Method and device for recommending items

Abstract

The invention is suitable for the technical field of information and provides a method and a device for recommending items. The method comprises the following steps: acquiring item information within a preset distance scope on a present position of a to-be-recommended user; acquiring a candidate user group according to the item information; calculating an interactive behavior similarity and an item scoring similarity between the to-be-recommended user and each candidate user in the candidate user group; calculating a compound similarity between the to-be-recommended user and each candidate user according to the interactive behavior similarity and the item scoring similarity; selecting N candidate users with the maximal compound similarity and forming a neighbor set of the to-be-recommended user; acquiring the item concerned by each candidate user in the neighbor set; and forecasting a score of the item scored by the to-be-recommended user and recommending M items in the highest scores to the to-be-recommended user. According to the invention, the problems of sparse data and cold starting of the present individual recommendation technique can be solved and the item recommendation accuracy is promoted.

Description

technical field [0001] The invention belongs to the field of information technology, and in particular relates to a method and device for item recommendation. Background technique [0002] The personalized recommendation technology for the Internet establishes the user's interest model by mining the user's historical browsing information, purchase information, consumption frequency and product evaluation, and then recommends advertisements, products and other item information to the user according to the interest model. However, for users and items that join the recommendation system for the first time, because there is no historical record, the existing personalized recommendation counts cannot recommend to the user, nor can the newly added item be recommended to the user. With the rapid increase of users and items, there are more and more items without user ratings, which leads to the scarcity of matrix values, which seriously affects the accuracy of recommendation results...

AI Technical Summary

Problems solved by technology

[0004] In view of this, the embodiment of the present invention provides a method and device for item recommendation to solve the problems of data sparseness, cold start and information overload in the existing personalized recommendation technology

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