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Home / Archives / Volume-4 / Issue-2 / Article-1

Volume - 4 | Issue - 2 | june 2022

POI Recommendation for Social Relations based on WORD2VEC
Li Yang-yang  , Wang Ya-jun, Zhang Mi-yuan  174  93
Pages: 87-98
Cite this article
Yang-yang, L., Ya-jun, W. & Mi-yuan, Z. (2022). POI Recommendation for Social Relations based on WORD2VEC. Journal of Artificial Intelligence and Capsule Networks, 4(2), 87-98. doi:10.36548/jaicn.2022.2.001
Published
18 May, 2022
Abstract

Most of the traditional recommendation algorithm models are recommended based on the user's own historical preferences, although it can recommend POI for users to a certain extent. But in real life, people are more willing to ask their friends what they think when they have a difficult decision. Therefore, a word2vec-based social relationship point of interest recommendation model (W-SimTru) is proposed, which combines the similarity of friends based on cosine similarity with the friend trust recommendation algorithm based on TF-IDF to improve the model recommendation effect. In addition, before modeling the similarity of users, word2vec is used to process the user's historical check-in behavior to solve the problem of inaccurate recommendation due to sparse check-in data. Finally, experiments are carried out on three datasets of Los Angeles, Washington and NYC in Gowalla, and the experimental results show that the proposed W-SimTru recommendation algorithm outperforms the algorithms of the three comparative experiments.

Keywords

Points of interest User similarity Friend trust TF-IDF Word2vec

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