Download Database Systems for Advanced Applications: DASFAA 2016 by Hong Gao, Jinho Kim, Yasushi Sakurai PDF

By Hong Gao, Jinho Kim, Yasushi Sakurai

This publication constitutes the workshop lawsuits of the twenty first overseas 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, every one concentrating on a particular zone that contributes to the most issues of DASFAA 2016: The 3rd foreign Workshop on Semantic Computing and Personalization, SeCoP 2016; the 3rd overseas Workshop on sizeable facts administration and repair, BDMS 2016; the 1st overseas Workshop on massive information caliber administration, BDQM 2016; and the second one foreign Workshop on cellular of net, MoI 2016.

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Read 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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Additional resources 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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In: SIGKDD, pp. 1266–1275 (2014) 3. : Object typicality for effective web of things recommendations. Decis. Support Syst. 63, 52–63 (2014) 4. : Typicality-based collaborative filtering recommendation. IEEE Trans. Knowl. Data Eng. 26(3), 766–779 (2014) 5. : Make new friends, but keep the old: recommending people on social networking sites. In: CHI, pp. 201–210 (2009) 6. : Librec: a java library for recommender systems. In: Posters, Demos, Late-breaking Results and Workshop Proceedings of User Modeling, Adaptation, and Personalization (UMAP 2015) (2015) 7.

In the following, we describe each explicit feature in detail. 32 S. Li et al. Social Features. Online social relations intuitively play an important role in helping create new social links. In the following, we define several social features based on node neighborhoods in the online social network of the EBSN. Number of common neighborhoods. Given a user pair ui , uj , this feature calculates how many user ui ’s followees have followed user uj and is defined as follows: common neighbor(ui , uj ) = Fi+ ∩ Fj− .

Data Eng. 26(3), 766–779 (2014) 7. : A hybrid recommendation algorithm adapted in e-learning environments. World Wide Web 17(2), 271–284 (2014) 8. : Performance of recommender algorithms on top-n recommendation tasks. In: Proceedings of the fourth ACM conference on Recommender systems, pp. 39–46. ACM (2010) 9. : Memory-based weighted majority prediction. In: ACM SIGIR 1999 Workshop on Recommender Systems. Citeseer (1999) 10. : User perception of differences in recommender algorithms. In: Proceedings of the 8th ACM Conference on Recommender systems, pp.

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