Recommending method based on high-order user preferences

Abstract

The invention provides a recommending method based on high-order user preferences. The method comprises the following steps of: using a user-object grading data set to calculate a high-order preference value of a user on an object, and building a user-object-object paired preference data set; initializing a model, and randomly selecting values from normal distribution to initialize the model; selecting relevant data from the grading data set and the paired preference data set; calculating errors and the user preference similarity to form an optimization criterion; calculating the preference index gradient, and updating the model; and repeating the steps till the model parameter convergence. The method provided by the invention consists of an OPTRP optimization criterion and an LearnRP learning algorithm; through the learning, an existing CF model can be generated; a new model can also be generated; a highly deviated grading data set can be effectively processed, so that the prediction precision and the recommending precision of a recommending system are improved; and important application values are realized in real scenes.

Description

technical field [0001] The invention relates to the fields of machine learning and pattern recognition, especially a recommendation method based on high-order user preferences. Background technique [0002] In recent years, with the rapid development of the network, people are faced with a large amount of information every day (that is, information overload). Faced with thousands of information, people are tired of finding valuable information that they are interested in. The recommendation system appears to solve the problem of information overload. The recommendation system is an information filtering technology, which can filter out valuable content that users are interested in from a large amount of information and provide it to users, so that users can be freed from complicated information. Commonly used recommender system technologies include content-based recommender systems, collaborative filtering-based recommender systems, and hybrid recommender systems, the most ...

AI Technical Summary

Problems solved by technology

Although highly skewed datasets with high skewness are ubiquitous, few works have studied the distribution of user ratings, and traditional collaborative filtering techniques cannot handle highly skewed datasets well.

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