10 results about "Item score" patented technology

Systems and methods for generating enhanced user interfaces are disclosed. A set of candidate items responsive to a request for an enhanced interface is generated. The set of candidate items is responsive to a query or an anchor item. A composite candidate score for each candidate item is generated by a pick item scoring engine based on one or more historical interactions. A set of top-k items is selected from the set of candidate items and at least one pick item is selected from the set of top-k items. At least one enhancement interface element is selected for display in conjunction with the at least one pick item and a response to the request for the enhanced interface is transmitted that causes a receiving device to generate a user interface including the at least one pick item displayed in conjunction with the at least one enhancement interface element.

The disclosure provides a long-term care insurance disability evaluation quality monitoring method based on a large model, which comprises the following steps: performing text transcription on the audio data to be processed to obtain dialogue content text; determining first text corresponding to an evaluator user and second text corresponding to an evaluated user from the dialogue content text; analyzing the first text based on a large language model to obtain a first analysis result, wherein the first analysis result is used to indicate at least one evaluation item and a first time interval corresponding to each evaluation item; analyzing the second text based on the large language model to obtain a second analysis result, wherein the second analysis result is used to indicate at least one item evaluation score and a second time interval corresponding to each evaluation item score; and performing correlation analysis on the first analysis result and the second analysis result according to the first time interval and the second time interval to determine a disability grade judgment result of an insured person and an evaluation quality report.

The invention is applicable to the technical field of recommendation, and provides a collaborative filtering recommendation method based on double popularity constraints, which comprises the following steps of: firstly, constructing a user item score table and an item attribute relation table; secondly, obtaining normalized item popularity and normalized attribute popularity, and obtaining constraint factors by using double popularity; performing similarity calculation by using a dual popularity constraint factor and a Pearson similarity formula to obtain adjacent users of the target user; according to the scoring information of the adjacent users of the target user, performing prediction scoring on the item set which is not scored by the target user to obtain a prediction scoring table of the target user; and finally, extracting first N items with the highest score value from the prediction score table as recommendation results and providing the recommendation results to the target user. According to the method, by constraining the item popularity and the attribute popularity, the popularity deviation is relieved, and the long-tail effect is relieved.

Described is a computing apparatus (500) for evaluating a scoring model (302), the computing apparatus (500) being configured to: obtain, for each of a plurality of items (204a, 204b, 204c, 204d) in an item set (204), an item score (205a, 205b, 205c, 205d); obtain, for each of the plurality of items (204a, 204b, 204c, 204d) in the item set (204), a scoring model-generated score (303a, 303b, 303c, 303d), the scoring model-generated score (303a, 303b, 303c, 303d) having been determined by a scoring model (302); and correlate the item score (205a, 205b, 205c, 205d) and the scoring model-generated score (303a, 303b, 303c, 303d); and evaluate the scoring model (302) in dependence on the correlation (301). In this way, the accuracy of the scoring model (302) may be evaluated based on a comparison to the item score (205a, 205b, 205c, 205d).

An online system receives information identifying items associated with a brand, a hierarchical taxonomy of the items, and information identifying a retailer associated with the brand. The system applies a machine learning model to predict availabilities of the items at (a) retailer location(s) associated with the retailer, identifies items that are likely available at the retailer location(s), and groups the identified items into categories based on the taxonomy. The system computes an item score for each item based on its popularity, attributes, and / or attributes of a user. The system assigns items in each category to positions within a display unit associated with the category and computes a category score for each category based on the item scores. The system assigns display units associated with the categories to positions within a template based on the category score and generates a page associated with the brand and retailer based on the assignments.

There is described a computing device (500) for evaluating a scoring model (302), the computing device (500) for: for each of a plurality of items (204a, 204b, 204c, 204d) in a set of items (204), obtaining an item score (205a, 205b, 205c, 205d); for each item of the plurality of items (204a, 204b, 204c, 204d) in the item set (204), obtaining a score (303a, 303b, 303c, 303d) generated by a scoring model, the score (303a, 303b, 303c, 303d) generated by the scoring model being determined by a scoring model (302); associating the item score (205a, 205b, 205c, 205d) with a score (303a, 303b, 303c, 303d) generated by the scoring model; and evaluating the scoring model (302) according to the correlation (301). In this manner, the accuracy of the scoring model (302) may be evaluated based on a comparison with the item scores (205a, 205b, 205c, 205d).

This application is directed to systems and methods for organizing information. In some embodiments, a disclosed method includes receiving a plurality of requests associated with a plurality of items corresponding to a plurality of item types, where each item type includes one or more items; selecting, from the plurality of item types, a subset of target item types that satisfy a predefined type selection criterion associated with the plurality of requests; for each item type of the subset of target item types, selecting a set of boosting items from a set of target items of the respective target item type based on an item score of each of the set of target items; and generating an ordered list of boosting information items for the subset of target item types by consolidating the sets of boosting items of the subset of target item types.

The invention relates to the field of education evaluation, and particularly discloses a big data-based education comprehensive evaluation method and system, and the method comprises the steps: obtaining the initial score and comment of a student; the method comprises the following steps: acquiring historical comments, establishing an rnnlm language model, performing word segmentation, filtering, classification, part-of-speech analysis, part-of-speech tagging and character extraction on the comments by the rnnlm language model to obtain a plurality of phrases, performing semantic understanding to obtain semantic results of the plurality of phrases, and synthesizing the semantic results of the plurality of phrases to obtain a comment score; adding the comment score to the initial score to obtain a subitem score of the student; and multiplying the subitem score by the influence factor of the subitem to obtain a comprehensive education score of the student. According to the method, examination scores and comment scores in the aspects of culture, sports and the like are considered, the examination scores and the comment scores are combined to evaluate the ability of students in a certain aspect, then the ability is multiplied by influence factors in all aspects, comprehensive education scores of the students are obtained, and the students can be evaluated more accurately.

Systems and methods for generating enhanced user interfaces are disclosed. A set of candidate items responsive to a request for an enhanced interface is generated. The set of candidate items is responsive to a query or an anchor item. A composite candidate score for each candidate item is generated by a pick item scoring engine based on one or more historical interactions. A set of top-k items is selected from the set of candidate items and at least one pick item is selected from the set of top-k items. At least one enhancement interface element is selected for display in conjunction with the at least one pick item and a response to the request for the enhanced interface is transmitted that causes a receiving device to generate a user interface including the at least one pick item displayed in conjunction with the at least one enhancement interface element.

The invention discloses a dialogue recommendation method and system based on intention awareness, and the method comprises the steps: firstly collecting historical dialogue information, carrying out the coding of the historical dialogue information through a preprocessing module, and obtaining the text embedding of a dialogue level; then candidate items are obtained through a retrieval module, text descriptions of the candidate items are coded, and item samples of the item level are obtained; then, item scores corresponding to all item samples screened on the basis of the current dialogue are obtained through a rearrangement module on the basis of text embedding of the dialogue level and item samples of the item level, and system responses corresponding to user input in each round of dialogue are obtained on the basis of the item scores, so that user requirements are deeply analyzed; products and services better meeting user expectations are customized, and important support is provided for the field of personalized recommendation.