Name
Abstract | ||
|---|---|---|
Next Point-of-interest (POI) recommendation has been recognized as an important technique in location-based services, and existing methods aim to utilize sequential models to return meaningful recommendation results. But these models fail to fully consider the phenomenon of user interest drift, i.e. a user tends to have different preferences when she is in out-of-town areas, resulting in sub-optimal results accordingly. To achieve more accurate next POI recommendation for out-of-town users, an adaptive attentional deep neural model HOPE is proposed in this paper for modeling user's out-of-town dynamic preferences precisely. Aside from hometown preferences of a user, it captures the long and short-term preferences of the user in out-of-town areas using "Asymmetric-SVD" and "TC-SeqRec" respectively. In addition, toward the data sparsity problem of out-of-town preference modeling, a region-based pattern discovery method is further adopted to capture all visitor's crowd preferences of this area, enabling out-of-town preferences of cold start users to be captured reasonably. In addition, we adaptively fuse all above factors according to the contextual information by adaptive attention, which incorporates temporal gating to balance the importance of the long-term and short-term preferences in a reasonable and explainable way. At last, we evaluate the HOPE with baseline sequential models for POI recommendation on two real datasets, and the results demonstrate that our proposed solution outperforms the state-of-art models significantly. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s11280-021-00895-2 | WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS |
Keywords | DocType | Volume |
Next POI recommendation, Out-of-town POI recommendation, Sequential recommendation | Journal | 24 |
Issue | ISSN | Citations |
5 | 1386-145X | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huimin Sun | 1 | 0 | 1.01 |
| Jiajie Xu | 2 | 1 | 0.68 |
| Rui Zhou | 3 | 21 | 17.94 |
| Wei Chen | 4 | 1711 | 246.70 |
| Lei Zhao | 5 | 30 | 2.42 |
| Chengfei Liu | 6 | 1402 | 127.17 |