Items where author is affiliated with Massachusetts Institute of Technology
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 Haghani, Parisa
and Jost, Michael
and Aberer, Karl
and De Meer, Hermann idMesh: Graph-Based Disambiguation of Linked Data.
We tackle the problem of disambiguating entities on the Web. We propose a user-driven scheme where graphs of entities – represented by globally identiﬁable declarative artifacts – self-organize in a dynamic and probabilistic manner. Our solution has the following two desirable properties: i) it lets end-users freely deﬁne associations between arbitrary entities and ii) it probabilistically infers entity relationships based on uncertain links using constraintsatisfaction mechanisms. We outline the interface between our scheme and the current data Web, and show how higher-layer applications can take advantage of our approach to enhance search and update of information relating to online entities. We describe a decentralized infrastructure supporting efﬁcient and scalable entity disambiguation and demonstrate the practicability of our approach in a deployment over several hundreds of machines.
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