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Alanazi, Sultan; Goulding, James; McAuley, Derek (2016)
Languages: English
Types: Unknown
It is increasingly rare to encounter a Web service that doesn’t engage in some form of automated recommendation, with Collaborative Filtering (CF) techniques being virtually ubiquitous as the means for delivering relevant content. Yet several key issues still remain unresolved, including optimal handling of cold starts and how best to maintain user privacy within that context. Recent work has demonstrated a potentially fruitful line of attack in the form of cross system user modelling, which uses features generated from one domain to bootstrap recommendations in another. In this paper we evidence the effectiveness of this approach through direct real-world user feedback, deconstructing a cross-system news recommendation service where user models are generated via social media data. It is shown that even when a relatively naive vector-space approach is used, it is possible to automatically generate user-models that provide statistically superior performance than when items are explicitly filtered based on a user’s self-declared preferences. Detailed qualitative analysis of why such effects occur indicate that different models are capturing widely different areas within a user’s preference space, and that hybrid models represent fertile ground for future research.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] F. Abel, Q. Gao, G.-J. Houben, and K. Tao. Twitter-based user modeling for news recommendations. In Proceedings of the Twenty-Third international joint conference on Arti cial Intelligence, pages 2962{2966. AAAI Press, 2013.
    • [2] F. Abel, E. Herder, G.-J. Houben, N. Henze, and D. Krause. Cross-system user modeling and personalization on the social web. User Modeling and User-Adapted Interaction, 23(2):169{209, 2013.
    • [3] A. Ahmed, A. Das, and A. J. Smola. Scalable hierarchical multitask learning algorithms for conversion optimization in display advertising. In Proceedings of the 7th ACM International Conference on Web Search and Data Mining, WSDM '14, pages 153{162, New York, NY, USA, 2014. ACM.
    • [4] A. Aizawa. An information-theoretic perspective of tf{idf measures. Information Processing & Management, 39(1):45{65, 2003.
    • [5] R. M. Bell and Y. Koren. Improved neighborhood-based collaborative ltering. In KDD-Cup and Workshop, pages 7{14. ACM press, 2007.
    • [6] G. Costa and R. Ortale. Xml document co-clustering via non-negative matrix tri-factorization. In Tools with Arti cial Intelligence (ICTAI), 2014 IEEE 26th International Conference on, pages 607{614, Nov 2014.
    • [7] P. Cremonesi and M. Quadrana. Cross-domain recommendations without overlapping data: myth or reality? In Proceedings of the 8th ACM Conference on Recommender systems, pages 297{300. ACM, 2014.
    • [8] A. M. Elkahky, Y. Song, and X. He. A multi-view deep learning approach for cross domain user modeling in recommendation systems. In Proceedings of the 24th International Conference on World Wide Web, WWW '15, pages 278{288, Republic and Canton of Geneva, Switzerland, 2015. International World Wide Web Conferences Steering Committee.
    • [9] L. Hu, J. Cao, G. Xu, L. Cao, Z. Gu, and C. Zhu. Personalized recommendation via cross-domain triadic factorization. In Proceedings of the 22Nd International Conference on World Wide Web, WWW '13, pages 595{606, Republic and Canton of Geneva, Switzerland, 2013. International World Wide Web Conferences Steering Committee.
    • [10] P. Knees, D. Schnitzer, and A. Flexer. Improving neighborhood-based collaborative ltering by reducing hubness. In Proceedings of International Conference on Multimedia Retrieval, ICMR '14, pages 161:161{161:168, New York, NY, USA, 2014. ACM.
    • [11] L. M. LaVange and G. G. Koch. Rank score tests. Circulation, 114(23):2528{2533, 2006.
    • [12] J. Lee, S. Bengio, S. Kim, G. Lebanon, and Y. Singer. Local collaborative ranking. In Proceedings of the 23rd International Conference on World Wide Web, WWW '14, pages 85{96, New York, NY, USA, 2014. ACM.
    • [13] B. Li. Cross-domain collaborative ltering: A brief survey. In Tools with Arti cial Intelligence (ICTAI), 2011 23rd IEEE International Conference on, pages 1085{1086. IEEE, 2011.
    • [14] H. Liu, J. Goulding, and T. Brailsford. Towards computation of novel ideas from corpora of scienti c text. In Machine Learning and Knowledge Discovery in Databases, pages 541{556. Springer, 2015.
    • [15] Manisha Hiralall. Recommender systems for e-shops. Msc dissertation, Vrije Universiteit, 2011.
    • [16] W. Pan, E. W. Xiang, N. N. Liu, and Q. Yang. Transfer learning in collaborative ltering for sparsity reduction. In AAAI, volume 10, pages 230{235, 2010.
    • [17] D. H. Park, H. K. Kim, I. Y. Choi, and J. K. Kim. A literature review and classi cation of recommender systems research. Expert Systems with Applications, 39:10072{10059, 2012.
    • [18] J. D. M. Rennie and N. Srebro. Fast maximum margin matrix factorization for collaborative prediction. In Proceedings of the 22Nd International Conference on Machine Learning, ICML '05, pages 713{719, New York, NY, USA, 2005. ACM.
    • [19] Y. Rong, X. Wen, and H. Cheng. A monte carlo algorithm for cold start recommendation. In Proceedings of the 23rd International Conference on World Wide Web, WWW '14, pages 327{336, New York, NY, USA, 2014. ACM.
    • [20] S. D. Roy, T. Mei, W. Zeng, and S. Li. Socialtransfer: cross-domain transfer learning from social streams for media applications. In Proceedings of the 20th ACM international conference on Multimedia, pages 649{658. ACM, 2012.
    • [21] S. Sahebi and P. Brusilovsky. Cross-domain collaborative recommendation in a cold-start context: The impact of user pro le size on the quality of recommendation. In User Modeling, Adaptation, and Personalization, pages 289{295. Springer, 2013.
    • [22] R. Salakhutdinov and A. Mnih. Bayesian probabilistic matrix factorization using markov chain monte carlo. In Proceedings of the 25th International Conference on Machine Learning, ICML '08, pages 880{887, New York, NY, USA, 2008. ACM.
    • [23] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative ltering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web, pages 285{295. ACM, 2001.
    • [24] B. Shapira, L. Rokach, and S. Freilikhman. Facebook single and cross domain data for recommendation systems. User Modeling and User-Adapted Interaction, 23(2-3):211{247, 2013.
    • [25] X. Su and T. M. Khoshgoftaar. A Survey of Collaborative Filtering Techniques. Arti cial Intelligence, pages 1{19, 2009.
    • [26] J. Tang, S. Wu, J. Sun, and H. Su. Cross-domain collaboration recommendation. In Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '12, pages 1285{1293, New York, NY, USA, 2012. ACM.
    • [27] F. Xia, N. Y. Asabere, A. M. Ahmed, J. Li, and X. Kong. Mobile Multimedia Recommendation in Smart Communities: A Survey, 2013.
    • [28] D. Zhang, C.-H. Hsu, M. Chen, Q. Chen, N. Xiong, and J. Lloret. Cold-start recommendation using bi-clustering and fusion for large-scale social recommender systems. Emerging Topics in Computing, IEEE Transactions on, 2(2):239{250, June 2014.
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  • Discovered through pilot similarity algorithms. Send us your feedback.

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