research-article

Approximate modeling of continuous context in factorization algorithms

Authors:

Balázs Hidasi,

Domonkos TikkAuthors Info & Claims

CARR '14: Proceedings of the 4th Workshop on Context-Awareness in Retrieval and Recommendation

Pages 3 - 9

https://doi.org/10.1145/2601301.2601303

Published: 13 April 2014 Publication History

Abstract

Factorization based algorithms -- such as matrix or tensor factorization -- are widely used in the field of recommender systems. These methods model the relations between the entities of two or more dimensions. The entity based approach is suitable for dimensions such as users, items and several context types, where the domain of the context is nominal. Continuous and ordinal context dimensions are usually discretized and their values are used as nominal entities. While this enables the usage of continuous context in factorization methods, still much information is lost during the process. In this paper we propose two approaches for better modeling of the continuous context dimensions. Fuzzy event modeling tackles the problem through the uncertainty of the value of the observation in the context dimension. Fuzzy context modeling, on the other hand, enables context-states to overlap, thus certain observations are influenced by multiple context-states. Throughout the paper seasonality is used as an example of continuous context. We incorporate the modeling concepts into the iTALS algorithm, without degrading its scalability. The effect of the two approaches on recommendation accuracy is measured on five implicit feedback databases.

References

[1]

Adomavicius, G., and Tuzhilin, A. Context-aware recommender systems. In Recsys'08: ACM Conf. on Recommender Systems (2008), 335--336.

Digital Library

[2]

Batista, M. A cyclic block-tridiagonal solver. Advances in Engineering Software 37, 2 (2006), 69--74.

Digital Library

[3]

Bell, R., and Koren, Y. Scalable collaborative filtering with jointly derived neighborhood interpolation weights. In ICDM'07: IEEE Int. Conf. on Data Mining (2007), 43--52.

Digital Library

[4]

Celma, O. Music Recommendation and Discovery in the Long Tail. Springer, 2010.

Digital Library

[5]

Chen, T., Zhang, W., Lu, Q., Chen, K., Zheng, Z., and Yu, Y. SVDFeature: A toolkit for feature-based collaborative filtering. Journal of Machine Learning Research 13 (2012), 3619--3622.

Digital Library

[6]

Cremonesi, P., and Turrin, R. Analysis of cold-start recommendations in IPTV systems. In Proc. of the 2009 ACM Conference on Recommender Systems (2009).

Digital Library

[7]

Hidasi, B., and Tikk, D. Fast ALS-based tensor factorization for context-aware recommendation from implicit feedback. In Proc. of the ECML-PKDD, Part II, no. 7524 in LNCS. Springer, 2012, 67--82.

Digital Library

[8]

Hidasi, B., and Tikk, D. Context-aware recommendations from implicit data via scalable tensor factorization. ArXiv e-prints (2013).

[9]

Hidasi, B., and Tikk, D. General factorization framework for context-aware recommendations. ArXiv e-prints (Jan. 2014).

[10]

Karatzoglou, A., Amatriain, X., Baltrunas, L., and Oliver, N. Multiverse recommendation: N-dimensional tensor factorization for context-aware collaborative filtering. In Recsys'10: ACM Conf. on Recommender Systems (2010), 79--86.

Digital Library

[11]

Koren, Y., and Bell, R. Advances in collaborative filtering. In Recommender Systems Handbook, F. Ricci et al., Eds. Springer, 2011, 145--186.

[12]

Liu, N. N., Zhao, B. C. M., and Yang, Q. Adapting neighborhood and matrix factorization models for context aware recommendation. In CAMRa'10: Workshop on Context-Aware Movie Recommendation (2010), 7--13.

Digital Library

[13]

Lops, P., Gemmis, M., and Semeraro, G. Content-based recommender systems: State of the art and trends. In Recommender Systems Handbook. Springer, 2011, 73--105.

[14]

Pilászy, I., and Tikk, D. Recommending new movies: Even a few ratings are more valuable than metadata. In Recsys'09: ACM Conf. on Recommender Systems (2009), 93--100.

Digital Library

[15]

Rendle, S. Factorization machines with libfm. ACM Transactions on Intelligent Systems and Technology (TIST) 3, 3 (2012), 57.

Digital Library

[16]

Rendle, S., Freudenthaler, C., Gantner, Z., and Schmidt-Thieme, L. BPR: Bayesian personalized ranking from implicit feedback. In UAI '09: 25^th Conf. on Uncertainty in Artificial Intelligence (2009), 452--461.

Digital Library

[17]

Rendle, S., and Schmidt-Thieme, L. Pairwise interaction tensor factorization for personalized tag recommendation. In WSDM'10: ACM Int. Conf. on Web Search and Data Mining (2010), 81--90.

Digital Library

[18]

Salakhutdinov, R., and Mnih, A. Probabilistic matrix factorization. In Advances in Neural Information Processing Systems 20. MIT Press, 2008.

Digital Library

[19]

Su, X., and Khoshgoftaar, T. M. A survey of collaborative filtering techniques. Advances in Artificial Intelligence (2009), Article ID 421425 (1--19).

Digital Library

[20]

Takács, G., Pilászy, I., Németh, B., and Tikk, D. Major components of the Gravity recommendation system. SIGKDD Explor. Newsl. 9 (December 2007), 80--83.

Digital Library

Cited By

Yue SHong XSmith R(2024)CSM-H-R: A Context Modeling Framework in Supporting Reasoning Automation for Interoperable Intelligent Systems and Privacy ProtectionIEEE Access10.1109/ACCESS.2024.344627412(115672-115686)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3446274
Pagano RCremonesi PLarson MHidasi BTikk DKaratzoglou AQuadrana MSen SGeyer WFreyne JCastells P(2016)The Contextual TurnProceedings of the 10th ACM Conference on Recommender Systems10.1145/2959100.2959136(249-252)Online publication date: 7-Sep-2016
https://dl.acm.org/doi/10.1145/2959100.2959136
Albakour MMacdonald COunis IClarke CBicer V(2014)Report on the 1st International Workshop on Information Access in Smart Cities (i-ASC 2014)ACM SIGIR Forum10.1145/2701583.270159748:2(96-104)Online publication date: 23-Dec-2014
https://dl.acm.org/doi/10.1145/2701583.2701597
Show More Cited By

Index Terms

Approximate modeling of continuous context in factorization algorithms
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
    2. Machine learning approaches
      1. Markov decision processes
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Markov decision processes

Recommendations

Context-aware item-to-item recommendation within the factorization framework
CaRR '13: Proceedings of the 3rd Workshop on Context-awareness in Retrieval and Recommendation

Item-to-item recommendation -- when the most similar items sought to the actual item -- is an important recommendation scenario in practical recommender systems. One way to solve this task is to use the similarity between item feature vectors of ...
Distributional semantic pre-filtering in context-aware recommender systems

Context-aware recommender systems improve context-free recommenders by exploiting the knowledge of the contextual situation under which a user experienced and rated an item. They use data sets of contextually-tagged ratings to predict how the target ...
General factorization framework for context-aware recommendations

Context-aware recommendation algorithms focus on refining recommendations by considering additional information, available to the system. This topic has gained a lot of attention recently. Among others, several factorization methods were proposed to ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

CARR '14: Proceedings of the 4th Workshop on Context-Awareness in Retrieval and Recommendation

April 2014

34 pages

ISBN:9781450327237

DOI:10.1145/2601301

Conference Chairs:
Alan Said
CWI
,
Ernesto W. De Luca
University of Applied Sciences Potsdam
,
Daniele Quercia
Yahoo! Labs
,
Matthias Böhmer
DFKI

Copyright © 2014 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]

Sponsors

DFKI: DFKI GmbH
University of Potsdam: University of Potsdam
Yahoo! Research
CWI: Centrum voor Wiskunde en Informatica - Netherlands

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 April 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Seventh Framework Programme

Conference

CaRR '14

Sponsor:

DFKI
University of Potsdam
CWI

CaRR '14: 4th Workshop on Context-awareness in Retrieval and Recommendation

April 13, 2014

Amsterdam, The Netherlands

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

13
Total Citations
View Citations
138
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)1

Reflects downloads up to 05 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Yue SHong XSmith R(2024)CSM-H-R: A Context Modeling Framework in Supporting Reasoning Automation for Interoperable Intelligent Systems and Privacy ProtectionIEEE Access10.1109/ACCESS.2024.344627412(115672-115686)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3446274
Pagano RCremonesi PLarson MHidasi BTikk DKaratzoglou AQuadrana MSen SGeyer WFreyne JCastells P(2016)The Contextual TurnProceedings of the 10th ACM Conference on Recommender Systems10.1145/2959100.2959136(249-252)Online publication date: 7-Sep-2016
https://dl.acm.org/doi/10.1145/2959100.2959136
Albakour MMacdonald COunis IClarke CBicer V(2014)Report on the 1st International Workshop on Information Access in Smart Cities (i-ASC 2014)ACM SIGIR Forum10.1145/2701583.270159748:2(96-104)Online publication date: 23-Dec-2014
https://dl.acm.org/doi/10.1145/2701583.2701597
Said ADe Luca EQuercia D(2014)Report on the 4th Workshop on Context-awareness in Retrieval and Recommendation (CaRR 2014)ACM SIGIR Forum10.1145/2701583.270159548:2(89-92)Online publication date: 23-Dec-2014
https://dl.acm.org/doi/10.1145/2701583.2701595
Si LYang H(2014)PIR 2014 The First International Workshop on Privacy-Preserving IRACM SIGIR Forum10.1145/2701583.270159348:2(83-88)Online publication date: 23-Dec-2014
https://dl.acm.org/doi/10.1145/2701583.2701593
Goeuriot LKelly LJones GMüller HZobel J(2014)Report on the SIGIR 2014 Workshop on Medical Information Retrieval (MedIR)ACM SIGIR Forum10.1145/2701583.270159248:2(78-82)Online publication date: 23-Dec-2014
https://dl.acm.org/doi/10.1145/2701583.2701592
Carmel DChang MGabrilovich EHsu BWang K(2014)ERD'14ACM SIGIR Forum10.1145/2701583.270159148:2(63-77)Online publication date: 23-Dec-2014
https://dl.acm.org/doi/10.1145/2701583.2701591
Liu TChaudhuri SKim VHuang QMitra NFunkhouser T(2014)Creating consistent scene graphs using a probabilistic grammarACM Transactions on Graphics10.1145/2661229.266124333:6(1-12)Online publication date: 19-Nov-2014
https://dl.acm.org/doi/10.1145/2661229.2661243
Wu SSun WLong PHuang HCohen-Or DGong MDeussen OChen B(2014)Quality-driven poisson-guided autoscanningACM Transactions on Graphics10.1145/2661229.266124233:6(1-12)Online publication date: 19-Nov-2014
https://dl.acm.org/doi/10.1145/2661229.2661242
Chen KLai YWu YMartin RHu S(2014)Automatic semantic modeling of indoor scenes from low-quality RGB-D data using contextual informationACM Transactions on Graphics10.1145/2661229.266123933:6(1-12)Online publication date: 19-Nov-2014
https://dl.acm.org/doi/10.1145/2661229.2661239
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten