dc.contributor.author | Sun, Ke | |
dc.contributor.author | Qian, Tieyun | |
dc.contributor.author | Chen, Tong | |
dc.contributor.author | Liang, Yile | |
dc.contributor.author | Nguyen, Quoc Viet Hung | |
dc.contributor.author | Yin, Hongzhi | |
dc.date.accessioned | 2020-11-13T04:37:01Z | |
dc.date.available | 2020-11-13T04:37:01Z | |
dc.date.issued | 2020 | |
dc.identifier.isbn | 978-1-57735-835-0 | |
dc.identifier.issn | 2159-5399 | |
dc.identifier.doi | 10.1609/aaai.v34i01.5353 | |
dc.identifier.uri | http://hdl.handle.net/10072/399266 | |
dc.description.abstract | Point-of-Interest (POI) recommendation has been a trending research topic as it generates personalized suggestions on facilities for users from a large number of candidate venues. Since users' check-in records can be viewed as a long sequence, methods based on recurrent neural networks (RNNs) have recently shown promising applicability for this task. However, existing RNN-based methods either neglect users' long-term preferences or overlook the geographical relations among recently visited POIs when modeling users' short-term preferences, thus making the recommendation results unreliable. To address the above limitations, we propose a novel method named Long- and Short-Term Preference Modeling (LSTPM) for next-POI recommendation. In particular, the proposed model consists of a nonlocal network for long-term preference modeling and a geo-dilated RNN for short-term preference learning. Extensive experiments on two real-world datasets demonstrate that our model yields significant improvements over the state-of-the-art methods. | |
dc.description.peerreviewed | Yes | |
dc.publisher | Association for the Advancement of Artificial Intelligence (AAAI) Press | |
dc.relation.ispartofconferencename | Thirty-Fourth AAAI Conference on Artificial Intelligence | |
dc.relation.ispartofconferencetitle | Proceedings of the AAAI Conference on Artificial Intelligence | |
dc.relation.ispartofdatefrom | 2020-02-07 | |
dc.relation.ispartofdateto | 2020-02-12 | |
dc.relation.ispartoflocation | New York, USA | |
dc.relation.ispartofpagefrom | 214 | |
dc.relation.ispartofpageto | 221 | |
dc.relation.ispartofissue | 01 | |
dc.relation.ispartofvolume | 34 | |
dc.subject.fieldofresearch | Pattern recognition | |
dc.subject.fieldofresearch | Data mining and knowledge discovery | |
dc.subject.fieldofresearchcode | 460308 | |
dc.subject.fieldofresearchcode | 460502 | |
dc.title | Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation | |
dc.type | Conference output | |
dc.type.description | E1 - Conferences | |
dcterms.bibliographicCitation | Sun, K; Qian, T; Chen, T; Liang, Y; Nguyen, QVH; Yin, H, Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation, Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34 (01), pp. 214-221 | |
dc.date.updated | 2020-11-13T04:35:28Z | |
gro.hasfulltext | No Full Text | |
gro.griffith.author | Nguyen, Henry | |