Items where author is affiliated with Peking University
Number of items: 2.
and Chu, Jon-Chyuan
and Luan, Junyi
and Bai, Hongjie
and Wang, Yi
and Chang, Edward Y. Collaborative Filtering for Orkut Communities: Discovery of User Latent Behavior.
Users of social networking services can connect with each other by forming communities for online interaction. Yet as the number of communities hosted by such websites grows over time, users have even greater need for effective commu- nity recommendations in order to meet more users. In this paper, we investigate two algorithms from very different do- mains and evaluate their effectiveness for personalized com- munity recommendation. First is association rule mining (ARM), which discovers associations between sets of com- munities that are shared across many users. Second is latent Dirichlet allocation (LDA), which models user-community co-occurrences using latent aspects. In comparing LDA with ARM, we are interested in discovering whether modeling low-rank latent structure is more effective for recommen- dations than directly mining rules from the observed data. We experiment on an Orkut data set consisting of 492, 104 users and 118, 002 communities. Our empirical comparisons using the top-k recommendations metric show that LDA performs consistently better than ARM for the community recommendation task when recommending a list of 4 or more communities. However, for recommendation lists of up to 3 communities, ARM is still a bit better. We analyze exam- ples of the latent information learned by LDA to explain this finding. To efficiently handle the large-scale data set, we parallelize LDA on distributed computers  and demon- strate our parallel implementation’s scalability with varying numbers of machines.
and Song, Guo-Jie
and Xie, Kun-Qing
and Wang, Jing-Yao An Experimental Study of Large-Scale Mobile Social Network.
Mobile social network is a typical social network where one or more individuals of similar interests or commonalities, conversing and connecting with one another using the mobile phone. Our works in this paper focus on the experimental study for this kind of social network with the support of large-scale real mobile call data. The main contributions can be summarized as three-fold: ﬁrstly, a large-scale real mobile phone call log of one city has been extracted from a mobile phone carrier in China to construct mobile social network; secondly, common features of traditional social networks, such as power law distribution and small diameter etc, have been experimented, with which we conﬁrm that the mobile social network is a typical scale-free network and has small-world phenomenon; lastly, different from traditional analytical methods, important properties of the actors, such as gender and age, have been introduced into our experiments with some interesting ﬁndings about human behavior, for example, the middle-age people are more active than the young and old people, and the female is unusual more active than the male while in the old age.
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