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KDD
2009
ACM
223views Data Mining» more  KDD 2009»
16 years 7 months ago
Collaborative filtering with temporal dynamics
Customer preferences for products are drifting over time. Product perception and popularity are constantly changing as new selection emerges. Similarly, customer inclinations are ...
Yehuda Koren
KDD
2009
ACM
190views Data Mining» more  KDD 2009»
16 years 7 months ago
Efficient influence maximization in social networks
Influence maximization is the problem of finding a small subset of nodes (seed nodes) in a social network that could maximize the spread of influence. In this paper, we study the ...
Wei Chen, Yajun Wang, Siyu Yang
KDD
2009
ACM
198views Data Mining» more  KDD 2009»
16 years 7 months ago
Heterogeneous source consensus learning via decision propagation and negotiation
Nowadays, enormous amounts of data are continuously generated not only in massive scale, but also from different, sometimes conflicting, views. Therefore, it is important to conso...
Jing Gao, Wei Fan, Yizhou Sun, Jiawei Han
KDD
2009
ACM
167views Data Mining» more  KDD 2009»
16 years 7 months ago
Seven pitfalls to avoid when running controlled experiments on the web
Controlled experiments, also called randomized experiments and A/B tests, have had a profound influence on multiple fields, including medicine, agriculture, manufacturing, and adv...
Thomas Crook, Brian Frasca, Ron Kohavi, Roger Long...
191
Voted
KDD
2009
ACM
198views Data Mining» more  KDD 2009»
16 years 7 months ago
Pervasive parallelism in data mining: dataflow solution to co-clustering large and sparse Netflix data
All Netflix Prize algorithms proposed so far are prohibitively costly for large-scale production systems. In this paper, we describe an efficient dataflow implementation of a coll...
Srivatsava Daruru, Nena M. Marin, Matt Walker, Joy...