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A Study of Priors for Relevance-Based Language Modelling of Recommender Systems

Authors:

Daniel Valcarce,

Javier Parapar,

Alvaro BarreiroAuthors Info & Claims

RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems

Pages 237 - 240

https://doi.org/10.1145/2792838.2799677

Published: 16 September 2015 Publication History

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Abstract

Probabilistic modelling of recommender systems naturally introduces the concept of prior probability into the recommendation task. Relevance-Based Language Models, a principled probabilistic query expansion technique in Information Retrieval, has been recently adapted to the item recommendation task with success. In this paper, we study the effect of the item and user prior probabilities under that framework. We adapt two priors from the document retrieval field and then we propose other two new probabilistic priors. Evidence gathered from experimentation indicates that a linear prior for the neighbour and a probabilistic prior based on Dirichlet smoothing for the items improve the quality of the item recommendation ranking.

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Cited By

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Valcarce D(2021)Information retrieval models for recommender systemsACM SIGIR Forum10.1145/3458537.345854553:1(44-45)Online publication date: 23-Mar-2021
https://dl.acm.org/doi/10.1145/3458537.3458545
Liu WWang ZYao BNie MWang JMao RYin J(2018)Geographical Relevance Model for Long Tail Point-of-Interest RecommendationDatabase Systems for Advanced Applications10.1007/978-3-319-91452-7_5(67-82)Online publication date: 13-May-2018
https://doi.org/10.1007/978-3-319-91452-7_5
Valcarce DParapar JBarreiro ÁKando NSakai TJoho HLi Hde Vries AWhite R(2017)Combining Top-N Recommenders with Metasearch AlgorithmsProceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3077136.3080647(805-808)Online publication date: 7-Aug-2017
https://dl.acm.org/doi/10.1145/3077136.3080647
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Index Terms

A Study of Priors for Relevance-Based Language Modelling of Recommender Systems
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

RecSys '15: Proceedings of the 9th ACM Conference on Recommender Systems

September 2015

414 pages

ISBN:9781450336925

DOI:10.1145/2792838

General Chairs:
Hannes Werthner
TU Wien, Austria
,
Markus Zanker
Alpen-Adria-Universität Klagenfurt, Austria
,
Program Chairs:
Jennifer Golbeck
University of Maryland, USA
,
Giovanni Semeraro
University of Bari Aldo Moro, Italy

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 the author(s) 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 September 2015

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Short-paper

Funding Sources

Xunta de Galicia
Ministerio de Economía y Competitividad

Conference

RecSys '15

Sponsor:

SIGCHI

RecSys '15: Ninth ACM Conference on Recommender Systems

September 16 - 20, 2015

Vienna, Austria

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RecSys '15 Paper Acceptance Rate 28 of 131 submissions, 21%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

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Valcarce D(2021)Information retrieval models for recommender systemsACM SIGIR Forum10.1145/3458537.345854553:1(44-45)Online publication date: 23-Mar-2021
https://dl.acm.org/doi/10.1145/3458537.3458545
Liu WWang ZYao BNie MWang JMao RYin J(2018)Geographical Relevance Model for Long Tail Point-of-Interest RecommendationDatabase Systems for Advanced Applications10.1007/978-3-319-91452-7_5(67-82)Online publication date: 13-May-2018
https://doi.org/10.1007/978-3-319-91452-7_5
Valcarce DParapar JBarreiro ÁKando NSakai TJoho HLi Hde Vries AWhite R(2017)Combining Top-N Recommenders with Metasearch AlgorithmsProceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3077136.3080647(805-808)Online publication date: 7-Aug-2017
https://dl.acm.org/doi/10.1145/3077136.3080647
Valcarce DParapar JBarreiro Á(2016)Additive Smoothing for Relevance-Based Language Modelling of Recommender SystemsProceedings of the 4th Spanish Conference on Information Retrieval10.1145/2934732.2934737(1-8)Online publication date: 14-Jun-2016
https://dl.acm.org/doi/10.1145/2934732.2934737
Valcarce DParapar JBarreiro Á(2016)Item-based relevance modelling of recommendations for getting rid of long tail productsKnowledge-Based Systems10.1016/j.knosys.2016.03.021103:C(41-51)Online publication date: 1-Jul-2016
https://dl.acm.org/doi/10.1016/j.knosys.2016.03.021
Valcarce DParapar JBarreiro Á(2016)Language Models for Collaborative Filtering NeighbourhoodsAdvances in Information Retrieval10.1007/978-3-319-30671-1_45(614-625)Online publication date: 2016
https://doi.org/10.1007/978-3-319-30671-1_45

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