publication . Preprint . 2018

SQL-Rank: A Listwise Approach to Collaborative Ranking

Wu, Liwei; Hsieh, Cho-Jui; Sharpnack, James;
Open Access English
  • Published: 28 Feb 2018
Abstract
In this paper, we propose a listwise approach for constructing user-specific rankings in recommendation systems in a collaborative fashion. We contrast the listwise approach to previous pointwise and pairwise approaches, which are based on treating either each rating or each pairwise comparison as an independent instance respectively. By extending the work of (Cao et al. 2007), we cast listwise collaborative ranking as maximum likelihood under a permutation model which applies probability mass to permutations based on a low rank latent score matrix. We present a novel algorithm called SQL-Rank, which can accommodate ties and missing data and can run in linear ti...
Subjects
free text keywords: Statistics - Machine Learning, Computer Science - Information Retrieval, Computer Science - Machine Learning
Funded by
NSF| RI: SMALL: Fast Prediction and Model Compression for Large-Scale Machine Learning
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1719097
  • Funding stream: Directorate for Computer & Information Science & Engineering | Division of Information and Intelligent Systems
,
NSF| Point-to-Point Process Models for Spatio-temporal Networks
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1712996
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Mathematical Sciences
Download from
23 references, page 1 of 2

Agarwal, Shivani. Ranking on graph data. In Proceedings of the 23rd international conference on Machine learning, pp. 25-32. ACM, 2006. [OpenAIRE]

Cao, Zhe, Qin, Tao, Liu, Tie-Yan, Tsai, Ming-Feng, and Li, Hang. Learning to rank: from pairwise approach to listwise approach. In Proceedings of the 24th international conference on Machine learning, pp. 129-136. ACM, 2007.

Chapelle, Olivier and Keerthi, S Sathiya. Efficient algorithms for ranking with svms. Information Retrieval, 13 (3):201-215, 2010.

Freund, Yoav, Iyer, Raj, Schapire, Robert E, and Singer, Yoram. An efficient boosting algorithm for combining preferences. Journal of machine learning research, 4 (Nov):933-969, 2003.

Hill, Will, Stead, Larry, Rosenstein, Mark, and Furnas, George. Recommending and evaluating choices in a virtual community of use. In Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 194-201. ACM Press/Addison-Wesley Publishing Co., 1995. [OpenAIRE]

Hsieh, Cho-Jui, Natarajan, Nagarajan, and Dhillon, Inderjit. Pu learning for matrix completion. In International Conference on Machine Learning, pp. 2445-2453, 2015.

Hu, Yifan, Koren, Yehuda, and Volinsky, Chris. Collaborative filtering for implicit feedback datasets. In Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on, pp. 263-272. Ieee, 2008. [OpenAIRE]

Huang, Shanshan, Wang, Shuaiqiang, Liu, Tie-Yan, Ma, Jun, Chen, Zhumin, and Veijalainen, Jari. Listwise collaborative filtering. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 343-352. ACM, 2015.

Joachims, Thorsten. Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 133-142. ACM, 2002. [OpenAIRE]

Koren, Yehuda. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 426-434. ACM, 2008.

Lan, Yanyan, Liu, Tie-Yan, Ma, Zhiming, and Li, Hang. Generalization analysis of listwise learning-to-rank algorithms. In Proceedings of the 26th Annual International Conference on Machine Learning, pp. 577-584. ACM, 2009. [OpenAIRE]

Mikolov, Tomas, Sutskever, Ilya, Chen, Kai, Corrado, Greg S, and Dean, Jeff. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, pp. 3111-3119, 2013.

Mnih, Andriy and Salakhutdinov, Ruslan R. Probabilistic matrix factorization. In Advances in neural information processing systems, pp. 1257-1264, 2008. [OpenAIRE]

Pahikkala, Tapio, Tsivtsivadze, Evgeni, Airola, Antti, Ja¨rvinen, Jouni, and Boberg, Jorma. An efficient algorithm for learning to rank from preference graphs. Machine Learning, 75(1):129-165, 2009. [OpenAIRE]

Park, Dohyung, Neeman, Joe, Zhang, Jin, Sanghavi, Sujay, and Dhillon, Inderjit. Preference completion: Largescale collaborative ranking from pairwise comparisons. In International Conference on Machine Learning, pp. 1907-1916, 2015. [OpenAIRE]

23 references, page 1 of 2
Abstract
In this paper, we propose a listwise approach for constructing user-specific rankings in recommendation systems in a collaborative fashion. We contrast the listwise approach to previous pointwise and pairwise approaches, which are based on treating either each rating or each pairwise comparison as an independent instance respectively. By extending the work of (Cao et al. 2007), we cast listwise collaborative ranking as maximum likelihood under a permutation model which applies probability mass to permutations based on a low rank latent score matrix. We present a novel algorithm called SQL-Rank, which can accommodate ties and missing data and can run in linear ti...
Subjects
free text keywords: Statistics - Machine Learning, Computer Science - Information Retrieval, Computer Science - Machine Learning
Funded by
NSF| RI: SMALL: Fast Prediction and Model Compression for Large-Scale Machine Learning
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1719097
  • Funding stream: Directorate for Computer & Information Science & Engineering | Division of Information and Intelligent Systems
,
NSF| Point-to-Point Process Models for Spatio-temporal Networks
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 1712996
  • Funding stream: Directorate for Mathematical & Physical Sciences | Division of Mathematical Sciences
Download from
23 references, page 1 of 2

Agarwal, Shivani. Ranking on graph data. In Proceedings of the 23rd international conference on Machine learning, pp. 25-32. ACM, 2006. [OpenAIRE]

Cao, Zhe, Qin, Tao, Liu, Tie-Yan, Tsai, Ming-Feng, and Li, Hang. Learning to rank: from pairwise approach to listwise approach. In Proceedings of the 24th international conference on Machine learning, pp. 129-136. ACM, 2007.

Chapelle, Olivier and Keerthi, S Sathiya. Efficient algorithms for ranking with svms. Information Retrieval, 13 (3):201-215, 2010.

Freund, Yoav, Iyer, Raj, Schapire, Robert E, and Singer, Yoram. An efficient boosting algorithm for combining preferences. Journal of machine learning research, 4 (Nov):933-969, 2003.

Hill, Will, Stead, Larry, Rosenstein, Mark, and Furnas, George. Recommending and evaluating choices in a virtual community of use. In Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 194-201. ACM Press/Addison-Wesley Publishing Co., 1995. [OpenAIRE]

Hsieh, Cho-Jui, Natarajan, Nagarajan, and Dhillon, Inderjit. Pu learning for matrix completion. In International Conference on Machine Learning, pp. 2445-2453, 2015.

Hu, Yifan, Koren, Yehuda, and Volinsky, Chris. Collaborative filtering for implicit feedback datasets. In Data Mining, 2008. ICDM'08. Eighth IEEE International Conference on, pp. 263-272. Ieee, 2008. [OpenAIRE]

Huang, Shanshan, Wang, Shuaiqiang, Liu, Tie-Yan, Ma, Jun, Chen, Zhumin, and Veijalainen, Jari. Listwise collaborative filtering. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 343-352. ACM, 2015.

Joachims, Thorsten. Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 133-142. ACM, 2002. [OpenAIRE]

Koren, Yehuda. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 426-434. ACM, 2008.

Lan, Yanyan, Liu, Tie-Yan, Ma, Zhiming, and Li, Hang. Generalization analysis of listwise learning-to-rank algorithms. In Proceedings of the 26th Annual International Conference on Machine Learning, pp. 577-584. ACM, 2009. [OpenAIRE]

Mikolov, Tomas, Sutskever, Ilya, Chen, Kai, Corrado, Greg S, and Dean, Jeff. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, pp. 3111-3119, 2013.

Mnih, Andriy and Salakhutdinov, Ruslan R. Probabilistic matrix factorization. In Advances in neural information processing systems, pp. 1257-1264, 2008. [OpenAIRE]

Pahikkala, Tapio, Tsivtsivadze, Evgeni, Airola, Antti, Ja¨rvinen, Jouni, and Boberg, Jorma. An efficient algorithm for learning to rank from preference graphs. Machine Learning, 75(1):129-165, 2009. [OpenAIRE]

Park, Dohyung, Neeman, Joe, Zhang, Jin, Sanghavi, Sujay, and Dhillon, Inderjit. Preference completion: Largescale collaborative ranking from pairwise comparisons. In International Conference on Machine Learning, pp. 1907-1916, 2015. [OpenAIRE]

23 references, page 1 of 2
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