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 Cui、Caiyin Wang、Zhiwei Zhang、Xiaoyong Yu、Fanghui 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 recommendation、Geo-Social Relationship、Topic 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 |
|
QR Code | |
該期刊 上一篇
| Using Cost-cognitive Bagging Ensemble to Improve Cross-project Defects Prediction |
該期刊 下一篇
| Hybrid Approach of CNN and SVM for Shrimp Freshness Diagnosis in Aquaculture Monitoring System using IoT based Learning Support System |