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Information retrieval models for recommender systems

Published: 23 March 2021 Publication History

Abstract

Information retrieval addresses the information needs of users by delivering relevant pieces of information but requires users to convey their information needs explicitly. In contrast, recommender systems offer personalized suggestions of items automatically. Ultimately, both fields help users cope with information overload by providing them with relevant items of information.
This thesis aims to explore the connections between information retrieval and recommender systems. Our objective is to devise recommendation models inspired in information retrieval techniques. We begin by borrowing ideas from the information retrieval evaluation literature to analyze evaluation metrics in recommender systems [2]. Second, we study the applicability of pseudo-relevance feedback models to different recommendation tasks [1]. We investigate the conventional top-N recommendation task [5, 4, 6, 7], but we also explore the recently formulated user-item group formation problem [3] and propose a novel task based on the liquidation of long tail items [8]. Third, we exploit ad hoc retrieval models to compute neighborhoods in a collaborative filtering scenario [9, 10, 12]. Fourth, we explore the opposite direction by adapting an effective recommendation framework to pseudo-relevance feedback [13, 11]. Finally, we discuss the results and present our conclusions.
In summary, this doctoral thesis adapts a series of information retrieval models to recommender systems. Our investigation shows that many retrieval models can be accommodated to deal with different recommendation tasks. Moreover, we find that taking the opposite path is also possible. Exhaustive experimentation confirms that the proposed models are competitive. Finally, we also perform a theoretical analysis of some models to explain their effectiveness.
Advisors: Álvaro Barreiro and Javier Parapar.
Committee members: Gabriella Pasi, Pablo Castells and Fidel Cacheda.
The dissertation is available at: https://www.dc.fi.udc.es/~dvalcarce/thesis.pdf.

References

[1]
Daniel Valcarce. Exploring Statistical Language Models for Recommender Systems. In RecSys '15, pages 375--378, 2015.
[2]
Daniel Valcarce, Alejandro Bellogín, Javier Parapar, and Pablo Castells. On the robustness and discriminative power of information retrieval metrics for top-N recommendation. In RecSys '18, pages 260--268, 2018.
[3]
Daniel Valcarce, Igo Brilhante, Jose Antonio Macedo, Franco Maria Nardini, Raffaele Perego, and Chiara Renso. Item-driven group formation. Online Social Networks and Media, 8:17--31, 2018.
[4]
Daniel Valcarce, Javier Parapar, and Álvaro Barreiro. A Study of Priors for Relevance-Based Language Modelling of Recommender Systems. In RecSys '15, pages 237--240, 2015.
[5]
Daniel Valcarce, Javier Parapar, and Álvaro Barreiro. A Study of Smoothing Methods for Relevance-Based Language Modelling of Recommender Systems. In ECIR '15, pages 346--351, 2015.
[6]
Daniel Valcarce, Javier Parapar, and Álvaro Barreiro. Additive Smoothing for Relevance-Based Language Modelling of Recommender Systems. In CERI '16, pages 1--8, 2016.
[7]
Daniel Valcarce, Javier Parapar, and Álvaro Barreiro. Efficient Pseudo-Relevance Feedback Methods for Collaborative Filtering Recommendation. In ECIR '16, pages 602--613, 2016.
[8]
Daniel Valcarce, Javier Parapar, and Álvaro Barreiro. Item-based Relevance Modelling of Recommendations for Getting Rid of Long Tail Products. Knowledge-Based Systems, 103:41--51, 2016.
[9]
Daniel Valcarce, Javier Parapar, and Álvaro Barreiro. Language Models for Collaborative Filtering Neighbourhoods. In ECIR '16, pages 614--625, 2016.
[10]
Daniel Valcarce, Javier Parapar, and Álvaro Barreiro. Axiomatic Analysis of Language Modelling of Recommender Systems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 25(Suppl. 2):113--127, 2017.
[11]
Daniel Valcarce, Javier Parapar, and Álvaro Barreiro. Document-based and Term-based Linear Methods for Pseudo-Relevance Feedback. Applied Computing Review, 18(4):5--17, 2018.
[12]
Daniel Valcarce, Javier Parapar, and Álvaro Barreiro. Finding and analysing good neighbourhoods to improve collaborative filtering. Knowledge-Based Systems, 159:193--202, 2018.
[13]
Daniel Valcarce, Javier Parapar, and Álvaro Barreiro. LiMe: Linear Methods for Pseudo-Relevance Feedback. In SAC '18, pages 678--687, 2018.
  1. Information retrieval models for recommender systems

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    cover image ACM SIGIR Forum
    ACM SIGIR Forum  Volume 53, Issue 1
    June 2019
    43 pages
    ISSN:0163-5840
    DOI:10.1145/3458537
    Issue’s Table of Contents
    Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 23 March 2021
    Published in SIGIR Volume 53, Issue 1

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