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 lawsuits of the 18th Pacific-Asia convention on wisdom Discovery and information Mining, PAKDD 2014, held in Tainan, Taiwan, in might 2014. The forty complete papers and the 60 brief papers awarded inside of those complaints have been conscientiously reviewed and chosen from 371 submissions. They conceal the final fields of trend mining; social community and social media; type; graph and community mining; purposes; privateness maintaining; advice; characteristic choice and aid; laptop studying; temporal and spatial info; novel algorithms; clustering; biomedical facts mining; circulate mining; outlier and anomaly detection; multi-sources mining; and unstructured facts and textual content mining.
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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
557–566 (2013) 13. : Real-time top-n recommendation in social streams. In: RecSys 2012, pp. 59–66 (2012) 14. : Personalized tweet re-ranking. In: WSDM 2013, pp. 577–586 (2013) 15. : Learning to rank social update streams. In: SIGIR 2012, pp. 651–660 (2012) 16. : Random Sampling with a Reservoir. ACM TOMS 11(1), 37–57 (1985) 17. com/ 18. org/ 19. : Factorization Machines with libFM. ACM TIST 3(3), 57 (2012) 20. : SVDFeature: A Toolkit for Feature-based Collaborative Filtering. , Beijing Jiaotong University, China School of Information Science and Technology, Sun Yat-sen University, China 3 School of Computer Science, University of Adelaide, Australia School of Electronics and Info.
Figure 3 shows that runtime convergence of different models. All models have different convergence rate and converge to steady values after 30 rounds. So our offline base model is reasonably set to 40 rounds. In addition, we calculate P@5 value by K. Song et al. 36 term feature hashtag feature social feature CTR CTR+ 1 5 9 13 17 21 25 29 P@5 P@10 P@15 MAP iteration number Fig. 2. 3. Runtime Convergence of CTR+ setting factor number to 32, 64, 80, 96, and 112. 8641) remains stable. Therefore, we set 64 factors reasonably.
Ji et al. (Figure 1(a)) using the similarity measurement (PCC, VSS ), whereas model based algorithms explore the training data to train a model, which can make fast prediction using only a few parameters of the model instead of manipulating the whole matrix. Traditional CF algorithms have several challenges. Due to the sparsity, they cannot make reliable recommendation for lazy users who have rated few items or cold start users who have never rated any items because of insuﬃcient data to capture their tastes accurately.