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

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 lawsuits 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 offered inside those lawsuits have been rigorously reviewed and chosen from 371 submissions. They conceal the overall fields of trend mining; social community and social media; class; graph and community mining; purposes; privateness keeping; advice; function choice and relief; computer studying; temporal and spatial info; novel algorithms; clustering; biomedical info mining; circulation mining; outlier and anomaly detection; multi-sources mining; and unstructured facts 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

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8] revealed two important factors: individual preference and interpersonal influence for better utilization of social information. CircleCon [11] used the domain-specific “Trust Circles” to extend the SocialMF [7]. However, all of them give no consideration to item content and the similarity among items. In this paper, we incorporate this information to elaborate recommendation. 3. 1 Preliminaries Because of the weakness of directly modeling every rating mentioned in Section 1, we take advantage of user’s tastes to the information of items and indirectly Two-Phase Layered Learning Recommendation via Category Structure 17 model the rating.

Such great efforts motivate us to wonder: Is it possible to design a dynamic circle recommendation system which can automatically suggest a ranked list of friend candidates driven by both historical interaction statistics and contextual information such as time point? Most studies on formation of groups mainly focus on static group formation, where a group is a fixed set of friedns manually pre-defined by a user. S. Tseng et al. ): PAKDD 2014, Part II, LNAI 8444, pp. 25–37, 2014. -K. Chou et al. Fig.

In this paper, we set reservoir size to T1=(R+=1,000: R−=4,000), T2=(R+=5,000: R−=20,000), and T3=(R+=10,000: R−=40,000) respectively. 2%. 5% within the scope of tolerable time. So we can draw a conclusion that recommendation quality is good enough when 5,000≤|R+|≤10,000 and R−=4|R+|. 7 Conclusions and Future Work In this paper, we propose an offline ranking model CTR+ which considers explicit feature, content, social relation and personal hashtags. Moreover, we propose a novel tweet ranking online framework CTROF for real-time personalized recommendation.

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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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