Name
Abstract | ||
|---|---|---|
ABSTRACTNext Point-of-Interest (POI) recommendation plays an important role in location-based services. The state-of-the-art methods utilize recurrent neural networks (RNNs) to model users' check-in sequences and have shown promising results. However, they tend to recommend POIs similar to those that the user has often visited. As a result, users become bored with obvious recommendations. To address this issue, we propose Serendipity-oriented Next POI Recommendation model (SNPR), a supervised multi-task learning problem, with objective to recommend unexpected and relevant POIs only. To this end, we define the quantitativeserendipity as a trade-off ofrelevance andunexpectedness in the context of next POI recommendation, and design a dedicated neural network with Transformer to capture complex interdependencies between POIs in user's check-in sequence. Extensive experimental results show that our model can improverelevance significantly while theunexpectedness outperforms the state-of-the-art serendipity-oriented recommendation methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3459637.3482394 | Conference on Information and Knowledge Management |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingwei Zhang | 1 | 0 | 0.34 |
| Yang Yang | 2 | 0 | 0.34 |
| Rizwan Abbas | 3 | 0 | 0.34 |
| Ke Deng | 4 | 0 | 0.68 |
| Jianxin Li | 5 | 443 | 48.67 |
| Bin Zhang | 6 | 213 | 41.40 |