By Matthias Renz, Cyrus Shahabi, Xiaofang Zhou, Muhammad Aamir Cheema
This quantity set LNCS 9049 and LNCS 9050 constitutes the refereed lawsuits of the twentieth foreign convention on Database structures for complex functions, DASFAA 2015, held in Hanoi, Vietnam, in April 2015. The sixty three complete papers offered have been rigorously reviewed and chosen from a complete of 287 submissions. The papers disguise the next themes: info mining; information streams and time sequence; database garage and index; spatio-temporal facts; glossy computing platform; social networks; info integration and knowledge caliber; info retrieval and summarization; protection and privateness; outlier and imbalanced info research; probabilistic and unsure info; question processing.
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Extra info for Database Systems for Advanced Applications: 20th International Conference, DASFAA 2015, Hanoi, Vietnam, April 20-23, 2015, Proceedings, Part I
Knowl. Inf. Syst. 8(2) (2005) 7. : Mining patterns in networks using homomorphism. In: SDM (2012) 8. : Eﬃcient pattern discovery for semistructured data. In: ICTAI (2005) 9. : Mining tree queries in a graph. In: KDD (2005) 10. : Mining frequent patterns without candidate generation. In: SIGMOD Conference (2000) 11. : Amiot: Induced ordered tree mining in tree-structured databases. In: ICDM (2005) 12. : Ordered and unordered tree inclusion. SIAM J. Comput. 24(2), 340–356 (1995) 13. : Containment and equivalence for a fragment of xpath.
There are two essential parameters in our model, which are the dimension of latent space K, and the graph regularization parameter λW . The parameter K represents the latent feature size of latent user model and latent topic space of questions. The parameter λW shows the obtained beneﬁts of our method from the inferred user-to-user graph. We ﬁrst study the impact of parameter K by varying its value from 10 to 50, and present the experimental results in Figures 5(a) to 5(d). Figure 5(a) shows that the Cold-Start Rate of the experts found by GRLM increases and then becomes convergent with respect to the dimension of latent space.
We explore the “following relations” between users and topical interests to build the user-to-user graph. We then propose the graph regularized latent Cold-Start Expert Finding in CQA via Graph Regularization 23 Fig. 2. An Illustration of User’s Topical Interests model by incorporating with the user-to-user graph and devise a variational method for inferring the model. – We conduct extensive experiments on our proposed method. We demonstrate that, by incorporating with user-to-user graph, our method signiﬁcantly outperforms other state-of-the-art expert ﬁnding techniques.