Sequence-Aware Recommender Systems

By Massimo Quadrana, Paolo Cremonesi, Dietmar Jannach. User Modeling, Adaptation, and Personalization, 2018

565 citations recorded by the source corpus.

Recommender systems are one of the most successful applications of data mining and machine-learning technology in practice. Academic research in the field is historically often based on the matrix completion problem formulation, where for each user-item-pair only one interaction (e.g., a rating) is considered. In many application domains, however, multiple user-item interactions of different types can be recorded over time. And, a number of recent works have shown that this information can be used to build richer individual user models and to discover additional behavioral patterns that can be leveraged in the recommendation process. In this work, we review existing works that consider information from such sequentially ordered user-item interaction logs in the recommendation process. Based on this review, we propose a categorization of the corresponding recommendation tasks and goals, summarize existing algorithmic solutions, discuss methodological approaches when benchmarking what we call sequence-aware recommender systems, and outline open challenges in the area.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/6ad092c3dc7c08631a2220ba08b09c680750d504

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Xiang Wang
3. Meng Wang
4. Le Wu
5. Yi Tay
6. Kun Zhang
7. Dietmar Jannach
8. Fei Sun

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Meng Wang
4. Le Wu
5. Kun Zhang
6. Yi Tay
7. Bin Cui
8. Dietmar Jannach

In Management Science and Operations Research

1. Xiangnan He
2. Dietmar Jannach
3. Xiang Wang
4. Meng Wang
5. Malte Ludewig
6. Yan Wang
7. Longbing Cao
8. Quan Z. Sheng

Citations in the corpus

• Reinforcement Learning based Recommender Systems: A Survey (2021, background): “Other related surveys target sequence-aware [41] and session-based [42] recommendation techniques.”
• Evaluation of session-based recommendation algorithms (2018, methodology): “2 Most of the approaches for session-based recommendation proposed in the literature implement some form of sequence learning , see also [Quadrana et al. 2018] for a recent survey on the more general class of sequence-aware recommenders.”
• A Survey on Accuracy-Oriented Neural Recommendation: From Collaborative Filtering to Information-Rich Recommendation (2021, background): “domain recommendation [8], explainable recommendation [11], knowledge graph-enhanced recommendation [12], sequential recommendation [13], [14], and session-based”
• A Survey on Session-based Recommender Systems (2019, background): “Also, it covers both session-based and session-aware RSs discussed in Reference [84].”
• Current challenges and visions in music recommender systems research (2017, unknown): “In many scenarios, adoption of such quality metrics can lead to a trade-o‚ with accuracy which should be balanced by the RS algorithm [145].”
• Explainable Interaction-driven User Modeling over Knowledge Graph for Sequential Recommendation (2019, background): “It cannot be directly applied to sequence-aware recommendation in most cases since data sparsity quickly leads to poor estimates of the transition matrices, and it also faces the challenge of the choice of the order [14].”
• Graph Neural Networks in Recommender Systems: A Survey (2020, background): “Sequential recommendation predicts users’ next preferences based on their most recent activities, which seeks to model sequential patterns among successive items, and generate well-timed recommendations for users [43].”
• Deep Learning Based Recommender System (2017, background): “, sequence-aware recommender [117]) and spatial (e.”
• Are we really making much progress? A worrying analysis of recent neural recommendation approaches (2019, methodology): “Novel methods were proposed for a variety of settings and algorithmic tasks, including top-n recommendation based on long-term preference profiles or for session-based recommendation scenarios [36].”