The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Time Interval Aware Self-Attention for Sequential Recommendation
Open accessAbstract
Sequential recommender systems seek to exploit the order of users' interactions, in order to predict their next action based on the context of what they have done recently. Traditionally, Markov Chains(MCs), and more recently Recurrent Neural Networks (RNNs) and Self Attention (SA) have proliferated due to their ability to capture the dynamics of sequential patterns. However a simplifying assumption made by most of these models is to regard interaction histories as ordered sequences, without regard for the time intervals between each interaction (i.e., they model the time-order but not the actual timestamp). In this paper, we seek to explicitly model the timestamps of interactions within a sequential modeling framework to explore the influence of different time intervals on next item prediction. We propose TiSASRec (Time Interval aware Self-attention based sequential recommendation), which models both the absolute positions of items as well as the time intervals between them in a sequence. Extensive empirical studies show the features of TiSASRec under different settings and compare the performance of self-attention with different positional encodings. Furthermore, experimental results show that our method outperforms various state-of-the-art sequential models on both sparse and dense datasets and different evaluation metrics.
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Cites
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Adam: A Method for Stochastic Optimization
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Neural Machine Translation by Jointly Learning to Align and Translate
Bahdanau, D., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1409.0473 · Cited by 16,213
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
Xu, K., Ba, J., Kiros, R., Cho, K., Courville, A., Salakhutdinov, R., Zemel, R., & Bengio, Y. (2015) · arXiv · doi:10.48550/arxiv.1502.03044 · Cited by 8,248
BPR: Bayesian Personalized Ranking from Implicit Feedback
Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1205.2618 · Cited by 4,242
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Koren, Y. (2008) · Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1401890.1401944 · Cited by 3,852
Factorizing personalized Markov chains for next-basket recommendation
Rendle, S., Freudenthaler, C., & Schmidt-Thieme, L. (2010) · Proceedings of the 19th international conference on World wide web · doi:10.1145/1772690.1772773 · Cited by 2,692
Self-Attention with Relative Position Representations
Shaw, P., Uszkoreit, J., & Vaswani, A. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) · doi:10.18653/v1/n18-2074 · Cited by 2,505
Image-Based Recommendations on Styles and Substitutes
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Self-citationPersonalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
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Cited by
Filter-enhanced MLP is All You Need for Sequential Recommendation
Zhou, K., Yu, H., Zhao, W. X., & Wen, J.-R. (2022) · Proceedings of the ACM Web Conference 2022 · doi:10.1145/3485447.3512111 · Cited by 401
Geography-Aware Sequential Location Recommendation
Lian, D., Wu, Y., Ge, Y., Xie, X., & Chen, E. (2020) · Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3394486.3403252 · Cited by 253
Metadata
- Authors
- Li, J., Wang, Y., & McAuley, J.
- Venue
- Proceedings of the 13th International Conference on Web Search and Data Mining
- Publisher
- ACM
- Published
- 2020-01-20
- Pages
- 322-330
- OpenAlex
- W2996931760
- Cited by
- 649
- References
- 31
- FWCI
- 59.93
- Type
- conference paper
- Language
- en
- Open access
- gold
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