research-article

Putting things in context: Challenge on Context-Aware Movie Recommendation

Authors:

Shlomo Berkovsky,

Ernesto W. De LucaAuthors Info & Claims

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

Pages 2 - 6

https://doi.org/10.1145/1869652.1869665

Published: 30 September 2010 Publication History

Abstract

The Challenge on Context-Aware Movie Recommendation (CAMRa) was conducted as part of a join event on Context-Awareness in Recommender Systems at the 2010 ACM Recommender Systems conference. The challenge focused on three context-aware recommendation tasks: time-based, mood-based, and social recommendation. The participants were provided with anonymized datasets from two real world online movie recommendation communities and competed against each other for obtaining the highest recommendation accuracy for each task. The datasets contained contextual features, such as mood, plot annotation, social network, and comments, normally not available in movie recommendation datasets. Over 40 teams from 20 countries participated in the challenge. Their participation was summarized by 10 papers accepted to the CAMRa workshop.

References

[1]

}}Gediminas Adomavicius, Ramesh Sankaranarayanan, Shahana Sen, and Alexander Tuzhilin, 'Incorporating contextual information in recommender systems using a multidimensional approach', ACM Trans. Inf. Syst., 23(1), 103--145, (2005).

Digital Library

[2]

}}Sarabjot S. Anand and Bamshad Mobasher, 'Contextual recommendation', in WebMine, pp. 142--160, (2006).

Digital Library

[3]

}}Shlomo Berkovsky, Tsvi Kuflik, Lora Aroyo, Dominik Heckmann, Alexander Kröner, Francesco Ricci, and Geert-Jan Houben, 'Predicting user experiences through cross-context reasoning', in LWA, pp. 27--31, (2006).

[4]

}}Antoine Brenner, Bruno Pradel, Nicolas Usunier, and Patrick Gallinari, 'Predicting most rated items in weekly recommendation with temporal regression', in Proc. of CAMRa'10. ACM, (2010).

Digital Library

[5]

}}Pedro G. Campos, Alejandro Bellogín, Fernando Díez, and J. Enrique Chavarriaga, 'Simple time-biased knn-based recommendations', in Proc. of CAMRa'10. ACM, (2010).

Digital Library

[6]

}}Fernando Díez, Enrique Chavarriaga, Pedro G. Campos, and Alejandro Bellogín, 'Movie recommendations based in explicit and implicit features extracted from the filmtipset dataset', in Proc. of CAMRa'10. ACM, (2010).

Digital Library

[7]

}}Zeno Gantner, Steffen Rendle, and Lars Schmidt-Thieme, 'Factorization models for context-/time-aware movie recommendations', in Proc. of CAMRa'10. ACM, (2010).

Digital Library

[8]

}}Jonathan L. Herlocker, Joseph A. Konstan, Loren G. Terveen, and John Riedl, 'Evaluating collaborative filtering recommender systems', ACM Trans. Inf. Syst., 22(1), 5--53, (2004).

Digital Library

[9]

}}Bin Liu and Zheng Yuan, 'Incorporating social networks and user opinions for collaborative recommendation: Modified cf and local trust network based methods', in Proc. of CAMRa'10. ACM, (2010).

Digital Library

[10]

}}Nathan N. Liu, Bin Cao, Min Zhao, and Qiang Yang, 'Adapting neighborhood and matrix factorization models for context aware recommendation', in Proc. of CAMRa'10. ACM, (2010).

Digital Library

[11]

}}Hossein Rahmani, Beau Piccart, Daan Fierens, and Hendrik Blockeel, 'Two complementary approaches to context aware movie recommendation', in Proc. of CAMRa'10. ACM, (2010).

Digital Library

[12]

}}Alan Said, Ernesto W. De Luca, and Sahin Albayrak, 'How social relationships affect user similarities', in SRS Workshop, (2010).

[13]

}}Yue Shi, Martha Larson, and Alan Hanjalic, 'Mining mood-specific movie similarity with matrix factorization for context-aware recommendation', in Proc. of CAMRa'10. ACM, (2010).

Digital Library

[14]

}}Licai Wang, Xiangwu Meng, Yujie Zhang, and Yancui Shi, 'A new approach to mood-based hybrid collaborative filtering', in Proc. of CAMRa'10. ACM, (2010).

Digital Library

[15]

}}Pei Wu, Weiping Liu, and Cihang Jin, 'A novel recommender system fusing the opinions from experts and ordinary people', in Proc. of CAMRa'10. ACM, (2010).

Digital Library

Cited By

Wang DZeng QChawla NJiang M(2021)Modeling Complementarity in Behavior Data with Multi-Type Itemset EmbeddingACM Transactions on Intelligent Systems and Technology10.1145/345872412:4(1-25)Online publication date: 28-Jun-2021
https://dl.acm.org/doi/10.1145/3458724
Jin YHtun NTintarev NVerbert KPapadopoulos GSamaras GWeibelzahl SJannach DSantos O(2019)ContextPlayProceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3320435.3320445(294-302)Online publication date: 7-Jun-2019
https://dl.acm.org/doi/10.1145/3320435.3320445
Yan WLi CHuang YYang L(2019)Low-Complexity Decentralized Recommendation System With Similarity ConstraintsIEEE Access10.1109/ACCESS.2019.29464857(146922-146938)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2946485
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Index Terms

Putting things in context: Challenge on Context-Aware Movie Recommendation
1. 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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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

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Published: 30 September 2010

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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

Wang DZeng QChawla NJiang M(2021)Modeling Complementarity in Behavior Data with Multi-Type Itemset EmbeddingACM Transactions on Intelligent Systems and Technology10.1145/345872412:4(1-25)Online publication date: 28-Jun-2021
https://dl.acm.org/doi/10.1145/3458724
Jin YHtun NTintarev NVerbert KPapadopoulos GSamaras GWeibelzahl SJannach DSantos O(2019)ContextPlayProceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization10.1145/3320435.3320445(294-302)Online publication date: 7-Jun-2019
https://dl.acm.org/doi/10.1145/3320435.3320445
Yan WLi CHuang YYang L(2019)Low-Complexity Decentralized Recommendation System With Similarity ConstraintsIEEE Access10.1109/ACCESS.2019.29464857(146922-146938)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2946485
Wang DJiang MZeng QEberhart ZChawla NGuo YFarooq F(2018)Multi-Type Itemset Embedding for Learning Behavior SuccessProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3219819.3219949(2397-2406)Online publication date: 19-Jul-2018
https://dl.acm.org/doi/10.1145/3219819.3219949
Riegler MLarson MSpampinato CHalvorsen PLux MMarkussen JPogorelov KGriwodz CStensland HTimmerer C(2016)Right inflight?Proceedings of the 7th International Conference on Multimedia Systems10.1145/2910017.2910619(1-6)Online publication date: 10-May-2016
https://dl.acm.org/doi/10.1145/2910017.2910619
Bambia MBoughanem MFaiz R(2016)Exploring Current Viewing Context for TV Contents Recommendation2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)10.1109/WI.2016.0046(272-279)Online publication date: Oct-2016
https://doi.org/10.1109/WI.2016.0046
Hidasi BTikk D(2016)Speeding up ALS learning via approximate methods for context-aware recommendationsKnowledge and Information Systems10.1007/s10115-015-0863-247:1(131-155)Online publication date: 1-Apr-2016
https://dl.acm.org/doi/10.1007/s10115-015-0863-2
Aggarwal CAggarwal C(2016)Time- and Location-Sensitive Recommender SystemsRecommender Systems10.1007/978-3-319-29659-3_9(283-308)Online publication date: 29-Mar-2016
https://doi.org/10.1007/978-3-319-29659-3_9
Hayashi TOnai R(2015)Movie Recommendation Using Reviews on the WebTransactions of the Japanese Society for Artificial Intelligence10.1527/tjsai.30.10230:1(102-111)Online publication date: 2015
https://doi.org/10.1527/tjsai.30.102
Xue SLiu S(2015)Algorithm Research of Individualized Travelling Route Recommendation Based on SimilarityMATEC Web of Conferences10.1051/matecconf/2015220101322(01013)Online publication date: 9-Jul-2015
https://doi.org/10.1051/matecconf/20152201013
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