Fusing Dual Geo-Social Relationship and Deep Implicit Interest Topic Similarity for POI Recommendation,ERICDATA高等教育知識庫
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篇名
Fusing Dual Geo-Social Relationship and Deep Implicit Interest Topic Similarity for POI Recommendation
並列篇名
Fusing Dual Geo-Social Relationship and Deep Implicit Interest Topic Similarity for POI Recommendation
作者 Lin CuiCaiyin WangZhiwei ZhangXiaoyong YuFanghui Zha
英文摘要

Nowadays, POI recommendation has been a hot research area, which are almost based on incomplete social relationships and geographical influence. However, few research simultaneously focuses on the refined social relationship and the user deep implicit topic similarity under a reachable region. Under this background, a novel Dual Geo-Social Relationship and Deep Implicit Interest Topic Similarity mining under a Reachable Region for POI Recommendation (DDR-PR) is proposed. DDR-PR first adopts kernel density estimation to compute the user checking-in reachable area. Under the reachable area, the combined relationship similarity based on the link relationship and common check-in social relationship is computed out. Then, the deep implicit interest topic similarity between users is mined out adopting the proposed topic model RTAU-TCP. We formulate the combined relationship similarity and implicit interest topic similarity as two regularization terms to incorporate into matrix factorization, which can recommend new POIs for a user under his or her reachable area. Extensive experiments prove the superiority of DDR-PR.

 

起訖頁 791-799
關鍵詞 POI recommendationGeo-Social RelationshipTopic similarity
刊名 網際網路技術學刊  
期數 202207 (23:4期)
出版單位 台灣學術網路管理委員會
DOI 10.53106/160792642022072304014  複製DOI
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