By Hong Gao, Jinho Kim, Yasushi Sakurai
This e-book constitutes the workshop court cases 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, every one concentrating on a particular region that contributes to the most subject matters of DASFAA 2016: The 3rd overseas Workshop on Semantic Computing and Personalization, SeCoP 2016; the 3rd foreign Workshop on mammoth info administration and repair, BDMS 2016; the 1st foreign Workshop on massive information caliber administration, BDQM 2016; and the second one foreign Workshop on cellular of web, MoI 2016.
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
556–562 (2000) 12. : The link prediction problem for social networks. In: CIKM, pp. 556–559 (2003) 13. : Event-based social networks: linking the online and oﬄine socialworlds. In: SIGKDD, pp. 1032–1040 (2012) 14. : Link prediction via matrix factorization. , Vazirgiannis, M. ) ECML PKDD 2011, Part II. LNCS, vol. 6912, pp. 437–452. Springer, Heidelberg (2011) 42 S. Li et al. 15. : Oneclass collaborative ﬁltering. In: ICDM, pp. 502–511 (2008) 16. : BPR: bayesian personalized ranking from implicit feedback.
It is demonstrated that these ranking prediction approaches can get better ranking results than rating prediction ones. However, experiments show that good performance on ranking prediction does not necessarily indicate good quality of top-N recommendation, which is the main purpose of recommender systems. 3 X. Zhao et al. Two-Step Recommendation Approaches Typical recommendation task is based on the rating data which contain two layers of user behaviors. The ﬁrst one is that the current user selects an item to rate.
Since our proposed method, denoted as HNFR, combines all the explicit features (social features and event-based features) and latent features, we devise methods that incorporate these factors individually. Beside, we compare our method with some existing works which are designed for followee recommendation in traditional social networks. In summary, we compare HNFR with the following methods: – FoF : For each user u, this method ranks the candidates according to the number of user u’s followees who have followed the candidate.
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