By Hong Gao, Jinho Kim, Yasushi Sakurai

ISBN-10: 3319320548

ISBN-13: 9783319320540

ISBN-10: 3319320556

ISBN-13: 9783319320557

This ebook constitutes the workshop complaints of the twenty first foreign convention on Database platforms for complex purposes, DASFAA 2016, held in Dallas, TX, united states, in April 2016.

The quantity includes 32 complete papers (selected from forty three submissions) from four workshops, each one targeting a particular sector that contributes to the most topics of DASFAA 2016: The 3rd foreign Workshop on Semantic Computing and Personalization, SeCoP 2016; the 3rd foreign Workshop on mammoth information administration and repair, BDMS 2016; the 1st overseas Workshop on monstrous facts caliber administration, BDQM 2016; and the second one overseas Workshop on cellular of web, MoI 2016.

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Read Online or Download Database Systems for Advanced Applications: DASFAA 2016 International Workshops: BDMS, BDQM, MoI, and SeCoP, Dallas, TX, USA, April 16-19, 2016, Proceedings PDF

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Extra info for Database Systems for Advanced Applications: DASFAA 2016 International Workshops: BDMS, BDQM, MoI, and SeCoP, Dallas, TX, USA, April 16-19, 2016, Proceedings

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Syst. 23(2), 107–143 (2004) 21. : Recommender Systems Handbook. Springer, Heidelberg (2011) 22. : CliMF: Learning to maximize reciprocal rank with collaborativeless-is-more filtering. In: Proceedings of the Sixth ACM Conference on Recommender Systems, pp. 139–146. ACM (2012) 23. : Improving sales diversity by recommending users to items. In: Proceedings of the 8th ACM Conference on Recommender systems, pp. 145– 152. ACM (2014) 24. : Cofi rank-maximum margin matrix factorization for collaborative ranking.

Moreover, to take advantage of the latent factor model, we employ matrix factorization model in the online social network and the offline event participation network to capture the latent features in both the networks. More importantly, to derive users’s latent features better and more comprehensively, we assume that these two networks share the same latent user feature in the matrix factorization model for each user. Finally, we combine all the explicit and latent features into a unified recommendation model.

Notice that, these two preferences share the same latent user feature for each user. 3 Recommendation Model In this work, we combine the explicit and latent features to obtain a user’s overall preference towards another user. Let ru (ui , uj ) denote the user ui ’s overall preference towards user uj . Then, we define ru (ui , uj ) as follows: u ru (ui , uj ) = rfu (ui , uj ) + rm (ui , uj ) = wT zij + bui + buj + xi T Hxj . (3) Moreover, we derive a user’s overall preference towards an event only based on the latent features.

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Database Systems for Advanced Applications: DASFAA 2016 International Workshops: BDMS, BDQM, MoI, and SeCoP, Dallas, TX, USA, April 16-19, 2016, Proceedings by Hong Gao, Jinho Kim, Yasushi Sakurai


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