As users increasingly turn to on-demand television platforms such as IPTV for leisure, effective recommendation systems that leverage users’ historical viewing sequences have become essential for enhancing user satisfaction and fostering long-term loyalty. However, existing sequential recommendation models exhibit notable limitations in the video-on-demand context. First, they often fail to adequately capture multi-dimensional temporal features, particularly watching duration, within user behavior sequences. Second, they typically overlook the distinct repeat and explore consumption patterns exhibited by users. Third, evaluation metrics tend to emphasize a single aspect – most commonly accuracy – while neglecting other dimensions such as novelty. To address these challenges, we propose TIER, a Temporal Information-Enhanced Repeat-aware sequential recommendation model. TIER innovatively incorporates rich temporal signals, including timestamps, time intervals, and viewing durations, and introduces a repeat-explore preference selection module to differentiate between habitual re-watching and novel content discovery behaviors. The architecture combines an attention mechanism tailored for modeling repeat behaviors with a custom-designed convolutional neural network – featuring both vertical and horizontal convolution – to effectively capture exploratory patterns. We evaluate TIER on a large-scale provincial IPTV dataset (more than 12 million interaction logs). The model achieves a 3.29% improvement in MRR@20 and comparable performance on novelty metrics. Comprehensive subgroup analyses, ablation studies, and case examinations further validate the model’s robustness and practical relevance. This work offers a novel perspective on integrating multi-dimensional temporal features into sequential recommendation and contributes a viable solution for balancing predictive accuracy with users’ demand for fresh content.
| [1] |
Anderson A, Kumar R, Tomkins A, Vassilvitskii S. The dynamics of repeat consumption. Proceedings of the 23rd International Conference on World Wide Web, 2014. April 07–11, 2014.
|
| [2] |
Ariannezhad M, Jullien S, Li M, Fang M, Schelter S, De Rijke M. ReCANet: A repeat consumption-aware neural network for next basket recommendation in grocery shopping. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022. July 11–15, 2022.
|
| [3] |
Chang J, Gao C, Zheng Y, Hui Y, Niu Y, Song Y, Jin D, Li Y. Sequential recommendation with graph neural networks. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021. July 11–15, 2021.
|
| [4] |
Chen G, Zhao G, Zhu L, Zhuo Z, Qian X. Combining non-sampling and self-attention for sequential recommendation. Information Processing & Management, 2022, 59(2): 102814.
|
| [5] |
Chen J, Wang C, Wang J. Will you “reconsume” the near past? Fast prediction on short-term reconsumption behaviors. Proceedings of the AAAI Conference on Artificial Intelligence, 2015. January 25–30, 2015.
|
| [6] |
Cho S M, Park E, Yoo S. MEANTIME: Mixture of attention mechanisms with multi-temporal embeddings for sequential recommendation. Fourteenth ACM Conference on Recommender Systems, 2020. September 22–26, 2020.
|
| [7] |
Dallmann A, Grimm A, Pölitz C, Zoller D, Hotho A (2017). Improving session recommendation with recurrent neural networks by exploiting dwell time. arXiv Preprint arXiv: 1706.10231. https://arxiv.org/abs/1706.10231
|
| [8] |
Dang Y, Yang E, Guo G, Jiang L, Wang X, Xu X, Sun Q, Liu H. Uniform sequence better: Time interval aware data augmentation for sequential recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 2023. February 07–14, 2023.
|
| [9] |
Fang H, Zhang D, Shu Y, Guo G. Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations. ACM Transactions on Information Systems, 2020, 39(1): 1-42.
|
| [10] |
Fu Z, Niu X, Maher M L. Deep learning models for serendipity recommendations: A survey and new perspectives. ACM Computing Surveys, 2023, 56(1): 1-26.
|
| [11] |
Gao Y. MOOCs video recommendation using low-rank and sparse matrix factorization with inter-entity relations and intra-entity affinity information. Information Processing & Management, 2024, 61(6): 103861.
|
| [12] |
Gupta A, Smith K G, Shalley C E. The interplay between exploration and exploitation. Academy of Management Journal, 2006, 49(4): 693-706.
|
| [13] |
He Y, Wu G, Cai D, Hu X. Meta-path based graph contrastive learning for micro-video recommendation. Expert Systems with Applications, 2023, 222: 119713.
|
| [14] |
Hidasi B, Karatzoglou A, Baltrunas L, Tikk D (2016). Session-based recommendations with recurrent neural networks. arXiv Preprint arXiv: 1511.06939. https://arxiv.org/abs/1511.06939
|
| [15] |
Kaminskas M, Bridge D. Diversity, serendipity, novelty, and coverage: A survey and empirical analysis of beyond-accuracy objectives in recommender systems. ACM Transactions on Interactive Intelligent Systems, 2017, 7(1): 1-42.
|
| [16] |
Kang W C, McAuley J. Self-attentive sequential recommendation. 2018 IEEE International Conference on Data Mining, 2018. November 17–20, 2018.
|
| [17] |
Li J, Sun A, Ma W, Sun P, Zhang M (2024). Recommender for its purpose: Repeat and exploration in food delivery recommendations. arXiv Preprint arXiv: 2402.14440. https://arxiv.org/abs/2402.14440
|
| [18] |
Li J, Wang Y, McAuley J. Time interval aware self-attention for sequential recommendation. Proceedings of the 13th International Conference on Web Search and Data Mining, 2020. February 03–07, 2020.
|
| [19] |
Li M, Vardasbi A, Yates A, de Rijke M. Repetition and exploration in sequential recommendation. Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023. July 23–27, 2023.
|
| [20] |
Lin J, Pan W, Ming Z. FISSA: Fusing item similarity models with self-attention networks for sequential recommendation. Fourteenth ACM Conference on Recommender Systems, 2020. September 22–26, 2020.
|
| [21] |
Liu B, Li D, Wang J, Wang Z, Li B, Zeng C. Integrating user short-term intentions and long-term preferences in heterogeneous hypergraph networks for sequential recommendation. Information Processing & Management, 2024, 61(3): 103680.
|
| [22] |
Liu W, Wan H, Yan B. Short video recommendation algorithm incorporating temporal contextual information and user context. CMES-Computer Modeling in Engineering & Sciences, 2023, 135(1): 239-258.
|
| [23] |
Lv F, Jin T, Yu C, Sun F, Lin Q, Yang K, Ng W. SDM: Sequential deep matching model for online large-scale recommender system. Proceedings of the 28th ACM International Conference on Information and Knowledge Management, 2019. November 03–07, 2019.
|
| [24] |
Ma J, Bian K, Wen J, Xu Y, Zhong M, Zhu L. SRDPR: Social relation-driven dynamic network for personalized micro-video recommendation. Expert Systems with Applications, 2023, 226: 120157.
|
| [25] |
March J G. Exploration and exploitation in organizational learning. Organization Science, 1991, 2(1): 71-87.
|
| [26] |
Pan G, Hu H, Xu G. Modeling multi-behavior sequence via HyperGRU contrastive network for micro-video recommendation. Knowledge-Based Systems, 2024, 295: 111841.
|
| [27] |
Pan Y, Gao C, Chang J, Niu Y, Song Y, Gai K, Jin D, Li Y. Understanding and modeling passive-negative feedback for short-video sequential recommendation. Proceedings of the 17th ACM Conference on Recommender Systems, 2023. September 18–22, 2023.
|
| [28] |
Peng S, Siet S, Ilkhomjon S, Kim D Y, Park D S. Integration of deep reinforcement learning with collaborative filtering for movie recommendation systems. Applied Sciences, 2024, 14(3): 1155.
|
| [29] |
Qiu Z, Hu Y, Wu X. Graph neural news recommendation with user existing and potential interest modeling. ACM Transactions on Knowledge Discovery from Data, 2022, 16(5): 1-17.
|
| [30] |
Quadrana M, Karatzoglou A, Hidasi B, Cremonesi P. Personalizing session-based recommendations with hierarchical recurrent neural networks. Proceedings of the Eleventh ACM Conference on Recommender Systems, 2017. August 27–31, 2017.
|
| [31] |
Quan S, Liu S, Zheng Z, Wu F. Enhancing repeat-aware recommendation from a temporal-sequential perspective. Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, 2023. October 21–25, 2023.
|
| [32] |
Ren P, Chen Z, Li J, Ren Z, Ma J, de Rijke M. RepeatNet: A repeat aware neural recommendation machine for session-based recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 2019. January 27–February 01, 2019.
|
| [33] |
Shaw P, Uszkoreit J, Vaswani A (2018). Self-attention with relative position representations. arXiv Preprint arXiv: 1803.02155. https://arxiv.org/abs/1803.02155
|
| [34] |
Shrimali H, Saxena R, Kavita. Content-based video recommendation system. 2023 3rd International Conference on Intelligent Communication and Computational Techniques, 2023. February 11–12, 2023.
|
| [35] |
Singh K, Sharma T, Yadav A L, Rani J. YTBlend: YouTube video recommendation system. 2024 International Conference on Computational Intelligence and Computing Applications, 2024. May 24–25, 2024.
|
| [36] |
Tan Y K, Xu X, Liu Y. Improved recurrent neural networks for session-based recommendations. Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, 2016. September 15, 2016.
|
| [37] |
Tang J, Wang K. Personalized top-N sequential recommendation via convolutional sequence embedding. Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 2018. February 05–09, 2018.
|
| [38] |
Tao Z, Wei Y, Wang X, et al.. MGAT: Multimodal graph attention network for recommendation. Information Processing & Management, 2020, 57(5): 102277.
|
| [39] |
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser Ł, Polosukhin I. Attention is all you need. Advances in Neural Information Processing Systems, 2017. December 04–09, 2017.
|
| [40] |
Wang C, Zhang M, Ma W, Liu Y, Ma S. Modeling item-specific temporal dynamics of repeat consumption for recommender systems. Proceedings of the World Wide Web Conference, 2019. May 13–17, 2019.
|
| [41] |
Wang S, Hu L, Wang Y, Cao L, Sheng Q Z, Orgun M. Sequential recommender systems: Challenges, progress and prospects. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019. August 10–16, 2019.
|
| [42] |
Wang X, He X, Wang M, Feng F, Chua T S. Neural graph collaborative filtering. Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019. July 21–25, 2019.
|
| [43] |
Wang Y, Chu C, Li K. Research on game recommendation algorithms based on hybrid models. Proceedings of the 3rd International Conference on Computer, Artificial Intelligence and Control Engineering, 2024. May 24–26, 2024.
|
| [44] |
Wasilewski J, Hurley N. Bayesian personalized ranking for novelty enhancement. Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization, 2019. June 09–12, 2019.
|
| [45] |
Wu L, Li S, Hsieh C J, Sharpnack J. SSE-PT: Sequential recommendation via personalized transformer. Fourteenth ACM Conference on Recommender Systems, 2020. September 22–26, 2020.
|
| [46] |
Wu S, Tang Y, Zhu Y, Wang L, Xie X, Tan T. Session-based recommendation with graph neural networks. Proceedings of the AAAI Conference on Artificial Intelligence, 2019. January 27–February 01, 2019.
|
| [47] |
Xu Y, Lai K T, Xiong P, Wu Z. Multi-preview recommendation via reinforcement learning. 18th ACM Conference on Recommender Systems, 2024. October 14–18, 2024.
|
| [48] |
Xu Y, Yang Y, Wang E, Han J, Zhuang F, Yu Z, Xiong H. Neural serendipity recommendation: Exploring the balance between accuracy and novelty with sparse explicit feedback. ACM Transactions on Knowledge Discovery from Data, 2020, 14(4): 1-25.
|
| [49] |
Yang Y, Huang C, Xia L, Huang C, Luo D, Lin K. Debiased contrastive learning for sequential recommendation. WWW’23: Proceedings of the ACM Web Conference 2023, 2023. April 30–May 04, 2023.
|
| [50] |
Ye W, Wang S, Chen X, Wang X, Qin Z, Yin D. Time matters: Sequential recommendation with complex temporal information. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020. July 25–29, 2020.
|
| [51] |
Yi Z, Wang X, Ounis I, Macdonald C. Multi-modal graph contrastive learning for micro-video recommendation. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022. July 11–15, 2022.
|
| [52] |
Yuan F, Karatzoglou A, Arapakis I, Jose J M, He X. A simple convolutional generative network for next item recommendation. Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, 2018. February 11–15, 2018.
|
| [53] |
Zang R, Zuo M, Zhou J, Xue Y, Huang K. Sequence and distance aware transformer for recommendation systems. 2021 IEEE International Conference on Web Services (ICWS), 2021. September 05–10, 2021.
|
| [54] |
Zhang S, Chen L, Wang C, Li S, Xiong H. Temporal graph contrastive learning for sequential recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 2024. February 20–27, 2024.
|
| [55] |
Zhang S, Tay Y, Yao L, Sun A (2018). Next item recommendation with self-attention. arXiv Preprint arXiv: 1808.06414. https://arxiv.org/abs/1808.06414
|
| [56] |
Zhang Y, Wu Y, Le R, Zhu Y, Zhuang F, Han R, Li X, Lin W, An Z, Xu Y. Modeling dual period-varying preferences for takeaway recommendation. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023. August 06–10, 2023.
|
| [57] |
Zhao H, Cai G, Zhu J, Dong Z, Xu J, Wen J R. Counteracting duration bias in video recommendation via counterfactual watch time. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2024. August 25–29, 2024.
|
| [58] |
Zhao H, Zhang L, Xu J, Cai G, Dong Z, Wen J R. Uncovering user interest from biased and noised watch time in video recommendation. Proceedings of the 17th ACM Conference on Recommender Systems, 2023. September 18–22, 2023.
|
| [59] |
Zhu T, Sun L, Chen G. Graph-based embedding smoothing for sequential recommendation. IEEE Transactions on Knowledge and Data Engineering, 2021, 35(1): 496-508
|
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