Movie recommender: semantically enriched unified relevance model for rating prediction in collaborative filtering

Yashar Moshfeghi; Deepak Agarwal; Benjamin Piwowarski; Joemon M. Jose

doi:10.1007/978-3-642-00958-7_8

Movie recommender: semantically enriched unified relevance model for rating prediction in collaborative filtering

Yashar Moshfeghi, Deepak Agarwal, Benjamin Piwowarski, Joemon M. Jose

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

12 Citations (Scopus)

Abstract

Collaborative recommender systems aim to recommend items to a user based on the information gathered from other users who have similar interests. The current state-of-the-art systems fail to consider the underlying semantics involved when rating an item. This in turn contributes to many false recommendations. These models hinder the possibility of explaining why a user has a particular interest or why a user likes a particular item. In this paper, we develop an approach incorporating the underlying semantics involved in the rating. Experiments on a movie database show that this improves the accuracy of the model.

Original language	English
Title of host publication	Advances in Information Retrieval
Subtitle of host publication	31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6-9, 2009. Proceedings
Editors	Mohand Boughanem, Catherine Berrut, Josiane Mothe, Chantal Soule-Dupuy
Place of Publication	Berlin
Publisher	Springer
Pages	54-65
Number of pages	12
ISBN (Print)	9783642009570, 9783642009587
DOIs	https://doi.org/10.1007/978-3-642-00958-7_8
Publication status	Published - 27 Mar 2009
Event	European Conference on IR Research - Toulouse, France Duration: 6 Apr 2009 → 9 Apr 2009

Conference

Conference	European Conference on IR Research
Abbreviated title	ECIR 2009
Country/Territory	France
City	Toulouse
Period	6/04/09 → 9/04/09

Keywords

recommender systems
collaborative
movie database
rating prediction
filtering

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10.1007/978-3-642-00958-7_8

Cite this

Moshfeghi, Y., Agarwal, D., Piwowarski, B., & Jose, J. M. (2009). Movie recommender: semantically enriched unified relevance model for rating prediction in collaborative filtering. In M. Boughanem, C. Berrut, J. Mothe, & C. Soule-Dupuy (Eds.), Advances in Information Retrieval: 31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6-9, 2009. Proceedings (pp. 54-65). Springer. https://doi.org/10.1007/978-3-642-00958-7_8

Moshfeghi, Yashar ; Agarwal, Deepak ; Piwowarski, Benjamin et al. / Movie recommender : semantically enriched unified relevance model for rating prediction in collaborative filtering. Advances in Information Retrieval: 31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6-9, 2009. Proceedings. editor / Mohand Boughanem ; Catherine Berrut ; Josiane Mothe ; Chantal Soule-Dupuy. Berlin : Springer, 2009. pp. 54-65

@inproceedings{db1d4a523dde47b5bbe33abe9ea50d0b,

title = "Movie recommender: semantically enriched unified relevance model for rating prediction in collaborative filtering",

abstract = "Collaborative recommender systems aim to recommend items to a user based on the information gathered from other users who have similar interests. The current state-of-the-art systems fail to consider the underlying semantics involved when rating an item. This in turn contributes to many false recommendations. These models hinder the possibility of explaining why a user has a particular interest or why a user likes a particular item. In this paper, we develop an approach incorporating the underlying semantics involved in the rating. Experiments on a movie database show that this improves the accuracy of the model.",

keywords = "recommender systems, collaborative, movie database, rating prediction, filtering",

author = "Yashar Moshfeghi and Deepak Agarwal and Benjamin Piwowarski and Jose, {Joemon M.}",

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note = "European Conference on IR Research, ECIR 2009 ; Conference date: 06-04-2009 Through 09-04-2009",

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Moshfeghi, Y, Agarwal, D, Piwowarski, B & Jose, JM 2009, Movie recommender: semantically enriched unified relevance model for rating prediction in collaborative filtering. in M Boughanem, C Berrut, J Mothe & C Soule-Dupuy (eds), Advances in Information Retrieval: 31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6-9, 2009. Proceedings. Springer, Berlin, pp. 54-65, European Conference on IR Research, Toulouse, France, 6/04/09. https://doi.org/10.1007/978-3-642-00958-7_8

Movie recommender: semantically enriched unified relevance model for rating prediction in collaborative filtering. / Moshfeghi, Yashar; Agarwal, Deepak; Piwowarski, Benjamin et al.
Advances in Information Retrieval: 31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6-9, 2009. Proceedings. ed. / Mohand Boughanem; Catherine Berrut; Josiane Mothe; Chantal Soule-Dupuy. Berlin: Springer, 2009. p. 54-65.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution book

TY - GEN

T1 - Movie recommender

T2 - European Conference on IR Research

AU - Moshfeghi, Yashar

AU - Agarwal, Deepak

AU - Piwowarski, Benjamin

AU - Jose, Joemon M.

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Y1 - 2009/3/27

N2 - Collaborative recommender systems aim to recommend items to a user based on the information gathered from other users who have similar interests. The current state-of-the-art systems fail to consider the underlying semantics involved when rating an item. This in turn contributes to many false recommendations. These models hinder the possibility of explaining why a user has a particular interest or why a user likes a particular item. In this paper, we develop an approach incorporating the underlying semantics involved in the rating. Experiments on a movie database show that this improves the accuracy of the model.

AB - Collaborative recommender systems aim to recommend items to a user based on the information gathered from other users who have similar interests. The current state-of-the-art systems fail to consider the underlying semantics involved when rating an item. This in turn contributes to many false recommendations. These models hinder the possibility of explaining why a user has a particular interest or why a user likes a particular item. In this paper, we develop an approach incorporating the underlying semantics involved in the rating. Experiments on a movie database show that this improves the accuracy of the model.

KW - recommender systems

KW - collaborative

KW - movie database

KW - rating prediction

KW - filtering

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Moshfeghi Y, Agarwal D, Piwowarski B, Jose JM. Movie recommender: semantically enriched unified relevance model for rating prediction in collaborative filtering. In Boughanem M, Berrut C, Mothe J, Soule-Dupuy C, editors, Advances in Information Retrieval: 31th European Conference on IR Research, ECIR 2009, Toulouse, France, April 6-9, 2009. Proceedings. Berlin: Springer. 2009. p. 54-65 doi: 10.1007/978-3-642-00958-7_8

Movie recommender: semantically enriched unified relevance model for rating prediction in collaborative filtering

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