survey

Sequence-Aware Recommender Systems

Authors:

Massimo Quadrana,

Paolo Cremonesi,

Dietmar JannachAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 51, Issue 4

Article No.: 66, Pages 1 - 36

https://doi.org/10.1145/3190616

Published: 06 July 2018 Publication History

Abstract

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.

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Cited By

Kużelewska UCharytanowicz M(2024)Characteristics of the Learning Data of a Session-Based Recommendation System and their Impact on the Performance of the SystemProceedings of the 32nd International Conference on Information Systems Development10.62036/ISD.2024.24Online publication date: 2024
https://doi.org/10.62036/ISD.2024.24
Vuong Nguyen L(2024)Classifications, evaluation metrics, datasets, and domains in recommendation servicesInternational Journal of Hybrid Intelligent Systems10.3233/HIS-24000320:2(85-100)Online publication date: 1-Jan-2024
https://dl.acm.org/doi/10.3233/HIS-240003
Guo SLiao XMeng FZhao QTang YLi HZong Q(2024)FSASA: Sequential recommendation based on fusing session-aware models and self-attention networksComputer Science and Information Systems10.2298/CSIS230522067G21:1(1-20)Online publication date: 2024
https://doi.org/10.2298/CSIS230522067G
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Index Terms

Sequence-Aware Recommender Systems
1. Computing methodologies
  1. Machine learning
    1. Learning settings
      1. Learning from implicit feedback
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Data mining
      1. Collaborative filtering

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 51, Issue 4

July 2019

765 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3236632

Editor:
Sartaj Sahni
Department of Computer and Information Science and Engineering / University of Florida / Gainesville, FL 32611

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Publication History

Published: 06 July 2018

Accepted: 01 February 2018

Revised: 01 February 2018

Received: 01 July 2017

Published in CSUR Volume 51, Issue 4

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