Download PDF by Vincent S. Tseng, Tu Bao Ho, Zhi-Hua Zhou, Arbee L.P. Chen,: Advances in Knowledge Discovery and Data Mining: 18th

By Vincent S. Tseng, Tu Bao Ho, Zhi-Hua Zhou, Arbee L.P. Chen, Hung-Yu Kao

ISBN-10: 3319066048

ISBN-13: 9783319066042

ISBN-10: 3319066056

ISBN-13: 9783319066059

The two-volume set LNAI 8443 + LNAI 8444 constitutes the refereed complaints of the 18th Pacific-Asia convention on wisdom Discovery and knowledge Mining, PAKDD 2014, held in Tainan, Taiwan, in may possibly 2014. The forty complete papers and the 60 brief papers provided inside those complaints have been conscientiously reviewed and chosen from 371 submissions. They conceal the final fields of trend mining; social community and social media; class; graph and community mining; purposes; privateness retaining; advice; characteristic choice and relief; laptop studying; temporal and spatial facts; novel algorithms; clustering; biomedical facts mining; circulate mining; outlier and anomaly detection; multi-sources mining; and unstructured info and textual content mining.

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Read Online or Download Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part II PDF

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Extra info for Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part II

Example text

Let ((u, i), (u, j)) ∈D denotes a training instance, where i∈Ru has been retweeted and j∉Ru not. Thus, D is formally defined as the tuple set from I, and we can describe it as: D={((u, i), (u, j))|i∈Ru∧j∉Ru∧u∈U}. According to BPR Optimization Criterion, probability p(Θ) follows normal distribution N(0, ∑Θ), in which diagonal matrix ∑Θ=λΘE, E is a unit diagonal matrix and λΘ is a constant, we aim to maximize the formula below: (5) ∏ δ ( xˆuij (Θ )) × p (Θ ) (( u , i ),( u , j ))∈ D where δ is sigmoid function.

8. 7 to 1. 005. 004, despite a certain loss in convergence rate. Figure 2 shows the precision of CTR+ is always higher than CTR, and reflects the importance of single component and their combination. Chronological method’s precision is shown for reference. For simplicity, we just choose explicit features (Text Length, Retweet Score, and Relevance to Hash Tags) as global features. We find that explicit features, term, hash tag, and social component improve MAP by 54%, 70%, 86% and 92% respectively relative to chronological method, which indicates all components are necessary and effective.

Chou et al. Fig. 1. Distribution of Time Centrality that the notion of group should dynamically adapt to context information such as location, time, etc. That is, some users may have the tendency to share information to different groups of friends at certain time points while some users may share information to the same group of friends at all time. For example, a user may have the tendency to share information to his/her family during daytime and share information to his/her close colleagues in the evening.

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Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part II by Vincent S. Tseng, Tu Bao Ho, Zhi-Hua Zhou, Arbee L.P. Chen, Hung-Yu Kao


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