Information publishing service system and method

Abstract

The invention discloses an information publishing service system and method, and relates to the technical field of information publication, and the method comprises the steps: obtaining historical information browsing records of a user, dividing the historical information browsing records according to different information obtaining modes, and obtaining a historical information browsing record set in any mode; obtaining reading parameters after the user browses the information, obtaining a reading index of the information through calculation, and judging that the information is feature information; determining the type of the feature information, calculating the weight of any entry of any information type, and dividing the levels of the entries; the score of the user on the information and the number of entries of any grade form a data set, and function fitting is performed on the data set to obtain a score prediction model of any information type; and sorting the real-time to-be-published information according to the score prediction model to realize accurate pushing of the information.

Description

Technical Field

[0001] The present invention relates to the technical field of information publishing, and in particular to an information publishing service system and method. Background Art

[0002] Nowadays, we are all in the era of informatization and data. With the continuous deepening of social informatization, the increase in user types and the convenience of obtaining information services have greatly stimulated the potential of user information needs and the demand for diversified information services;

[0003] Information publishing platforms have been integrated into our lives. On information publishing platforms, the latest news will be released to users. However, different users may have different interests, so there will be certain differences in demand. If the needs of users are not taken into consideration when information is released, users will not have a good reading experience when reading information. Therefore, it is particularly important to develop a publishing service system that can combine the user's personal preferences, automatically match the information to be released with the user information, and achieve accurate push. Summary of the invention

[0004] The purpose of the present invention is to provide an information publishing service system and method to solve the problems raised in the prior art.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: an information publishing service method, the method comprising:

[0006] Step S100: obtaining the user's historical information browsing records through the information publishing platform system, collecting the information acquisition methods of the historical information browsing records, dividing the historical information browsing records according to different information acquisition methods, and obtaining a set of historical information browsing records in any method;

[0007] Step S200: in any manner of historical information browsing record set, obtaining the reading parameters of the user after browsing the information, and obtaining the reading index of the information by calculation, and determining whether the information is characteristic information;

[0008] Step S300: confirm the type of the characteristic information according to the information type of the information publishing platform system, perform text processing on the characteristic information of any type, obtain the term composition and term proportion of the characteristic information, and calculate the weight of any term;

[0009] Step S400: Calculate the weight of any term of any information type according to the weight of any term in step S300, and classify the terms according to the weight;

[0010] Step S500: obtaining a user's rating record for a certain historical browsing information, in which the user's rating of the information and the number of entries of any level are combined into a data group, and a function fitting is performed on the data group to obtain a rating prediction model for any information type;

[0011] Step S600: sorting the real-time information to be published according to the rating prediction model, and pushing the information to the user in the sorted order.

[0012] Further, in step S100, in the information publishing platform system, the user's historical information browsing records are obtained, and the historical information browsing records of the same method of obtaining information are summarized to obtain a set of historical information browsing records in any method A={a1, a2, a3, ..., an}, wherein a1, a2, a3, ..., an respectively represent the 1st, 2nd, 3rd, ..., nth historical information browsing records in any method; wherein an={an1, an2, an3, ..., anm}, wherein an1, an2, an3, ..., anm respectively represent the values ​​of the 1st, 2nd, 3rd, ..., mth reading parameters in the nth historical information browsing record in any method;

[0013] When users browse information, they obtain information in different ways, such as active search by users and automatic recommendation by the system. Different ways may represent different levels of interest of users, so it is necessary to conduct classified discussions to improve the accuracy of data for subsequent calculations;

[0014] The reading parameters in the information browsing record include reading time, number of views, reading depth, etc. The reading depth is the depth of the user's scrolling page, which is used to determine whether the user has browsed the entire article or only read the beginning part.

[0015] Further, step S200 includes:

[0016] Step S201: In any set of historical information browsing records, the reading index of the nth historical information browsing record is calculated according to the formula:

[0017]

[0018] Among them, X bn It is represented by the reading index of the nth historical information browsing record in the bth way, X bnc It is represented by the value of the reading parameter of item c in the browsing record of the nth historical information in the bth mode, d bc It is expressed as the weight of the cth reading parameter of the bth method;

[0019] Step S202: When X bn ≥X bWhen X b The reading index threshold of the b-th mode is expressed as the reading index threshold of the b-th mode, then the n-th historical information browsing record of the b-th mode is determined to be the characteristic information of the b-th mode, and the characteristic information of any mode is aggregated to obtain a characteristic information set of any mode and a reading index set of the characteristic information;

[0020] Because different reading parameters have different importance, it is necessary to calculate the reading index of the information browsing record, determine whether the information is characteristic information, and analyze the characteristic information to ensure the accuracy of subsequent analysis.

[0021] Furthermore, step S300 includes:

[0022] Step S301: According to the information type of the information publishing platform, in the characteristic information set of any mode, the information type of the characteristic information is collected, and a certain information type collected is summarized to obtain the characteristic information set of any information type;

[0023] Step S302: input the feature information in the feature information set of any information type into the NLP model for text analysis, extract the terms of the feature information, summarize the number of times any term appears in the feature information set, and calculate the frequency of a term in any feature information according to the following formula:

[0024]

[0025] Among them, Y tm It is represented by the frequency of the mth term in the tth feature information, y tm It is represented by the number of occurrences of the mth term in the tth feature information, y t It is represented by the number of occurrences of all terms in the t-th feature information;

[0026] Step S303: In the reading index set of feature information, the weight of a certain term in any feature information is calculated according to the following formula:

[0027] Z tm =Y tm ×X t ;

[0028] Among them, Z tm It is represented as the weight of the mth term in the tth feature information, X t Expressed as the reading index of the t-th feature information, the entry weight set of any feature information is obtained, and the feature information of any information type is summarized to obtain the weight set of a certain entry in any information type;

[0029] Information types include livelihood, sports, education, etc.;

[0030] Because the frequency of an entry can only represent part of the data, if only the frequency of the entry is analyzed, the result will be monotonous and the accuracy will decrease. Therefore, it is necessary to analyze it together with the reading index so that the result will be more accurate.

[0031] Furthermore, step S400 includes:

[0032] Step S401: dividing the weight set of a certain term of the same information type according to any method of obtaining information, and obtaining the weight set of a certain term of any method in the same information type;

[0033] Step S402: Calculate the weight of a term in any information type according to the following formula:

[0034]

[0035] Among them, Z pm It is expressed as the weight of the mth term in the pth information type, Z rpmi It is expressed as the i-th weight of the m-th term in the p-th information type in the r-th mode, j rpm It is expressed as the number of weight sets of the mth term in the pth information type in the rth way, α r It is represented as the weight of the rth way, L is represented as the number of ways to obtain information, and the weight set of terms of any information type is obtained;

[0036] Step S403: Divide the term weights into several levels, divide the term weight set of any information type according to the level, confirm the term level of any information type and summarize it, and obtain a term set of a certain level in any information type;

[0037] Because information obtained in different ways represents different levels of user interest, the weights of different methods must be calculated together to obtain a more accurate weight.

[0038] Furthermore, step S500 includes:

[0039] Step S501: obtaining a user's rating record for a certain historical browsing information, and collecting information types and entries of the browsing information, determining an entry set of any level according to the collected information type, comparing the collected entries with the entry set of any level, and obtaining the number of entries of any level;

[0040] Step S502: The rating of the information and the number of entries of any level are regarded as a data group, the rating records of the historical browsing information are summarized, and a function fitting is performed on several data groups according to the following formula:

[0041]

[0042] Among them, H represents the score of information, S e Expressed as the number of terms at level e, β e It is represented as the weight of the e-th level, f is the number of levels, S is the total number of terms that meet any level in any information, S1 is the total number of terms in any information, and γ is the weight of the information score, and the prediction score model for any information type is obtained.

[0043] Furthermore, step S600 includes:

[0044] Step S601: obtaining the information type and terms of the real-time information to be published, determining the number of terms that meet the arbitrary level of a certain user, inputting the number of terms at the arbitrary level into a prediction and scoring model of the same information type, and obtaining the user's prediction score;

[0045] Step S602: When the predicted score of the user is greater than or equal to the score threshold, the information to be released is set as information of interest, a set of information of interest is obtained, the set is sorted from high to low according to the predicted score of the information of interest, and the sorted set of information of interest is pushed to the user.

[0046] In order to better implement the above method, an information publishing service system is also proposed, which includes a historical information browsing record module, a feature information module, an entry weight module, an entry level module, a rating prediction model module, and a real-time sorting module;

[0047] Historical information browsing record module: obtains the user's historical information browsing record through the information publishing platform system, collects the information acquisition method of the historical information browsing record, divides the historical information browsing record according to different information acquisition methods, and obtains a collection of historical information browsing records in any method;

[0048] Characteristic information module: in any historical information browsing record set, obtains the reading parameters of the user after browsing the information, and obtains the reading index of the information by calculation to determine whether the information is characteristic information;

[0049] Term weight module: according to the information type of the information publishing platform system, confirm the type of the characteristic information, perform text processing on any type of characteristic information, obtain the term composition and term proportion of the characteristic information, and calculate the weight of any term;

[0050] Term ranking module: Calculate the weight of any term of any information type according to the weight of any term in step S300, and classify the terms according to the weight;

[0051] Rating prediction model module: obtain the user's rating record for a certain historical browsing information, in which the user's rating of the information and the number of entries of any level are combined into a data group, and a function is fitted on the data group to obtain a rating prediction model for any information type;

[0052] Real-time sorting module: sorts the real-time information to be published according to the rating prediction model, and pushes the information to the user in the sorted order.

[0053] Further, the term weight module includes a feature information classification unit and a term weight calculation unit;

[0054] Feature information classification unit: collects information types of feature information according to the information type of the information publishing platform, summarizes a certain information type collected, and obtains a feature information set of any information type;

[0055] Calculating term weight unit: extracting terms from feature information through a natural language processing model, calculating the frequency of terms appearing in the feature information, and calculating the weight of the terms based on the frequency and the reading index of the feature information.

[0056] Further, the term ranking module includes a term weight calculation unit and a ranking determination unit;

[0057] Term weight calculation unit: obtains a weight set of a term in any manner in the same information type, calculates the weight set, and obtains the weight of a term in any information type;

[0058] Determine the level unit: divide the term weights into several levels, divide the term weight set of any information type according to the levels, confirm the term level of any information type and summarize it, and obtain a term set of a certain level in any information type.

[0059] Compared with the prior art, the present invention has the following beneficial effects: obtaining the user's historical information browsing records through the information publishing platform system, collecting the information acquisition methods of the historical information browsing records, dividing the historical information browsing records according to different information acquisition methods, obtaining a collection of historical information browsing records in any method, obtaining the reading parameters of the user after browsing the information, and obtaining the reading index of the information by calculation to determine whether the information is characteristic information. This method determines characteristic information, firstly avoiding human subjective judgment, and secondly combining the reading parameters with the term frequency to improve the accuracy of the judgment;

[0060] According to the information type of the information publishing platform system, the type of the characteristic information is confirmed, and any type of characteristic information is subjected to text processing to obtain the word composition and word proportion of the characteristic information, and the weight of any word is calculated. According to the weight of any word in step S300, the weight of any word of any information type is calculated, and the level of the word is divided according to the weight. By dividing the word level by this method, the accuracy of the division is further improved;

[0061] Obtain the user's rating record for a certain historical browsing information. In the rating record, the user's rating of the information and the number of entries of any level are combined into a data group, and a function is fitted to the data group to obtain a rating prediction model for any information type; according to the rating prediction model, the real-time information to be released is sorted. Through this method, real-time information can be accurately pushed. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 The present invention is a flowchart of an information publishing service method. DETAILED DESCRIPTION

[0063] Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making any creative work shall fall within the scope of protection of the present invention.

[0064] See also Figure 1 The present invention provides a technical solution: an information publishing service method, the method comprising:

[0065] Step S100: obtaining the user's historical information browsing records through the information publishing platform system, collecting the information acquisition methods of the historical information browsing records, dividing the historical information browsing records according to different information acquisition methods, and obtaining a set of historical information browsing records in any method;

[0066] Wherein, in step S100, in the information publishing platform system, the user's historical information browsing records are obtained, and the historical information browsing records of the same method of obtaining information are summarized to obtain a set of historical information browsing records in any method A={a1, a2, a3, ..., an}, wherein a1, a2, a3, ..., an respectively represent the 1st, 2nd, 3rd, ..., nth historical information browsing records in any method; wherein an={an1, an2, an3, ..., anm}, wherein an1, an2, an3, ..., anm respectively represent the values ​​of the 1st, 2nd, 3rd, ..., mth reading parameters in the nth historical information browsing record in any method;

[0067] For example, the historical information browsing record set of the first method is {the first browsing record, the second browsing record, the third browsing record};

[0068] The reading parameter set of the first browsing record is {the first reading parameter, the second reading parameter, the third reading parameter}.

[0069] Step S200: in any manner of historical information browsing record set, obtaining the reading parameters of the user after browsing the information, and obtaining the reading index of the information by calculation, and determining whether the information is characteristic information;

[0070] Wherein, step S200 includes:

[0071] Step S201: In any set of historical information browsing records, the reading index of the nth historical information browsing record is calculated according to the formula:

[0072]

[0073] Among them, X bn It is represented by the reading index of the nth historical information browsing record in the bth way, X bnc It is represented by the value of the reading parameter of item c in the browsing record of the nth historical information in the bth mode, d bc It is expressed as the weight of the cth reading parameter of the bth method;

[0074] Step S202: When X bn ≥X b When X b Expressed as the reading index threshold of the b-th mode, the nth historical information browsing record of the b-th mode is determined to be the characteristic information of the b-th mode, and the characteristic information of any mode is aggregated to obtain a characteristic information set of any mode and a reading index set of the characteristic information.

[0075] Step S300: confirm the type of the characteristic information according to the information type of the information publishing platform system, perform text processing on the characteristic information of any type, obtain the term composition and term proportion of the characteristic information, and calculate the weight of any term;

[0076] Wherein, step S300 includes:

[0077] Step S301: According to the information type of the information publishing platform, in the characteristic information set of any mode, the information type of the characteristic information is collected, and a certain information type collected is summarized to obtain the characteristic information set of any information type;

[0078] Step S302: input the feature information in the feature information set of any information type into the NLP model for text analysis, extract the terms of the feature information, summarize the number of times any term appears in the feature information set, and calculate the frequency of a term in any feature information according to the following formula:

[0079]

[0080] Among them, Y tm It is represented by the frequency of the mth term in the tth feature information, y tm It is represented by the number of occurrences of the mth term in the tth feature information, y t It is represented by the number of occurrences of all terms in the t-th feature information;

[0081] Step S303: In the reading index set of feature information, the weight of a certain term in any feature information is calculated according to the following formula:

[0082] Z tm =Y tm ×X t ;

[0083] Among them, Z tm It is represented as the weight of the mth term in the tth feature information, X t Expressed as the reading index of the t-th feature information, the entry weight set of any feature information is obtained, and the feature information of any information type is summarized to obtain the weight set of a certain entry in any information type;

[0084] For example, in the first method, the frequency of the first term of the first feature information of the first information type is 0.3, the frequency of the second term is 0.25, and the frequency of the third term is 0.22;

[0085] The reading index of the first feature information is 3.8. Then, in the first method, the weight of the first term of the first feature information of the first information type is 1.14, the weight of the second term is 0.98, and the weight of the third term is 0.836.

[0086] Step S400: Calculate the weight of any term of any information type according to the weight of any term in step S300, and classify the terms according to the weight;

[0087] Wherein, step S400 includes:

[0088] Step S401: dividing the weight set of a certain term of the same information type according to any method of obtaining information, and obtaining the weight set of a certain term of any method in the same information type;

[0089] Step S402: Calculate the weight of a term in any information type according to the following formula:

[0090]

[0091] Among them, Z pm It is expressed as the weight of the mth term in the pth information type, Z rpmi It is expressed as the i-th weight of the m-th term in the p-th information type in the r-th mode, j rpm It is expressed as the number of weight sets of the mth term in the pth information type in the rth way, α r It is represented as the weight of the rth way, L is represented as the number of ways to obtain information, and the weight set of terms of any information type is obtained;

[0092] Step S403: Divide the term weights into several levels, divide the term weight set of any information type according to the levels, confirm the term level of any information type and summarize it to obtain a term set of a certain level in any information type.

[0093] Step S500: obtaining a user's rating record for a certain historical browsing information, in which the user's rating of the information and the number of entries of any level are combined into a data group, and a function fitting is performed on the data group to obtain a rating prediction model for any information type;

[0094] Wherein, step S500 includes:

[0095] Step S501: obtaining a user's rating record for a certain historical browsing information, and collecting information types and entries of the browsing information, determining an entry set of any level according to the collected information type, comparing the collected entries with the entry set of any level, and obtaining the number of entries of any level;

[0096] Step S502: The rating of the information and the number of entries of any level are regarded as a data group, the rating records of the historical browsing information are summarized, and a function fitting is performed on several data groups according to the following formula:

[0097]

[0098] Among them, H represents the score of information, S e Expressed as the number of terms at level e, β e It is represented as the weight of the e-th level, f is the number of levels, S is the total number of terms that meet any level in any information, S1 is the total number of terms in any information, and γ is the weight of the information score, and the prediction score model for any information type is obtained.

[0099] Step S600: sorting the real-time information to be released according to the rating prediction model, and pushing the information to the user in the sorted order;

[0100] Wherein, step S600 includes:

[0101] Step S601: obtaining the information type and terms of the real-time information to be published, determining the number of terms that meet the arbitrary level of a certain user, inputting the number of terms at the arbitrary level into a prediction and scoring model of the same information type, and obtaining the user's prediction score;

[0102] Step S602: When the predicted score of the user is greater than or equal to the score threshold, the information to be released is set as information of interest, a set of information of interest is obtained, the set is sorted from high to low according to the predicted score of the information of interest, and the sorted set of information of interest is pushed to the user.

[0103] In order to better implement the above method, an information publishing service system is also proposed, which includes a historical information browsing record module, a feature information module, an entry weight module, an entry level module, a rating prediction model module, and a real-time sorting module;

[0104] Historical information browsing record module: obtains the user's historical information browsing record through the information publishing platform system, collects the information acquisition method of the historical information browsing record, divides the historical information browsing record according to different information acquisition methods, and obtains a collection of historical information browsing records in any method;

[0105] Characteristic information module: in any historical information browsing record set, obtains the reading parameters of the user after browsing the information, and obtains the reading index of the information by calculation to determine whether the information is characteristic information;

[0106] Term weight module: according to the information type of the information publishing platform system, confirm the type of the characteristic information, perform text processing on any type of characteristic information, obtain the term composition and term proportion of the characteristic information, and calculate the weight of any term;

[0107] Among them, the entry weight module includes a feature information classification unit and an entry weight calculation unit;

[0108] Feature information classification unit: collects information types of feature information according to the information type of the information publishing platform, summarizes a certain information type collected, and obtains a feature information set of any information type;

[0109] Calculating term weight unit: extracting terms from feature information through a natural language processing model, calculating the frequency of terms appearing in the feature information, and calculating the weight of the terms based on the frequency and the reading index of the feature information.

[0110] Term ranking module: Calculate the weight of any term of any information type according to the weight of any term in step S300, and classify the terms according to the weight;

[0111] Among them, the entry level module includes an entry weight calculation unit and a level determination unit;

[0112] Term weight calculation unit: obtains a weight set of a term in any manner in the same information type, calculates the weight set, and obtains the weight of a term in any information type;

[0113] Determine the level unit: divide the term weights into several levels, divide the term weight set of any information type according to the levels, confirm the term level of any information type and summarize it, and obtain a term set of a certain level in any information type.

[0114] Rating prediction model module: obtain the user's rating record for a certain historical browsing information, in which the user's rating of the information and the number of entries of any level are combined into a data group, and a function is fitted on the data group to obtain a rating prediction model for any information type;

[0115] Real-time sorting module: sorts the real-time information to be published according to the rating prediction model, and pushes the information to the user in the sorted order.

[0116] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An information publishing service method, characterized in that: Step S100: obtaining the user's historical information browsing records through the information publishing platform system, collecting the information acquisition methods of the historical information browsing records, dividing the historical information browsing records according to different information acquisition methods, and obtaining a set of historical information browsing records in any method; Step S200: in a set of historical information browsing records in any manner, obtaining reading parameters of the user after browsing the information, and obtaining the reading index of the information by calculation to determine whether the information is characteristic information; Step S300: confirm the type of the characteristic information according to the information type of the information publishing platform system, perform text processing on the characteristic information of any type, obtain the term composition and term proportion of the characteristic information, and calculate the weight of any term; Step S400: Calculate the weight of any term of any information type according to the weight of any term in step S300, and classify the terms according to the weight; Step S500: obtaining a user's rating record for a certain historical browsing information, in which the user's rating of the information and the number of entries of any level are combined into a data group, and a function fitting is performed on the data group to obtain a rating prediction model for any information type; Step S600: sorting the real-time information to be published according to the rating prediction model, and pushing the information to the user in the sorted order.

2. The information publishing service method according to claim 1, characterized in that: In step S100, in the information publishing platform system, the user's historical information browsing records are obtained, and the historical information browsing records of the same method of obtaining information are summarized to obtain a historical information browsing record set A={a1, a2, a3, ..., an} of any method, wherein a1, a2, a3, ..., an respectively represent the 1st, 2nd, 3rd, ..., nth historical information browsing records of any method; wherein an={an1, an2, an3, ..., anm}, wherein an1, an2, an3, ..., anm respectively represent the values ​​of the 1st, 2nd, 3rd, ..., mth reading parameters in the nth historical information browsing record of any method.

3. The information publishing service method according to claim 2, characterized in that: The step S200 includes the following steps: Step S201: In any set of historical information browsing records, the reading index of the nth historical information browsing record is calculated according to the formula: Among them, X bn It is represented by the reading index of the nth historical information browsing record in the bth way, X bnc It is represented by the value of the reading parameter of item c in the browsing record of the nth historical information in the bth mode, d bc It is expressed as the weight of the cth reading parameter of the bth method; Step S202: When X bn ≥X b When X b The reading index threshold of the b-th mode is expressed as the reading index threshold of the b-th mode, and the nth historical information of the b-th mode is determined to be the characteristic information of the b-th mode. The characteristic information of any mode is aggregated to obtain a characteristic information set of any mode and a reading index set of the characteristic information.

4. The information publishing service method according to claim 3, characterized in that: The step S300 includes the following steps: Step S301: According to the information type of the information publishing platform, in the characteristic information set of any mode, the information type of the characteristic information is collected, and a certain information type collected is summarized to obtain the characteristic information set of any information type; Step S302: input the feature information in the feature information set of any information type into the NLP model for text analysis, extract the terms of the feature information, summarize the number of times any term appears in the feature information set, and calculate the frequency of a term in any feature information according to the following formula: Among them, Yt m It is represented by the frequency of the mth term in the tth feature information, yt m It is represented by the number of occurrences of the mth term in the tth feature information, y t It is represented by the number of occurrences of all terms in the t-th feature information; Step S303: In the reading index set of feature information, the weight of a certain term in any feature information is calculated according to the following formula: Ztm=Ytm×X Among them, Zt m It is represented as the weight of the mth term in the tth feature information, X t It is expressed as the reading index of the t-th feature information, and the entry weight set of any feature information is obtained. The feature information of any information type is summarized to obtain the weight set of a certain entry in any information type.

5. The information publishing service method according to claim 4, characterized in that: The step S400 includes the following steps: Step S401: dividing the weight set of a certain term of the same information type according to any method of obtaining information, and obtaining the weight set of a certain term of any method in the same information type; Step S402: Calculate the weight of a term in any information type according to the following formula: Among them, Zp m It is expressed as the weight of the mth term in the pth information type, Zrpm i It is expressed as the i-th weight of the m-th term in the p-th information type in the r-th mode, jrp m It is expressed as the number of weight sets of the mth term in the pth information type in the rth way, α r It is represented as the weight of the rth way, L is represented as the number of ways to obtain information, and the weight set of terms of any information type is obtained; Step S403: Divide the term weights into several levels, divide the term weight set of any information type according to the levels, confirm the term level of any information type and summarize it to obtain a term set of a certain level in any information type.

6. The information publishing service method according to claim 5, characterized in that The step S500 includes the following steps: Step S501: obtaining a user's rating record for a certain historical browsing information, and collecting information types and entries of the browsing information, determining an entry set of any level according to the collected information type, comparing the collected entries with the entry set of any level, and obtaining the number of entries of any level; Step S502: The rating of the information and the number of entries of any level are regarded as a data group, the rating records of the historical browsing information are summarized, and a function fitting is performed on several data groups according to the following formula: Among them, H represents the score of information, S e Expressed as the number of terms at level e, β e It is represented as the weight of the e-th level, f is the number of levels, S is the total number of terms that meet any level in any information, S1 is the total number of terms in any information, and γ is the weight of the information score, and the prediction score model for any information type is obtained.

7. The information publishing service method according to claim 6, characterized in that The step S600 includes the following steps: Step S601: obtaining the information type and terms of the real-time information to be published, determining the number of terms that meet the arbitrary level of a certain user, inputting the number of terms at the arbitrary level into a prediction and scoring model of the same information type, and obtaining the user's prediction score; Step S602: When the predicted score of the user is greater than or equal to the score threshold, the information to be released is set as information of interest, a set of information of interest is obtained, the set is sorted from high to low according to the predicted score of the information of interest, and the sorted set of information of interest is pushed to the user.

8. An information publishing service system, used to implement an information publishing service method as described in any one of claims 1 to 7, characterized in that: The system includes a historical information browsing record module, a feature information module, an entry weight module, an entry level module, a rating prediction model module, and a real-time sorting module; The historical information browsing record module: obtains the user's historical information browsing record through the information publishing platform system, collects the information acquisition method of the historical information browsing record, divides the historical information browsing record according to different information acquisition methods, and obtains a historical information browsing record set in any method; The characteristic information module: in any mode of historical information browsing record set, obtains the reading parameters of the user after browsing the information, and obtains the reading index of the information by calculation, and determines whether the information is characteristic information; The term weight module: confirms the type of the feature information according to the information type of the information publishing platform system, performs text processing on any type of feature information, obtains the term composition and term proportion of the feature information, and calculates the weight of any term; The term ranking module: calculates the weight of any term of any information type according to the weight of any term in step S300, and classifies the terms according to the weight; The rating prediction model module: obtains the user's rating record for a certain historical browsing information, in which the user's rating of the information and the number of entries of any level are combined into a data group, and a function is fitted on the data group to obtain a rating prediction model for any information type; The real-time sorting module sorts the real-time information to be published according to the rating prediction model, and pushes the information to the user in the sorted order.

9. An information publishing service system according to claim 8, characterized in that: The term weight module includes a feature information classification unit and a term weight calculation unit; The characteristic information classification unit: collects information types of characteristic information according to the information type of the information publishing platform, summarizes a certain information type collected, and obtains a characteristic information set of any information type; The term weight calculation unit extracts terms from feature information through a natural language processing model, calculates the frequency of terms appearing in the feature information, and calculates the weight of the terms based on the frequency and the reading index of the feature information.

10. An information publishing service system according to claim 8, characterized in that: The term ranking module includes a term weight calculation unit and a ranking determination unit; The term weight calculation unit: obtains a weight set of a term in any manner in the same information type, calculates the weight set, and obtains the weight of a term in any information type; The level determination unit divides the term weights into several levels, divides the term weight set of any information type according to the levels, confirms and summarizes the term level of any information type, and obtains a term set of a certain level in any information type.

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