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This e-book constitutes the workshop court cases of the twenty first foreign convention on Database structures for complex functions, 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 targeting a selected region that contributes to the most subject matters of DASFAA 2016: The 3rd overseas Workshop on Semantic Computing and Personalization, SeCoP 2016; the 3rd overseas Workshop on monstrous info administration and repair, BDMS 2016; the 1st overseas Workshop on substantial facts 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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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
Sample text
According to the social theory of homophily, users with similar interests are more likely to establish social relations. Besides, since the events are held at physical places, users who have attended the same event may have a chance to meet each other and develop new social links between them. Therefore, we can exploit the event participation records to improve the effectiveness of followee recommendation. To relieve the problem of data sparsity, we utilize both the social relations and event participation records for followee recommendation.
Interest of the neighbor set. Therefore, we can decrease the degree of popularity normalization in order to reduce the bias towards long tail items. The revised function is written as: Pˆ (u, i) = a∈N (u) β· sim(u, a) · VU (a)[i] |U (i)| · a∈N (u) (9) sim(u, a) where β is a small constant to make sure the probability is between 0 and 1. 2 The Second Step The second step is considered as a classical rating prediction problem. It can be done by making use of existing techniques. In UTSP, we use SVD++ [16] in the second step.
The AUC results of all the methods in both datasets are shown in Table 2. We can clearly observe that our proposed model, HNFR, always outperforms all the baseline methods in both the datasets significantly. Moreover, the performance of HNFR is better than BPR-MAF, which demonstrates the effectiveness of considering the latent features in the offline event participation network. In both the datasets, BPR-AF achieves better performance than BPR-SF and BPREF, which indicates the strength of combining social features and event-based features.