Recommendation probability fusion based hybrid recommendation method

Abstract

The invention discloses a recommendation probability fusion based hybrid recommendation method. The method includes the following steps: (1) using a two-dimensional table for representing scoring data of commodities; (2) taking any items in a scored set as unknowns, utilizing a basic recommendation method to obtain a prediction result of each corresponding item, training sets of scores of the scored items and the forecast results of the corresponding items by the aid of a neural network so as to obtain an SFM (score forecast model); (3) utilizing the basic recommendation method to obtain non-scored item forecast results, and utilizing the SFM to obtain a final forecast value of each non-scored item of a set of the non-scored item forecast results; (4) sorting all the non-scored items of a user according to size of each forecast value in a final forecast value set in a descending manner to obtain a non-scored item sorted set, and selecting top N items of the non-scored item sorted set as recommendation results to be recommended to the user. By the method, true situation of user evaluation can be effectively reflected, and precision in personalized recommendation is improved.

Description

technical field [0001] The invention belongs to the field of electronic commerce, in particular to a hybrid recommendation method based on fusion of recommendation probabilities. Background technique [0002] With the rapid development of e-commerce, the phenomenon of information overload is becoming more and more serious. How to meet the individual needs of users based on a massive collection of commodities has become an important issue to improve user experience and user satisfaction. Personalized recommendation system is an important means to meet the individual needs of users. The personalized recommendation system builds a user interest preference model based on the user's individual online browsing data or purchase data, so as to recommend products that meet their unique needs to users. Personalized product recommendation has been widely used in Amazon, JD.com, Taobao and other e-commerce websites, which effectively increases the possibility of users buying and impro...

AI Technical Summary

Problems solved by technology

The existing method expresses the user rating as a specific value, ignoring the uncertainty of user evaluation, unable to reflect the real situation of user evaluation products, and reducing the accuracy of the recommendation system

[0005] (2) Fusion of recommendation information

For example, a collaborative filtering recommendation algorithm based on item rating prediction uses the prediction results obtained by the item-based collaborative filtering method as the input of the user-based collaborative filtering method. method, but it does not fuse the results of user-based collaborative filtering and item-based collaborative filtering

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