Look into the Future: Deep Contextualized Sequential Recommendation

• URL: http://arxiv.org/abs/2405.14359v2
• Date: Wed, 14 Aug 2024 05:33:07 GMT
• Title: Look into the Future: Deep Contextualized Sequential Recommendation
• Authors: Lei Zheng, Ning Li, Yanhuan Huang, Ruiwen Xu, Weinan Zhang, Yong Yu,
• Abstract summary: We propose a novel framework of sequential recommendation called Look into the Future (LIFT) LIFT builds and leverages the contexts of sequential recommendation. In our experiments, LIFT achieves significant performance improvement on click-through rate prediction and rating prediction tasks.
• Score: 28.726897673576865
• License:
• Abstract: Sequential recommendation aims to estimate how a user's interests evolve over time via uncovering valuable patterns from user behavior history. Many previous sequential models have solely relied on users' historical information to model the evolution of their interests, neglecting the crucial role that future information plays in accurately capturing these dynamics. However, effectively incorporating future information in sequential modeling is non-trivial since it is impossible to make the current-step prediction for any target user by leveraging his future data. In this paper, we propose a novel framework of sequential recommendation called Look into the Future (LIFT), which builds and leverages the contexts of sequential recommendation. In LIFT, the context of a target user's interaction is represented based on i) his own past behaviors and ii) the past and future behaviors of the retrieved similar interactions from other users. As such, the learned context will be more informative and effective in predicting the target user's behaviors in sequential recommendation without temporal data leakage. Furthermore, in order to exploit the intrinsic information embedded within the context itself, we introduce an innovative pretraining methodology incorporating behavior masking. In our extensive experiments on five real-world datasets, LIFT achieves significant performance improvement on click-through rate prediction and rating prediction tasks in sequential recommendation over strong baselines, demonstrating that retrieving and leveraging relevant contexts from the global user pool greatly benefits sequential recommendation. The experiment code is provided at https://anonymous.4open.science/r/LIFT-277C/Readme.md.

Related papers

• Future Sight and Tough Fights: Revolutionizing Sequential Recommendation with FENRec [31.264334651290437]
  Sequential recommendation (SR) systems predict user preferences by analyzing time-ordered interaction sequences. A common challenge for SR is data sparsity, as users typically interact with only a limited number of items. We propose Future data utilization with Enduring Negatives for contrastive learning in sequential Recommendation (FENRec)
  arXiv  Detail & Related papers  (2024-12-16T09:20:29Z)
• Modeling the Heterogeneous Duration of User Interest in Time-Dependent Recommendation: A Hidden Semi-Markov Approach [11.392605386729699]
  We propose a hidden semi-Markov model to track the change of users' interests. This model allows for capturing the different durations of user stays in a (latent) interest state. We derive an algorithm to estimate the parameters and predict users' actions.
  arXiv  Detail & Related papers  (2024-12-15T09:17:45Z)
• USE: Dynamic User Modeling with Stateful Sequence Models [26.74966828348815]
  User Stateful Embedding (USE) generates user embeddings without the need for exhaustive reprocessing. We introduce a novel training objective named future W-behavior prediction to transcend the limitations of next-token prediction. We conduct experiments on 8 downstream tasks using Snapchat users' behavioral logs in both static (i.e., fixed user behavior sequences) and dynamic (i.e. periodically updated user behavior sequences) settings.
  arXiv  Detail & Related papers  (2024-03-20T07:05:19Z)
• Latent User Intent Modeling for Sequential Recommenders [92.66888409973495]
  Sequential recommender models learn to predict the next items a user is likely to interact with based on his/her interaction history on the platform. Most sequential recommenders however lack a higher-level understanding of user intents, which often drive user behaviors online. Intent modeling is thus critical for understanding users and optimizing long-term user experience.
  arXiv  Detail & Related papers  (2022-11-17T19:00:24Z)
• Modeling Dynamic User Preference via Dictionary Learning for Sequential Recommendation [133.8758914874593]
  Capturing the dynamics in user preference is crucial to better predict user future behaviors because user preferences often drift over time. Many existing recommendation algorithms -- including both shallow and deep ones -- often model such dynamics independently. This paper considers the problem of embedding a user's sequential behavior into the latent space of user preferences.
  arXiv  Detail & Related papers  (2022-04-02T03:23:46Z)
• TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations [12.626079984394766]
  We propose a novel sequential recommendation framework based on dynamic user-item heterogeneous graphs. We exploit the conditional random field to aggregate the heterogeneous graphs and user behaviors for probability estimation. We provide scalable and flexible implementations of the proposed framework.
  arXiv  Detail & Related papers  (2021-11-14T15:54:23Z)
• Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation [62.30552176649873]
  We devise a novel Dynamic Representation Learning model for Sequential Recommendation (DRL-SRe) To better model the user-item interactions for characterizing the dynamics from both sides, the proposed model builds a global user-item interaction graph for each time slice. To enable the model to capture fine-grained temporal information, we propose an auxiliary temporal prediction task over consecutive time slices.
  arXiv  Detail & Related papers  (2021-09-24T07:44:27Z)
• Contrastive Self-supervised Sequential Recommendation with Robust Augmentation [101.25762166231904]
  Sequential Recommendationdescribes a set of techniques to model dynamic user behavior in order to predict future interactions in sequential user data. Old and new issues remain, including data-sparsity and noisy data. We propose Contrastive Self-Supervised Learning for sequential Recommendation (CoSeRec)
  arXiv  Detail & Related papers  (2021-08-14T07:15:25Z)
• Dynamic Graph Collaborative Filtering [64.87765663208927]
  Dynamic recommendation is essential for recommender systems to provide real-time predictions based on sequential data. Here we propose Dynamic Graph Collaborative Filtering (DGCF), a novel framework leveraging dynamic graphs to capture collaborative and sequential relations. Our approach achieves higher performance when the dataset contains less action repetition, indicating the effectiveness of integrating dynamic collaborative information.
  arXiv  Detail & Related papers  (2021-01-08T04:16:24Z)
• S^3-Rec: Self-Supervised Learning for Sequential Recommendation with Mutual Information Maximization [104.87483578308526]
  We propose the model S3-Rec, which stands for Self-Supervised learning for Sequential Recommendation. For our task, we devise four auxiliary self-supervised objectives to learn the correlations among attribute, item, subsequence, and sequence. Extensive experiments conducted on six real-world datasets demonstrate the superiority of our proposed method over existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-08-18T11:44:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.