Interest Point Recommendation based on Multi Scenario Information Fusion
Hongfei Xu, Jia Wu, Wei Cui, Yuxiao Zhang · 2020
In order to solve the above problems, this paper proposes a method of interest points recommendation based on location-based social network fusion of multiple situation information based on the user data of check-in on location-based social network. The social network is transformed into a friend set, and the user interest information model is established based on the user and interest point backward lookup table method, which reduces the running time of interest point recommendation and improves the recommendation efficiency. LDA model is used to model the user's emotion, and the topic probability distribution of user's comments is calculated, and the similarity of user's emotional tendency is mined out. The geographic impact information is analyzed, the geographic impact information model is established, and the access probability of geographic information is calculated. The results show that the proposed method can improve the accuracy of recommendation, reduce the impact of data sparsity, and improve the efficiency of recommendation.