Query2Trip: Dual-Debiased Learning for Neural Trip Recommendation
Paper
Wang, Peipei, Li, Lin, Wang, Ru and Tao, Xizohui. 2023. "Query2Trip: Dual-Debiased Learning for Neural Trip Recommendation." 28th International Conference on Database Systems for Advanced Applications (DASFAA 2023). Tianjin, China 17 - 20 Apr 2023 Switzerland . Springer. pp. 80-96 https://doi.org/10.1007/978-3-031-30672-3_6
| Paper/Presentation Title | Query2Trip: Dual-Debiased Learning for Neural Trip Recommendation |
|---|---|
| Presentation Type | Paper |
| Authors | Wang, Peipei, Li, Lin, Wang, Ru and Tao, Xizohui |
| Journal or Proceedings Title | Proceedings of 28th International Conference on Database Systems for Advanced Applications (DASFAA 2023) |
| Journal Citation | 13944, pp. 80-96 |
| Page Range | 80-96 |
| Number of Pages | 17 |
| Year | 2023 |
| Publisher | Springer |
| Place of Publication | Switzerland |
| ISBN | 9783031306716 |
| 9783031306723 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-031-30672-3_6 |
| Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-031-30672-3_6 |
| Web Address (URL) of Conference Proceedings | https://link.springer.com/book/10.1007/978-3-031-30672-3 |
| Conference/Event | 28th International Conference on Database Systems for Advanced Applications (DASFAA 2023) |
| Event Details | 28th International Conference on Database Systems for Advanced Applications (DASFAA 2023) Parent Database Systems for Advanced Applications Delivery In person Event Date 17 to end of 20 Apr 2023 Event Location Tianjin, China |
| Abstract | Trip recommendation (TripRec) seeks to recommend a trip that consists of an ordered sequence of points-of-interest (POIs) for a tourist through a user-specific query. Recent neural TripRec methods with sequence-to-sequence models have achieved remarkable performance. However, alongside the exposure bias in general recommender systems, the selection bias caused by the lack of explicit feedback (e.g., ratings) from the trip data exacerbates the tendency toward users’ unsatisfactory experience in TripRec. To this end, a novel debiased representation learning method for neural TripRec is proposed to fulfill sequence generation from Query to Trip named Query2Trip. It develops dual-debiased learning to mitigate selection bias and exposure bias in TripRec. The former happens as the visit by a user does not necessarily mean the user exhibits a positive preference for the visit. Benefiting from the query provided by a user, Query2Trip designs a debiased adversarial learning module by conditional guidance to alleviate this selection bias from positives (visited). The latter happens as unvisited is not equivalent to negative. Query2Trip devises a debiased contrastive learning module by negative weighting to mitigate this exposure bias from negatives (unvisited). Experiments conducted on eight real-world datasets empirically demonstrate the superior performance of Query2Trip compared to the state-of-the-art baselines. |
| Keywords | Debiased learning; Trip recommendation; Sequence generat |
| ANZSRC Field of Research 2020 | 461104. Neural networks |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Series | Lecture Notes in Computer Science |
| Byline Affiliations | Wuhan University of Technology, China |
| Shandong Normal University, China | |
| School of Mathematics, Physics and Computing |
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