publication . Preprint . 2014

Predicting User Engagement in Twitter with Collaborative Ranking

Diaz-Aviles, Ernesto; Lam, Hoang Thanh; Pinelli, Fabio; Braghin, Stefano; Gkoufas, Yiannis; Berlingerio, Michele; Calabrese, Francesco;
Open Access English
  • Published: 26 Dec 2014
Abstract
Collaborative Filtering (CF) is a core component of popular web-based services such as Amazon, YouTube, Netflix, and Twitter. Most applications use CF to recommend a small set of items to the user. For instance, YouTube presents to a user a list of top-n videos she would likely watch next based on her rating and viewing history. Current methods of CF evaluation have been focused on assessing the quality of a predicted rating or the ranking performance for top-n recommended items. However, restricting the recommender system evaluation to these two aspects is rather limiting and neglects other dimensions that could better characterize a well-perceived recommendati...
Subjects
free text keywords: I.2.6, Computer Science - Computers and Society, H.3.3, Computer Science - Information Retrieval, Computer Science - Learning
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28 references, page 1 of 2

[1] F. Abel, E. Diaz-Aviles, N. Henze, D. Krause, and P. Siehndel. Analyzing the blogosphere for predicting the success of music and movie products. 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 0:276-280, 2010.

[2] G. Adomavicius and A. Tuzhilin. Context-aware recommender systems. In F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors, Recommender Systems Handbook, pages 217-253. Springer US, 2011.

[3] R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. Addison Wesley, 2nd edition, 2011.

[4] S. Balakrishnan and S. Chopra. Collaborative ranking. In Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, WSDM '12, pages 143-152, New York, NY, USA, 2012. ACM.

[5] C. J. Burges. From ranknet to lambdarank to lambdamart: An overview. Technical Report MSR-TR-2010-82, June 2010.

[6] C. J. C. Burges, K. M. Svore, P. N. Bennett, A. Pastusiak, and Q. Wu. Learning to rank using an ensemble of lambda-gradient models. In Chapelle et al. [8], pages 25-35.

[7] O. Chapelle and Y. Chang. Yahoo! learning to rank challenge overview. In Chapelle et al. [8], pages 1-24.

[8] O. Chapelle, Y. Chang, and T.-Y. Liu, editors. Proceedings of the Yahoo! Learning to Rank Challenge, held at ICML 2010, Haifa, Israel, June 25, 2010, volume 14 of JMLR Proceedings. JMLR.org, 2011.

[9] E. Diaz-Aviles, L. Drumond, L. Schmidt-Thieme, and W. Nejdl. Real-time top-n recommendation in social streams. In Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys '12, pages 59-66, New York, NY, USA, 2012. ACM.

[10] E. Diaz-Aviles, M. Georgescu, and W. Nejdl. Swarming to rank for recommender systems. In Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys '12, pages 229-232, New York, NY, USA, 2012. ACM.

[11] E. Diaz-Aviles, A. Stewart, E. Velasco, K. Denecke, and W. Nejdl. Epidemic intelligence for the crowd, by the crowd. 2012.

[12] S. Dooms, T. De Pessemier, and L. Martens. Movietweetings: a movie rating dataset collected from twitter. In Workshop on Crowdsourcing and Human Computation for Recommender Systems, CrowdRec at RecSys 2013, 2013.

[13] J. H. Friedman. Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5):1189-1232, 10 2001.

[14] J. H. Friedman. Stochastic gradient boosting. Comput. Stat. Data Anal., 38(4):367-378, Feb. 2002.

[15] S. Funk. Netflix Update: Try This at Home. http:// sifter.org/~simon/journal/20061211.html, 2006. [Online; accessed 2014-08].

28 references, page 1 of 2
Related research
Abstract
Collaborative Filtering (CF) is a core component of popular web-based services such as Amazon, YouTube, Netflix, and Twitter. Most applications use CF to recommend a small set of items to the user. For instance, YouTube presents to a user a list of top-n videos she would likely watch next based on her rating and viewing history. Current methods of CF evaluation have been focused on assessing the quality of a predicted rating or the ranking performance for top-n recommended items. However, restricting the recommender system evaluation to these two aspects is rather limiting and neglects other dimensions that could better characterize a well-perceived recommendati...
Subjects
free text keywords: I.2.6, Computer Science - Computers and Society, H.3.3, Computer Science - Information Retrieval, Computer Science - Learning
Download from
28 references, page 1 of 2

[1] F. Abel, E. Diaz-Aviles, N. Henze, D. Krause, and P. Siehndel. Analyzing the blogosphere for predicting the success of music and movie products. 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 0:276-280, 2010.

[2] G. Adomavicius and A. Tuzhilin. Context-aware recommender systems. In F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors, Recommender Systems Handbook, pages 217-253. Springer US, 2011.

[3] R. Baeza-Yates and B. Ribeiro-Neto. Modern Information Retrieval. Addison Wesley, 2nd edition, 2011.

[4] S. Balakrishnan and S. Chopra. Collaborative ranking. In Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, WSDM '12, pages 143-152, New York, NY, USA, 2012. ACM.

[5] C. J. Burges. From ranknet to lambdarank to lambdamart: An overview. Technical Report MSR-TR-2010-82, June 2010.

[6] C. J. C. Burges, K. M. Svore, P. N. Bennett, A. Pastusiak, and Q. Wu. Learning to rank using an ensemble of lambda-gradient models. In Chapelle et al. [8], pages 25-35.

[7] O. Chapelle and Y. Chang. Yahoo! learning to rank challenge overview. In Chapelle et al. [8], pages 1-24.

[8] O. Chapelle, Y. Chang, and T.-Y. Liu, editors. Proceedings of the Yahoo! Learning to Rank Challenge, held at ICML 2010, Haifa, Israel, June 25, 2010, volume 14 of JMLR Proceedings. JMLR.org, 2011.

[9] E. Diaz-Aviles, L. Drumond, L. Schmidt-Thieme, and W. Nejdl. Real-time top-n recommendation in social streams. In Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys '12, pages 59-66, New York, NY, USA, 2012. ACM.

[10] E. Diaz-Aviles, M. Georgescu, and W. Nejdl. Swarming to rank for recommender systems. In Proceedings of the Sixth ACM Conference on Recommender Systems, RecSys '12, pages 229-232, New York, NY, USA, 2012. ACM.

[11] E. Diaz-Aviles, A. Stewart, E. Velasco, K. Denecke, and W. Nejdl. Epidemic intelligence for the crowd, by the crowd. 2012.

[12] S. Dooms, T. De Pessemier, and L. Martens. Movietweetings: a movie rating dataset collected from twitter. In Workshop on Crowdsourcing and Human Computation for Recommender Systems, CrowdRec at RecSys 2013, 2013.

[13] J. H. Friedman. Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5):1189-1232, 10 2001.

[14] J. H. Friedman. Stochastic gradient boosting. Comput. Stat. Data Anal., 38(4):367-378, Feb. 2002.

[15] S. Funk. Netflix Update: Try This at Home. http:// sifter.org/~simon/journal/20061211.html, 2006. [Online; accessed 2014-08].

28 references, page 1 of 2
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