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Movie recommendations based in explicit and implicit features extracted from the Filmtipset dataset

Authors:

Fernando Díez,

J. Enrique Chavarriaga,

Pedro G. Campos,

Alejandro BellogínAuthors Info & Claims

CAMRa '10: Proceedings of the Workshop on Context-Aware Movie Recommendation

Pages 45 - 52

https://doi.org/10.1145/1869652.1869660

Published: 30 September 2010 Publication History

Abstract

In this paper, we describe the experiments conducted by the Information Retrieval Group at the Universidad Autónoma de Madrid (Spain) in order to better recommend movies for the 2010 CAMRa Challenge edition. Experiments were carried out on the dataset corresponding to social Filmtipset track. To obtain the movies recommendations we have used different algorithms based on Random Walks, which are well documented in the literature of collaborative recommendation. We have also included a new proposal in one of the algorithms in order to get better results. The results obtained have been computed by means of the trec_eval standard NIST evaluation procedure.

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Cited By

(2019)The wisdom of the fewInternational Journal of Computational Science and Engineering10.5555/3302674.330267718:1(21-28)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.5555/3302674.3302677
Pan WYang QDuan YTan BMing Z(2017)Transfer Learning for Behavior RankingACM Transactions on Intelligent Systems and Technology10.1145/30577328:5(1-23)Online publication date: 30-Jun-2017
https://dl.acm.org/doi/10.1145/3057732
Wu INiu Y(2015)Effects of anchoring process under preference stabilities for interactive movie recommendationsJournal of the Association for Information Science and Technology10.1002/asi.2328066:8(1673-1695)Online publication date: 1-Aug-2015
https://dl.acm.org/doi/10.1002/asi.23280
Show More Cited By

Index Terms

Movie recommendations based in explicit and implicit features extracted from the Filmtipset dataset
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Information

Published In

cover image ACM Other conferences

CAMRa '10: Proceedings of the Workshop on Context-Aware Movie Recommendation

September 2010

66 pages

ISBN:9781450302586

DOI:10.1145/1869652

Conference Chairs:
Shlomo Berkovsky
CSIRO, Australia
,
Ernesto W. De Luca
TU-Berlin, Germany
,
Alan Said
TU-Berlin, Germany

Copyright © 2010 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 ACM 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: 30 September 2010

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Ministerio de Ciencia e Innovación

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CAMRa '10

CAMRa '10: Workshop on Context-aware Movie Recommendation 2010

September 30, 2010

Barcelona, Spain

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Cited By

(2019)The wisdom of the fewInternational Journal of Computational Science and Engineering10.5555/3302674.330267718:1(21-28)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.5555/3302674.3302677
Pan WYang QDuan YTan BMing Z(2017)Transfer Learning for Behavior RankingACM Transactions on Intelligent Systems and Technology10.1145/30577328:5(1-23)Online publication date: 30-Jun-2017
https://dl.acm.org/doi/10.1145/3057732
Wu INiu Y(2015)Effects of anchoring process under preference stabilities for interactive movie recommendationsJournal of the Association for Information Science and Technology10.1002/asi.2328066:8(1673-1695)Online publication date: 1-Aug-2015
https://dl.acm.org/doi/10.1002/asi.23280
Bellogín ACantador IDíez FCastells PChavarriaga E(2013)An empirical comparison of social, collaborative filtering, and hybrid recommendersACM Transactions on Intelligent Systems and Technology10.1145/2414425.24144394:1(1-29)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1145/2414425.2414439
Said ABerkovsky SDe Luca E(2013)Introduction to special section on CAMRa2010ACM Transactions on Intelligent Systems and Technology10.1145/2414425.24144384:1(1-9)Online publication date: 1-Feb-2013
https://dl.acm.org/doi/10.1145/2414425.2414438
Odić ATkalčič MTasič JKošir A(2013)Predicting and Detecting the Relevant Contextual Information in a Movie-Recommender SystemInteracting with Computers10.1093/iwc/iws00325:1(74-90)Online publication date: 3-Jan-2013
https://doi.org/10.1093/iwc/iws003
Lu QYang DChen TZhang WYu YSaid ABerkovsky SDe Luca EHermanns J(2011)Informative household recommendation with feature-based matrix factorizationProceedings of the 2nd Challenge on Context-Aware Movie Recommendation10.1145/2096112.2096116(15-22)Online publication date: 23-Oct-2011
https://dl.acm.org/doi/10.1145/2096112.2096116
Said ABerkovsky SDe Luca EBerkovsky SDe Luca ESaid A(2010)Putting things in contextProceedings of the Workshop on Context-Aware Movie Recommendation10.1145/1869652.1869665(2-6)Online publication date: 30-Sep-2010
https://dl.acm.org/doi/10.1145/1869652.1869665

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