With development of urban modernization, there are a large number of hop spots covering the entire city, defined as Pionts-of-Interest (POIs) Group consist of POIs. POI Groups have a significant impact on people’s lives and urban planning. Every person has her/his own personalized POI Groups (PPGs) based on preferences and friendship in location-based social networks (LBSNs). However, there are almost no researches on this aspect in recommendation systems. This paper proposes a novel PPGs Recommendation algorithm, and models the PPGs by expanding the model of DBSCAN. Our model considers the degree to each PPG covering the target users’ POI preferences. The system recommends the target user with the PPGs which have the top-N largest scores, and it is one NP-hard problem. This paper proposes the greedy algorithm to solve it. Extensive experiments on the two LBSN datasets illustrate the effectiveness of our proposed algorithm.
|Original language||English (US)|
|Title of host publication||Web and Big Data - 1st International Joint Conference, APWeb-WAIM 2017, Proceedings|
|Editors||Christian S. Jensen, Xiang Lian, Lei Chen, Cyrus Shahabi, Xiaochun Yang|
|Number of pages||10|
|State||Published - 2017|
|Event||1st Asia-Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2017 - Beijing, China|
Duration: Jul 7 2017 → Jul 9 2017
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||1st Asia-Pacific Web and Web-Age Information Management Joint Conference on Web and Big Data, APWeb-WAIM 2017|
|Period||7/7/17 → 7/9/17|
Bibliographical noteFunding Information:
Acknowledgments. This work was supported in part by the National Science Foundation grants NSF-61672196, NSF-61370214, NSF-61300210.
- Density-based clustering
- Geo-social distance
- POI group recommendation