Trust degree and metric factor matrix decomposition fused interest point recommendation method and system

Abstract

The invention discloses an interest point recommendation method and system fusing credibility and metric factor matrix decomposition. According to the recommendation method and system, construction ofa user social relation matrix from different perspectives is the key of research, user information in a social network and interaction information between users are utilized to construct a user social trust network, and the user social trust network and a metric factor matrix decomposition model are fused for comprehensive recommendation. According to the recommendation method and system, information related to the user is mined from different perspectives through data breadth and depth, user attributes and behaviors are restored more truly, and the user preference model is established, so that recommendation of the user interest points is more accurate and more personalized, and a better recommendation effect is achieved.

Description

technical field [0001] The invention relates to the technical field of information retrieval and recommendation, in particular to a method and system for recommending points of interest based on fusion of trust and measurement factor matrix decomposition. Background technique [0002] With the continuous advancement of Internet technology, applications based on social networks have developed rapidly and have attracted widespread attention from the industry. In these social networks, users have their own social relationships, evaluation of things and sharing of their lives, and they can also follow Some dynamics of friends. Therefore, it contains a wealth of data. Useful information can be mined through check-in information, friend relationships, evaluation data, etc. to better build a user's interest preference model, and recommend to users attractions that they may be more interested in and give better ratings. , this type of recommendation is called Point-of-Interest (POI...

AI Technical Summary

Problems solved by technology

However, compared with other recommender systems, personalized travel recommender systems will face greater challenges. The structure of travel data is more complex and difficult to obtain; users’ evaluations and historical information are difficult to accurately express their needs; users’ Historical information and personal information are very scarce; tourism data is also very sparse and redundant

The effect of traditional recommendation methods applied to personalized travel recommendations is not satisfactory, and collaborative filtering recommendations cannot solve the problems of data sparsity, cold start, and new city recommendations faced by travel recommendations.

Images