research-article

Exploring Statistical Language Models for Recommender Systems

Author:

Daniel ValcarceAuthors Info & Claims

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

Pages 375 - 378

https://doi.org/10.1145/2792838.2796547

Published: 16 September 2015 Publication History

Abstract

Even though there exist multiple approaches to build recommendation algorithms, algebraic techniques based on vector and matrix representations are predominant in the field. Notwithstanding the fact that these algebraic Collaborative Filtering methods have been demonstrated to be very effective in the rating prediction task, they do not generally provide good results in the top-N recommendation task. In this research, we return to the roots of recommender systems and we explore the relationship between Information Filtering and Information Retrieval. We think that probabilistic methods taken from the latter field such as statistical Language Models can be a more effective and formal way for generating personalised ranks of recommendations. We compare our improvements against several algebraic and probabilistic state-of-the-art algorithms and pave the way to future and promising research directions.

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Cited By

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
Lin J(2020)Hybrid Translation and Language Model for Micro Learning Material Recommendation2020 IEEE 20th International Conference on Advanced Learning Technologies (ICALT)10.1109/ICALT49669.2020.00121(384-386)Online publication date: Jul-2020
https://doi.org/10.1109/ICALT49669.2020.00121
Sanz-Cruzado JPepa SCastells PTramullas JTrillo-Lado RNogueras-Iso J(2018)Recommending Contacts in Social Networks Using Information Retrieval ModelsProceedings of the 5th Spanish Conference on Information Retrieval10.1145/3230599.3230619(1-8)Online publication date: 26-Jun-2018
https://dl.acm.org/doi/10.1145/3230599.3230619
Show More Cited By

Index Terms

Exploring Statistical Language Models for 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

cover image ACM Conferences

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

Copyright © 2015 ACM.

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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New York, NY, United States

Publication History

Published: 16 September 2015

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Xunta de Galicia
Ministerio de Economía y Competitividad

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RecSys '15

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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

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
Lin J(2020)Hybrid Translation and Language Model for Micro Learning Material Recommendation2020 IEEE 20th International Conference on Advanced Learning Technologies (ICALT)10.1109/ICALT49669.2020.00121(384-386)Online publication date: Jul-2020
https://doi.org/10.1109/ICALT49669.2020.00121
Sanz-Cruzado JPepa SCastells PTramullas JTrillo-Lado RNogueras-Iso J(2018)Recommending Contacts in Social Networks Using Information Retrieval ModelsProceedings of the 5th Spanish Conference on Information Retrieval10.1145/3230599.3230619(1-8)Online publication date: 26-Jun-2018
https://dl.acm.org/doi/10.1145/3230599.3230619
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

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