By Hong Gao, Jinho Kim, Yasushi Sakurai
This e-book constitutes the workshop complaints of the twenty first overseas convention on Database structures for complex functions, DASFAA 2016, held in Dallas, TX, united states, in April 2016.
The quantity comprises 32 complete papers (selected from forty three submissions) from four workshops, every one targeting a selected zone that contributes to the most issues of DASFAA 2016: The 3rd overseas Workshop on Semantic Computing and Personalization, SeCoP 2016; the 3rd overseas Workshop on enormous info administration and repair, BDMS 2016; the 1st foreign Workshop on monstrous information caliber administration, BDQM 2016; and the second one overseas 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 info for Database Systems for Advanced Applications: DASFAA 2016 International Workshops: BDMS, BDQM, MoI, and SeCoP, Dallas, TX, USA, April 16-19, 2016, Proceedings
Syst. 44(3), 335–353 (2014) 31. : Large-scale parallel collaborative ﬁltering for the netﬂix prize. , Xu, J. ) AAIM 2008. LNCS, vol. 5034, pp. 337–348. Springer, Heidelberg (2008) 32. : Improving recommendation lists through topic diversiﬁcation. In: Proceedings of the 14th International Conference on World Wide Web, pp. 22–32. cn Abstract. Recent years have witnessed the rapid growth of event-based social networks (EBSNs) such as Plancast and DoubanEvent. In these EBSNs, followee recommendation which recommends new users to follow can bring great beneﬁts to both users and service providers.
Let Nek denote the number of attendees of event ek and we use a discrete uniform distribution Followee Recommendation in Event-Based Social Networks 33 pi = 1/Nek to describe the proportion of a certain user among the attendees. Let ENek denote the entropy of event ek , then we deﬁne ENek as follows: ENek = − pi log pi = log Nek . i As shown, the event entropy has a positive correlation with the number of attendees of an event. , en event(ui , uj ) = ek ∈Ei ∩Ej 1 . ENek Weighted common events. This feature takes the weights of events into consideration.
In our method, we combine all the explicit and latent features which are captured from the online social network and the oﬄine event participation network. Moreover, we propose a Bayesian optimization framework which adopts pairwise user preference on both the social relations and the events. The experimental results on real-world data demonstrate the eﬀectiveness of our method. Acknowledgements. The work was supported by National Natural Science Foundation of China under Grant 61502047. References 1.