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Tutorial: Sequence-Aware Recommender Systems

Published: 13 May 2019 Publication History

Abstract

Recommender systems are widely used in online applications to help users find items of interest and help them deal with information overload. In this tutorial, we discuss the class of sequence-aware recommender systems. Differently from the traditional problem formulation based on a user-item rating matrix, the input to such systems is a sequence of logged user interactions. Likewise, sequence-aware recommender systems implement alternative computational tasks, such as predicting the next items a user will be interested in an ongoing session or creating entire sequences of items to present to the user. We propose a problem formulation, sketch a number of computational tasks, review existing algorithmic approaches, and finally discuss evaluation aspects of sequence-aware recommender systems.

References

[1]
Dietmar Jannach, Paul Resnick, Alexander Tuzhilin, and Markus Zanker. 2016. Recommender Systems - Beyond Matrix Completion. Commun. ACM 59, 11 (2016), 94–102.
[2]
Massimo Quadrana, Paolo Cremonesi, and Dietmar Jannach. 2018. Sequence-Aware Recommender Systems. ACM Comput. Surv. 51, 4 (2018).

Cited By

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  • (2025)An introduction to collaborative filtering through the lens of the Netflix PrizeKnowledge and Information Systems10.1007/s10115-024-02315-zOnline publication date: 10-Jan-2025
  • (2023)Cross-platform sequential recommendation with sharing item-level relevance dataInformation Sciences10.1016/j.ins.2022.11.112621(265-286)Online publication date: Apr-2023
  • (2022)Individualized tourism recommendation based on self-attentionPLOS ONE10.1371/journal.pone.027231917:8(e0272319)Online publication date: 25-Aug-2022
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          cover image ACM Other conferences
          WWW '19: Companion Proceedings of The 2019 World Wide Web Conference
          May 2019
          1331 pages
          ISBN:9781450366755
          DOI:10.1145/3308560
          Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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          • IW3C2: International World Wide Web Conference Committee

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          Published: 13 May 2019

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          Author Tags

          1. Recommender Systems
          2. Sequence-Aware
          3. Session-Based

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          • Research-article
          • Research
          • Refereed limited

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          WWW '19
          WWW '19: The Web Conference
          May 13 - 17, 2019
          San Francisco, USA

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          Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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          Cited By

          View all
          • (2025)An introduction to collaborative filtering through the lens of the Netflix PrizeKnowledge and Information Systems10.1007/s10115-024-02315-zOnline publication date: 10-Jan-2025
          • (2023)Cross-platform sequential recommendation with sharing item-level relevance dataInformation Sciences10.1016/j.ins.2022.11.112621(265-286)Online publication date: Apr-2023
          • (2022)Individualized tourism recommendation based on self-attentionPLOS ONE10.1371/journal.pone.027231917:8(e0272319)Online publication date: 25-Aug-2022
          • (2022)Learning Diversity Attributes in Multi-Session Recommendations2022 IEEE International Conference on Big Data (Big Data)10.1109/BigData55660.2022.10020476(465-474)Online publication date: 17-Dec-2022
          • (2021)MOOCs One-Stop Shop: A Realization of a Unified MOOCs Search EngineIEEE Access10.1109/ACCESS.2021.31308419(160175-160185)Online publication date: 2021
          • (2020)Multi-level Feature Extraction in Time-Weighted Graphical Session-Based RecommendationNeural Information Processing10.1007/978-3-030-63836-8_42(504-515)Online publication date: 18-Nov-2020

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