DOCTORAL ABSTRACT

Information Retrieval Models
for Recommender Systems
Daniel Valcarce
Information Retrieval Lab
University of A Coruña, Spaindaniel.valcarce@udc.esAbstract
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, bothfields help users cope with information overload by providing them with relevant items ofinformation.
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 theapplicability 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 therecently formulated user-item group formation problem [3] and propose a novel task basedon the liquidation of long tail items [8]. Third, we exploit ad hoc retrieval models to computeneighborhoods 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 arecompetitive. Finally, we also perform a theoretical analysis of some models to explain theireffectiveness.
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. InRecSys ’15, pages 375–378, 2015.

ACM SIGIR Forum44Vol. 53 No. 1 June 2019[2] Daniel Valcarce, Alejandro Bellogı́n, 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 andMedia, 8:17–31, 2018.
[4] Daniel Valcarce, Javier Parapar, and Álvaro Barreiro. A Study of Priors for RelevanceBased 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 Modellingof 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, Fuzzinessand 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 goodneighbourhoods 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.

ACM SIGIR Forum45

Vol. 53 No. 1 June 2019