Sequential Recommender Systems: Challenges, Progress and Prospects
Shoujin Wang,Liang Hu,Liang Hu,Yan Wang,Longbing Cao,Quan Z. Sheng,Mehmet A. Orgun +6 more
- pp 6332-6338
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TLDR
The emerging topic of sequential recommender systems (SRSs) has attracted increasing attention in recent years as discussed by the authors, which involve the above aspects for more precise characterization of user contexts, intent and goals, and item consumption trend, leading to more accurate, customized and dynamic recommendations.Abstract:
The emerging topic of sequential recommender systems has attracted increasing attention in recent years.Different from the conventional recommender systems including collaborative filtering and content-based filtering, SRSs try to understand and model the sequential user behaviors, the interactions between users and items, and the evolution of users preferences and item popularity over time. SRSs involve the above aspects for more precise characterization of user contexts, intent and goals, and item consumption trend, leading to more accurate, customized and dynamic recommendations.In this paper, we provide a systematic review on SRSs.We first present the characteristics of SRSs, and then summarize and categorize the key challenges in this research area, followed by the corresponding research progress consisting of the most recent and representative developments on this topic.Finally, we discuss the important research directions in this vibrant area.read more
Citations
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Proceedings ArticleDOI
Global Context Enhanced Graph Neural Networks for Session-based Recommendation
TL;DR: A novel approach to exploit item transitions over all sessions in a more subtle manner for better inferring the user preference of the current session, called GCE-GNN, which outperforms the state-of-the-art methods consistently.
Posted Content
A Survey on Session-based Recommender Systems
TL;DR: A systematic and comprehensive review on SBRS is provided and a hierarchical framework is created to categorize the related research issues and methods of SBRS and to reveal its intrinsic challenges and complexities.
Proceedings ArticleDOI
Global Context Enhanced Graph Neural Networks for Session-based Recommendation
TL;DR: Zhang et al. as discussed by the authors proposed a Global Context Enhanced Graph Neural Networks (GCE-GNN) to exploit item transitions over all sessions in a more subtle manner for better inferring the user preference of the current session.
Proceedings ArticleDOI
Contrastive Learning for Sequential Recommendation
TL;DR: A novel multi-task framework called Contrastive Learning for Sequential Recommendation (CL4SRec) is proposed, which not only takes advantage of the traditional next item prediction task but also utilizes the contrastive learning framework to derive self-supervision signals from the original user behavior sequences.
Journal ArticleDOI
Artificial intelligence in recommender systems
Qian Zhang,Jie Lu,Yaochu Jin +2 more
TL;DR: The paper carefully surveys various issues related to recommender systems that use AI, and also reviews the improvements made to these systems through the use of such AI approaches as fuzzy techniques, transfer learning, genetic algorithms, evolutionary algorithms, neural networks and deep learning, and active learning.
References
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Proceedings ArticleDOI
Factorizing personalized Markov chains for next-basket recommendation
TL;DR: This paper introduces an adaption of the Bayesian Personalized Ranking (BPR) framework for sequential basket data and shows that the FPMC model outperforms both the common matrix factorization and the unpersonalized MC model both learned with and without factorization.
Proceedings ArticleDOI
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
Jiaxi Tang,Ke Wang +1 more
TL;DR: A Convolutional Sequence Embedding Recommendation Model »Caser» is proposed, which is to embed a sequence of recent items into an image in the time and latent spaces and learn sequential patterns as local features of the image using convolutional filters.
Journal ArticleDOI
Session-Based Recommendation with Graph Neural Networks
TL;DR: Wang et al. as discussed by the authors proposed Session-based Recommendation with Graph Neural Networks (SR-GNN) to capture complex transitions of items, which are difficult to be revealed by previous conventional sequential methods.
Proceedings Article
Session-based Recommendations with Recurrent Neural Networks
TL;DR: In this article, the authors apply recurrent neural networks (RNN) on a new domain, namely recommender systems, and propose an RNN-based approach for session-based recommendations.
Proceedings ArticleDOI
Recurrent Recommender Networks
TL;DR: Recurrent Recommender Networks (RRN) are proposed that are able to predict future behavioral trajectories by endowing both users and movies with a Long Short-Term Memory (LSTM) autoregressive model that captures dynamics, in addition to a more traditional low-rank factorization.