Item-based explicit and implicit feedback mixing collaborative filtering recommendation algorithm

Abstract

The invention discloses an item-based explicit and implicit feedback mixing collaborative filtering recommendation algorithm. The method comprises the following steps of obtaining the information of interest of users on every item and establishing the score matrix of every user on all the items; calculating the average score of every user, the quantity of the scoring users of every item and the average score of every item; calculating a common comment user quantity matrix; calculating the Pearson similarity and the modified cosine similarity of between any two items; calculating the similarity based on explicit feedback; calculating the cosine similarity based on implicit feedback; calculating a final similarity; obtaining the nearest neighbor set I of a current item; when providing a recommendation list to a target user u, according to the score matrix, obtaining the scored items and the unscored items of the target user u; calculating the prediction scores of the unscored items of the target user u and selecting N items with the highest scores inside the unscored items of the target user u to the user. The item-based explicit and implicit feedback mixing collaborative filtering recommendation algorithm can effectively improve the accuracy of prediction recommendation.

Description

technical field [0001] The invention relates to the technical field of personalized recommendation, in particular to an item-based collaborative filtering recommendation algorithm with mixed explicit and implicit feedback. Background technique [0002] The recommendation system is an intelligent agent system proposed to solve the problem of information overload, which can automatically recommend resources that meet their interests, preferences or needs from a large amount of information to users. With the popularity and rapid development of the Internet, recommender systems have been widely used in various fields, especially in the field of e-commerce, recommender systems have been more and more researched and applied. At present, almost all large-scale e-commerce websites use various forms of recommendation systems to varying degrees, such as Amazon, eBay, and Dangdang online bookstores. Among the existing recommendation systems, collaborative filtering technology has achie...

AI Technical Summary

Problems solved by technology

In the traditional item-based collaborative filtering recommendation algorithm, there are three traditional calculation methods for predicting the similarity between items based on explicit ratings: cosine similarity, Pearson similarity, and modified cosine similarity. People usually choose the most commonly used One of the Pearson similarity and modified cosine similarity is calculated as a standard, but they ignore their different emphases and can jointly contribute to the final similarity.

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