By Vincent S. Tseng, Tu Bao Ho, Zhi-Hua Zhou, Arbee L.P. Chen, Hung-Yu Kao
The two-volume set LNAI 8443 + LNAI 8444 constitutes the refereed court cases of the 18th Pacific-Asia convention on wisdom Discovery and information 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 rigorously reviewed and chosen from 371 submissions. They disguise the final fields of trend mining; social community and social media; category; graph and community mining; purposes; privateness keeping; advice; function choice and relief; desktop studying; temporal and spatial info; novel algorithms; clustering; biomedical information mining; circulate mining; outlier and anomaly detection; multi-sources mining; and unstructured information and textual content mining.
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Additional info for Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part II
In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, WSDM 2011, pp. 287–296. ACM, New York (2011) 11. : Circle-based recommendation in online social networks. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2012, pp. 1267–1275. ACM, New York (2012) 12. : A survey of hierarchical classiﬁcation across diﬀerent application domains. Data Min. Knowl. Discov. 22(1-2), 31–72 (2011) 13. : Trust based recommender system for semantic web.
Therefore, we set 64 factors reasonably. We imitate tweets flow into our framework in chronological sequence continually. In addition, we let each one’ tweet stream arrive at the same opportunity. Retweetes in testing set are far less than non-retweets, and the ratio is about 1/4. So we set the size of reservoirs R+ and R− to 10,000 and 40,000 respectively for reflecting real data distribution. Therefore, we won’t update our base model until 10,000 retweets and 40,000 non-retweets arrive. In order to verify prediction precision of top-N items in recommendation list, N is set to 5, 10 and 15 respectively.
The training process of CTR+ model is shown below. Algorithm. 1 Building Online CTROF Model In Section 3, we discussed CTR+ with tweet training set D. CTR+ is an offline model, because D is a static training set. As for new incoming tweet i+, we calculate xˆ ui + by decomposing i+ into words, publisher and hashtag vectors. The larger xˆ ui + is, the higher i+ is ranked. Based on offline CTR+, we introduce CTROF in real-time scenario, which update model dynamically every time new tweets arrive. In social network (such as Facebook) or microblogging service (like Twitter and Sina Weibo), messages are updated rapidly.