publication . Preprint . Part of book or chapter of book . 2017

Conformative Filtering for Implicit Feedback Data

Farhan Khawar; Nevin L. Zhang;
Open Access English
  • Published: 06 Apr 2017
Abstract
Implicit feedback is the simplest form of user feedback that can be used for item recommendation. It is easy to collect and is domain independent. However, there is a lack of negative examples. Previous work tackles this problem by assuming that users are not interested or not as much interested in the unconsumed items. Those assumptions are often severely violated since non-consumption can be due to factors like unawareness or lack of resources. Therefore, non-consumption by a user does not always mean disinterest or irrelevance. In this paper, we propose a novel method called Conformative Filtering (CoF) to address the issue. The motivating observation is that...
Subjects
free text keywords: Computer Science - Information Retrieval, Computer Science - Artificial Intelligence, Artificial intelligence, business.industry, business, Machine learning, computer.software_genre, computer, Cluster analysis, Baseline (configuration management), Information retrieval, Computer science, Recommender system, Filter (signal processing)
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http://arxiv.org/pdf/1704.0188...
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22 references, page 1 of 2

[Adomavicius and Kwon, 2012] Gediminas Adomavicius and YoungOk Kwon. Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans. on Knowl. and Data Eng., 24(5):896-911, May 2012.

[Chen et al., 2012] Tao Chen, Nevin L Zhang, Tengfei Liu, Kin Man Poon, and Yi Wang. Model-based multidimensional clustering of categorical data. Artificial Intelligence, 176(1):2246-2269, 2012.

[Chen et al., 2016] Peixian Chen, Nevin L. Zhang, Leonard K. M. Poon, and Zhourong Chen. Progressive em for latent tree models and hierarchical topic detection. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI'16, pages 1498-1504. AAAI Press, 2016.

[Gantner et al., 2011] Zeno Gantner, Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. Mymedialite: A free recommender system library. In Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys '11, pages 305-308, New York, NY, USA, 2011. ACM. [OpenAIRE]

[Goldberg et al., 1992] David Goldberg, David Nichols, Brian M. Oki, and Douglas Terry. Using collaborative filtering to weave an information tapestry. Commun. ACM, 35(12):61-70, December 1992.

[Hoeffding, 1963] Wassily Hoeffding. Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association, 58(301):13-30, March 1963.

[Hu et al., 2008] Yifan Hu, Yehuda Koren, and Chris Volinsky. Collaborative filtering for implicit feedback datasets. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, ICDM '08, pages 263-272, Washington, DC, USA, 2008. IEEE Computer Society.

[Koren and Bell, 2015] Yehuda Koren and Robert Bell. Advances in Collaborative Filtering, pages 77-118. Springer US, Boston, MA, 2015.

[Liu et al., 2014] Tengfei Liu, Nevin L Zhang, and Peixian Chen. Hierarchical latent tree analysis for topic detection. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 256-272. Springer, 2014.

[Liu et al., 2015] Teng-Fei Liu, Nevin L Zhang, Peixian Chen, April Hua Liu, Leonard KM Poon, and Yi Wang. Greedy learning of latent tree models for multidimensional clustering. Machine learning, 98(1- 2):301-330, 2015.

[McAuley et al., 2015] Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. Image-based recommendations on styles and substitutes. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 43-52. ACM, 2015.

[Nichols, 1997] David M. Nichols. Implicit ratings and filtering. In Proceedings of the 5th DELOS Workshop on Filtering and Collaborative Filtering. Budapaest: ERCIM, volume 12, 1997.

[Pan et al., 2008] Rong Pan, Yunhong Zhou, Bin Cao, Nathan N. Liu, Rajan Lukose, Martin Scholz, and Qiang Yang. One-class collaborative filtering. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, ICDM '08, pages 502-511, Washington, DC, USA, 2008. IEEE Computer Society. [OpenAIRE]

[Pearl, 1988] Judea Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1988.

[Rendle et al., 2009] Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars SchmidtThieme. Bpr: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI '09, pages 452-461, Arlington, Virginia, United States, 2009. AUAI Press.

22 references, page 1 of 2
Related research
Abstract
Implicit feedback is the simplest form of user feedback that can be used for item recommendation. It is easy to collect and is domain independent. However, there is a lack of negative examples. Previous work tackles this problem by assuming that users are not interested or not as much interested in the unconsumed items. Those assumptions are often severely violated since non-consumption can be due to factors like unawareness or lack of resources. Therefore, non-consumption by a user does not always mean disinterest or irrelevance. In this paper, we propose a novel method called Conformative Filtering (CoF) to address the issue. The motivating observation is that...
Subjects
free text keywords: Computer Science - Information Retrieval, Computer Science - Artificial Intelligence, Artificial intelligence, business.industry, business, Machine learning, computer.software_genre, computer, Cluster analysis, Baseline (configuration management), Information retrieval, Computer science, Recommender system, Filter (signal processing)
Download fromView all 2 versions
http://arxiv.org/pdf/1704.0188...
Part of book or chapter of book
Provider: UnpayWall
http://link.springer.com/conte...
Part of book or chapter of book
Provider: Crossref
22 references, page 1 of 2

[Adomavicius and Kwon, 2012] Gediminas Adomavicius and YoungOk Kwon. Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans. on Knowl. and Data Eng., 24(5):896-911, May 2012.

[Chen et al., 2012] Tao Chen, Nevin L Zhang, Tengfei Liu, Kin Man Poon, and Yi Wang. Model-based multidimensional clustering of categorical data. Artificial Intelligence, 176(1):2246-2269, 2012.

[Chen et al., 2016] Peixian Chen, Nevin L. Zhang, Leonard K. M. Poon, and Zhourong Chen. Progressive em for latent tree models and hierarchical topic detection. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI'16, pages 1498-1504. AAAI Press, 2016.

[Gantner et al., 2011] Zeno Gantner, Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. Mymedialite: A free recommender system library. In Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys '11, pages 305-308, New York, NY, USA, 2011. ACM. [OpenAIRE]

[Goldberg et al., 1992] David Goldberg, David Nichols, Brian M. Oki, and Douglas Terry. Using collaborative filtering to weave an information tapestry. Commun. ACM, 35(12):61-70, December 1992.

[Hoeffding, 1963] Wassily Hoeffding. Probability inequalities for sums of bounded random variables. Journal of the American Statistical Association, 58(301):13-30, March 1963.

[Hu et al., 2008] Yifan Hu, Yehuda Koren, and Chris Volinsky. Collaborative filtering for implicit feedback datasets. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, ICDM '08, pages 263-272, Washington, DC, USA, 2008. IEEE Computer Society.

[Koren and Bell, 2015] Yehuda Koren and Robert Bell. Advances in Collaborative Filtering, pages 77-118. Springer US, Boston, MA, 2015.

[Liu et al., 2014] Tengfei Liu, Nevin L Zhang, and Peixian Chen. Hierarchical latent tree analysis for topic detection. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 256-272. Springer, 2014.

[Liu et al., 2015] Teng-Fei Liu, Nevin L Zhang, Peixian Chen, April Hua Liu, Leonard KM Poon, and Yi Wang. Greedy learning of latent tree models for multidimensional clustering. Machine learning, 98(1- 2):301-330, 2015.

[McAuley et al., 2015] Julian McAuley, Christopher Targett, Qinfeng Shi, and Anton Van Den Hengel. Image-based recommendations on styles and substitutes. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 43-52. ACM, 2015.

[Nichols, 1997] David M. Nichols. Implicit ratings and filtering. In Proceedings of the 5th DELOS Workshop on Filtering and Collaborative Filtering. Budapaest: ERCIM, volume 12, 1997.

[Pan et al., 2008] Rong Pan, Yunhong Zhou, Bin Cao, Nathan N. Liu, Rajan Lukose, Martin Scholz, and Qiang Yang. One-class collaborative filtering. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, ICDM '08, pages 502-511, Washington, DC, USA, 2008. IEEE Computer Society. [OpenAIRE]

[Pearl, 1988] Judea Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1988.

[Rendle et al., 2009] Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars SchmidtThieme. Bpr: Bayesian personalized ranking from implicit feedback. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI '09, pages 452-461, Arlington, Virginia, United States, 2009. AUAI Press.

22 references, page 1 of 2
Related research
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publication . Preprint . Part of book or chapter of book . 2017

Conformative Filtering for Implicit Feedback Data

Farhan Khawar; Nevin L. Zhang;