured that the recommendation result not only meets the user's understanding level grading of medical viewpoints, but also can reflect the heat of medical content.

[0078] Specifically, assuming that the user is Dr. Zhang, the chief physician of the oncology department of a certain first-class hospital, who mainly focuses on the field of lung cancer. Dr. Zhang has the following historical data:

[0079] The understanding level grading of level 2 for the medical viewpoint “drug of brand A”;

[0080] The understanding level grading of level 3 for the medical viewpoint “drug of brand B”;

[0081] The understanding level grading of level 4 for the medical viewpoint “drug of brand C”;

[0082] Participated in an online meeting of brand A within 2 weeks (direct behavior);

[0083] Collected 1 article of brand A within 2 weeks (indirect behavior);

[0084] Deeply read (more than 10 seconds) 2 articles of brand B within 2 weeks (indirect behavior)”.

[0085] The first set of medical content can include the following medical content:

[0086] “Medical content 1: “Drug X III phase clinical OS data update: brand A” (the description depth grading is level 2, and the information density score is 3 points)

[0087] Medical content 2: “Drug Y safety real-world study: brand B” (the description depth grading is level 4, and the information density score is 3 points)

[0088] Medical content 3: “Drug Z monotherapy expert consensus: brand C” (the description depth grading is level 5, and the information density score is 2 points)

[0089] Medical content 4: "New evidence that drug W improves quality of life: brand D" (description depth rating is level 5 and information density score is 1 point)

[0090] Calculation process:

[0091]

[0092] Therefore, according to the descending order of the ranking score of at least one medical content, the at least one medical content can be presented to the user in the following order:

[0093] "1. Medical content 1 (9.525 points)

[0094] 2. Medical content 2 (5.35 points)

[0095] 3. Medical content 3 (2.2 points)

[0096] 4. Medical content 4 (1.25 points)".

[0097] The ranking result is highly consistent with the academic training goal. Medical content 1 and medical content 2 have high information density, so their rating scores are greatly weighted, occupying the top two. Although the rating score of medical content 4 is higher than that of medical content 3, because the information density score of medical content 4 is 1, the first weight of the rating score is reduced, so the total score is lower than that of medical content 3.

[0098] In one embodiment, the at least one medical content included in the first medical content set includes medical content that the user has not read. That is, the medical content in the first medical content set is not read by the user. According to the method of the embodiment of the present disclosure, a second medical content set including at least one medical content read by the user can be obtained. The second medical content set can be set after the first medical content set to present to the user. That is, the read content can be presented to the user after the unread content.

[0099] According to the embodiment of the present disclosure, the publication time of at least one medical content read by the user can be determined, and the at least one medical content read by the user can be presented to the user in the reverse order of the publication time. That is, after the read content is set after the unread content, the at least one medical content read by the user can be presented to the user in the order from early to late of the publication time of the read content (for example, the order of the content published earlier is earlier).

[0100] For example, it is assumed that Dr. Zhang has read:

[0101] "• Medical content 1: "Progress in immunotherapy for lung cancer" (published 3 days ago)

[0102] • Medical content 2: "New drug development for tumors" (published 5 days ago)".

[0103] The at least one medical content that has been read can be presented to the user in an order from early to late of a publication time of the read content (e.g., in an order in which content published earlier is placed earlier):

[0104] “Medical Content 2: New Drug Development for Tumors (5 days ago)

[0105] Medical Content 1: Progress in Immunotherapy for Lung Cancer (3 days ago)”.

[0106] According to the method of an embodiment of the disclosure, a third medical content set including at least one medical content expected to be presented to the user can be obtained. The third medical content set can be set before the first medical content set to be presented to the user. That is, the third medical content set can be pinned. For example, the third medical content set can include medical content such as “Guidelines for Lung Cancer Treatment in 2024”.

[0107] In this way, the following medical content can be presented to the user:

[0108] “=== Third Medical Content Set ===

[0109] 1. [Latest] Guidelines for Lung Cancer Treatment in 2024 (Operated Pin)

[0110] === First Medical Content Set ===

[0111] 2. “Drug X Phase III Clinical OS Data Update: Brand A”

[0112] 3. “Drug Y Safety Real-World Study: Brand B”

[0113] 4. “Drug Z Monotherapy Expert Consensus: Brand C”

[0114] 5. “Drug W New Evidence for Improving Quality of Life: Brand D”

[0115] === Second Medical Content Set ===

[0116] 6. Conference Replay New Drug Development for Tumors (5 days ago)

[0117] 7. Article Progress in Immunotherapy for Lung Cancer (3 days ago)”.

[0118] ​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​The method for recommending medical content disclosed herein can promote users' understanding and learning of medical perspectives while ensuring the recommended content is attractive, thereby achieving the goal of academic education for users. For example, the method not only considers users' immediate browsing preferences but also takes into account academic learning objectives, thus recommending medical content that describes insightful medical perspectives suitable for users' current cognitive level. Therefore, this method can maintain user engagement while effectively promoting their understanding and long-term accumulation of complex medical content, thereby achieving a leap from "recommending information" to "cultivating academic ability."

[0119] The method disclosed herein establishes different differentiated scoring mechanisms based on users' varying levels of understanding of medical viewpoints, thereby prioritizing the exposure of medical content that matches their level of understanding, breaking through traditional strategies and promoting the achievement of content reach strategies.

[0120] Figure 3 An apparatus for recommending medical content according to embodiments of the present disclosure is shown. Figure 3 As shown, the device 3000 for recommending medical content may include a medical content module 3100, a historical data module 3200, a grading and scoring module 3300, an associated interactive behavior scoring module 3400, a weighting module 3500, a sorting module 3600, and a content presentation module 3700.

[0121] The medical content module 3100 can be configured to obtain a first medical content set, the first medical content set including at least one medical content describing a medical viewpoint, the at least one medical content having a content information density score and a description depth rating of the medical viewpoint, wherein the information density score indicates the richness of the at least one medical content.

[0122] The historical data module 3200 can be configured to obtain the user's historical data, which includes a grading of the user's understanding of the relevant medical viewpoints.

[0123] The grading and scoring module 3300 can be configured to determine the grading score of at least one piece of medical content based on the user's level of understanding of the medical viewpoint described in at least one piece of medical content and the depth of description of the medical viewpoint by at least one piece of medical content.

[0124] The related interaction behavior scoring module 3400 can be configured to determine at least one attribute parameter of at least one medical content, and based on at least one attribute parameter and historical data, determine a user's related interaction behavior score for at least one medical content, wherein the historical data also includes the user's previous related interaction behavior for at least one attribute parameter.

[0125] The weight module 3500 can be configured to determine the first weight of the graded score based on the information density score and the second weight of the associated interactive behavior score.

[0126] The ranking module 3600 can be configured to obtain a ranking score for at least one medical content based on the graded score, the first weight, the associated interactive behavior score, and the second weight.

[0127] The content presentation module 3700 can be configured to present at least one piece of medical content to the user in descending order of the sorting scores of at least one piece of medical content.

[0128] According to another aspect of this disclosure, a device for recommending medical content is also provided. Figure 4 A schematic diagram of a device 400 for recommending medical content according to an embodiment of the present disclosure is shown.

[0129] like Figure 4 As shown, the device 400 for recommending medical content may include one or more processors 410 and one or more memories 420. The memories 420 store computer-readable code that, when executed by the one or more processors 410, can perform the method for recommending medical content as described above.

[0130] The processor in the embodiments of this disclosure can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), an off-the-shelf programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor, and can be based on an x86 architecture or an ARM architecture.

[0131] In general, the various exemplary embodiments of this disclosure can be implemented in hardware or dedicated circuitry, software, firmware, logic, or any combination thereof. Some aspects can be implemented in hardware, while others can be implemented in firmware or software that can be executed by a controller, microprocessor, or other computing device. When aspects of embodiments of this disclosure are illustrated or described as block diagrams, flowcharts, or using some other graphical representation, it will be understood that the blocks, apparatuses, systems, techniques, or methods described herein can be implemented as non-limiting examples in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0132] For example, the method or apparatus according to embodiments of this disclosure can also be used by means of Figure 5The illustrated architecture of the computing device 500 is implemented. As Figure 5 As illustrated, the computing device 500 can include a bus 510, one or more CPUs 520, a read only memory (ROM) 530, a random access memory (RAM) 540, a communication port connected to a network 550, an input / output component 560, a hard disk 570, etc. The storage device in the computing device 500, such as the ROM 570 or the hard disk 570, can store various data or files used in processing and / or communication of the method for recommending medical content provided by the present disclosure and program instructions executed by the CPU. The computing device 500 can also include a user interface 580. Of course, Figure 5 The illustrated architecture is only exemplary, and when implementing different devices, some of the components in the computing device can be omitted Figure 5 one or more components in the illustrated computing device.

[0133] According to still another aspect of the present disclosure, a computer readable storage medium is also provided. The computer readable storage medium has stored thereon computer readable instructions which, when executed by a processor, can perform the method for recommending medical content according to the embodiments of the present disclosure described with reference to the above figures. The computer readable storage medium in the embodiments of the present disclosure can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. The non-volatile memory can be a read only memory (ROM), a programmable read only memory (PROM), an erasable programmable read only memory (EPROM), an electrically erasable programmable read only memory (EEPROM), or a flash memory. The volatile memory can be a random access memory (RAM) used as an external cache. By way of example, and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct

[0134] Embodiments of the present disclosure further provide a computer program product or computer program including computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the method for recommending medical content according to the embodiments of the present disclosure.

[0135] It should be noted that the flowchart and block diagrams in the drawings are for illustrating the possible architectural, functional, and operational aspects of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams can represent a module, a segment, or a portion of code, which comprises one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently or in the reverse order, depending on the functionality involved. It will also be noted that each block of the block diagrams and / or flowchart illustrations, and combinations thereof, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or combinations of hardware and software.

[0136] In general, the various example embodiments of the present disclosure can be implemented in hardware or special-purpose circuits, software, firmware, logic, or any combination thereof. Some aspects can be implemented in hardware, while other aspects can be implemented in firmware or software which can be executed by a controller, microprocessor or other computing device, Although the various aspects of embodiments of the present disclosure can be illustrated and described as block diagrams, flow charts, or using some other pictorial representation, it is well understood that these blocks, apparatus, systems, techniques or methods described herein can be implemented in, as non-limiting examples, hardware, software, firmware, special purpose circuits or logic, general purpose hardware or controler or other computing devices, or some combination thereof.

[0137] The example embodiments of the present disclosure described in detail above are merely illustrative, and not restrictive. It should be understood by those skilled in the art that various modifications and combinations can be made to these embodiments or features thereof without departing from the principles and spirit of the present disclosure, and such modifications should fall within the scope of the present disclosure.

[0138] Although the present disclosure has been described with an example embodiment, various changes and modifications can be suggested to one skilled in the art. It is intended that the present disclosure encompass such changes and modifications as fall within the scope of the appended claims.

[0139] No description in the present application should be interpreted as implying any particular element, step or function is an essential element of the claims. The scope of the patent application subject matter is defined only by the claims.

Claims

1. A method for recommending medical content, comprising: obtaining a first set of medical content, the first set of medical content comprising at least one medical content describing a medical viewpoint, the at least one medical content having a content information density score and a description depth ranking of the medical viewpoint, wherein the information density score indicates a richness of the at least one medical content; obtaining historical data of a user, the historical data comprising a level of understanding ranking of the user of the medical viewpoint described by the at least one medical content; determining a ranking score of the at least one medical content based on the level of understanding ranking of the user of the medical viewpoint described by the at least one medical content and the description depth ranking of the medical viewpoint by the at least one medical content; determining at least one attribute parameter of the at least one medical content, determining a related interaction behavior score of the user for the at least one medical content based on the at least one attribute parameter and the historical data, wherein the historical data further comprises a related interaction behavior of the user previously for the at least one attribute parameter; determining a first weight of the ranking score and a second weight of the related interaction behavior score based on the information density score; obtaining a ranking score of the at least one medical content based on the ranking score, the first weight, the related interaction behavior score, and the second weight; and presenting the at least one medical content to the user in a descending order of the ranking score of the at least one medical content.

2. The method of claim 1, wherein, The determining the ranking score of the at least one medical content comprises: determining a difference between the level of understanding ranking of the user of the medical viewpoint described by the at least one medical content and the description depth ranking of the medical viewpoint by the at least one medical content; and configuring the ranking score of the at least one medical content based on the difference.

3. The method of claim 2, wherein, The determining the ranking score of the at least one medical content comprises: determining whether the difference is within a predetermined difference range; in response to the difference being within the predetermined difference range, configuring a first ranking score for the at least one medical content; and in response to the difference not being within the predetermined difference range, configuring a second ranking score for the at least one medical content, wherein the second ranking score is lower than the first ranking score.

4. The method of claim 3, wherein, The configuring the first ranking score for the at least one medical content comprises: adjusting the first ranking score based on the level of understanding ranking of the user of the medical viewpoint described by the at least one medical content. 5.The method of claim 1, further comprising: in response to one of the at least one medical content describing a plurality of medical viewpoints; determining a plurality of ranking scores for the one medical content respectively for the plurality of medical viewpoints; and determining an average of the plurality of ranking scores as a ranking score of the one medical content. The level of understanding ranking is obtained through an interaction control provided in a medical content previously read by the user, and 6. The method of claim 1, wherein, wherein the interaction control is configured to: ​ in response to the user selecting the interaction control, presenting one or more interaction contents for a medical viewpoint to the user, determining a level of understanding of the user for the medical viewpoint based on the user's interaction with the one or more interaction contents.

7. The method of claim 6, wherein, The interaction control is configured to: in response to the user obtaining multiple levels of understanding for a first medical viewpoint among multiple medical viewpoints by selecting multiple interaction controls for the first medical viewpoint, determining an average of the multiple levels of understanding as the level of understanding of the user for the first medical viewpoint.

8. The method of claim 1, wherein, The presenting the at least one medical content to the user in the descending order of the ranking scores of the at least one medical content comprises: in response to the ranking scores of the at least one medical content being the same, presenting the at least one medical content to the user in a descending order of reading volumes of the at least one medical content.

9. The method of claim 1, wherein, The at least one attribute parameter comprises one or more of a drug name and a drug brand.

10. The method of claim 9, wherein, The obtaining the ranking scores of the at least one medical content comprises: multiplying the product of the ranking score multiplied by the first weight, the product of the correlation interaction behavior score multiplied by the second weight, and the information density score to obtain the ranking scores of the at least one medical content.

11. The method of claim 9, wherein, The correlation interaction behavior comprises direct behavior, and the method further comprises: in response to determining that the user has the direct behavior for the at least one attribute parameter through the historical data, directly adjusting the correlation interaction behavior score of the medical content corresponding to the at least one attribute parameter, wherein the direct behavior comprises that the user has ever attended a meeting about the at least one attribute parameter.

12. The method of claim 9, wherein, The correlation interaction behavior comprises indirect behavior, and the method further comprises: in response to determining that the user has the indirect behavior for the at least one attribute parameter through the historical data, determining an interaction degree of the indirect behavior, based on the interaction degree of the indirect behavior, adjusting the correlation interaction behavior score of the medical content corresponding to the at least one attribute parameter, wherein the indirect behavior comprises: the user has ever collected, shared, and / or liked an article about the at least one attribute parameter, and / or the user has ever read an article about the at least one attribute parameter for more than a predetermined time.

13. The method of claim 9, wherein, The determining the correlation interaction behavior score of the user for the at least one medical content comprises: based on the historical data, determining a time interval from the occurrence time of the correlation interaction behavior to the current time, based on the time interval, attenuating the correlation interaction behavior score.

14. The method of claim 1, wherein, The at least one medical content included in the first medical content set comprises medical content unread by the user, and the method further comprises: obtaining a second medical content set comprising at least one medical content read by the user, setting the second medical content set after the first medical content set to present to the user.

15. The method of claim 14, wherein, The method further comprises: determining a publication time of the at least one medical content read by the user, presenting the at least one medical content to the user in a descending order of the ranking score of the at least one medical content.

16. The method of claim 1, wherein, a size of the information density score is positively correlated with a size of the first weight.

17. The method of claim 1, wherein, a size of the information density score is negatively correlated with a size of the second weight.

18. An apparatus for recommending medical content, comprising: a medical content module configured to obtain a first set of medical content, the first set of medical content comprising at least one medical content describing a medical viewpoint, the at least one medical content having an information density score and a depth level of description of the medical viewpoint, wherein the information density score indicates a richness level of the at least one medical content; a history data module configured to obtain history data of a user, the history data comprising a level of understanding of the user of a corresponding medical viewpoint; a ranking score module configured to determine a ranking score of the at least one medical content based on the level of understanding of the user of the medical viewpoint described by the at least one medical content and the depth level of description of the medical viewpoint by the at least one medical content; a related interaction behavior score module configured to determine at least one attribute parameter of the at least one medical content, determine a related interaction behavior score of the user for the at least one medical content based on the at least one attribute parameter and the history data, wherein the history data further comprises a related interaction behavior of the user for the at least one attribute parameter previously; a weight module configured to determine a first weight of the ranking score and a second weight of the related interaction behavior score based on the information density score; a ranking module configured to obtain a ranking score of the at least one medical content based on the ranking score, the first weight, the related interaction behavior score, and the second weight; and a content presentation module configured to present the at least one medical content to the user in a descending order of the ranking score of the at least one medical content.

19. An apparatus for recommending medical content, comprising: one or more processors; and one or more memories having computer-executable programs stored therein, which, when executed by the processors, perform the method of any one of claims 1-17.

20. A computer program product comprising computer programs or instructions, wherein, The computer program or instructions, when executed by a processor, implement the method of any one of claims 1-17.

21. A computer-readable storage medium having stored thereon computer-executable instructions for implementing the method of any one of claims 1-17 when executed by a processor.

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