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- item-2 hasContent maxim-grinev.
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- item-3 hasContent dmitry-lizorkin.
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- 68 type InProceedings.
- 68 label "Tagommenders: Connecting Users to Items through Tags".
- 68 sameAs 68.
- 68 abstract "Tagging has emerged as a powerful mechanism that enables users to find, organize, and understand online entities. Recommender systems similarly enable users to efficiently navigate vast collections of items. Algorithms combining tags with recommenders may deliver both the automation inherent in recommenders, and the flexibility and conceptual comprehensibility inherent in tagging systems. In this paper we explore tagommenders, recommender algorithms that predict users' preferences for items based on their inferred preferences for tags. We describe tag preference inference algorithms based on users' interactions with tags and movies, and evaluate these algorithms based on tag preference ratings collected from 995 MovieLens users. We design and evaluate algorithms that predict users' ratings for movies based on their inferred tag preferences. Our tag-based algorithms generate better recommendation rankings than state-of-the-art algorithms, and they may lead to flexible recommender systems that leverage the characteristics of items users find most important.".
- 68 hasAuthorList authorList.
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- 68 keyword "Social Networks and Web 2.0".
- 68 title "Tagommenders: Connecting Users to Items through Tags".
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- item-1 hasContent shilad-sen.
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- item-2 hasContent jesse-vig.
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- item-3 hasContent john-riedl.
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- 69 type InProceedings.
- 69 label "Collaborative Filtering for Orkut Communities: Discovery of User Latent Behavior".
- 69 sameAs 69.
- 69 abstract "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 [1] and demon- strate our parallel implementation's scalability with varying numbers of machines.".
- 69 hasAuthorList authorList.
- 69 isPartOf proceedings.
- 69 keyword "Social Networks and Web 2.0".
- 69 title "Collaborative Filtering for Orkut Communities: Discovery of User Latent Behavior".
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- item-1 hasContent wen-yen-chen.
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- item-2 hasContent jon-chyuan-chu.
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- item-3 hasContent junyi-luan.
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- item-4 hasContent hongjie-bai.
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- item-5 hasContent yi-wang.
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- item-6 hasContent edward-y-chang.
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- 7 type InProceedings.
- 7 label "Detecting the Origin of Text Segments Efficiently".
- 7 sameAs 7.
- 7 abstract "In the origin detection problem an algorithm is given a set S of documents, ordered by creation time, and a query document D. It needs to output for every consecutive sequence of k alphanumeric terms in D the earliest document in S in which the sequence appeared (if such a document exists). Algorithms for the origin detection problem can, for example, be used to detect the "origin" of text segments in D and thus to detect novel content in D. They can also find the document from which the author of D has copied the most (or show that D is mostly original.) We propose novel algorithms for this problem and evaluate them together with a large number of previously published algorithms. Our results show that (1) detecting the origin of text segments efficiently can be done with very high accuracy even when the space used is less than 1% of the size of the documents in S , (2) the precision degrades smoothly with the amount of available space, (3) various estimation techniques can be used to increase the performance of the algorithms.".
- 7 hasAuthorList authorList.
- 7 isPartOf proceedings.
- 7 keyword "Data Mining".
- 7 title "Detecting the Origin of Text Segments Efficiently".
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- item-1 hasContent ossama-abdel-hamid.
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- item-2 hasContent behshad-behzadi.
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- item-3 hasContent stefan-christoph.
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- item-4 hasContent monika-henzinger.
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- 70 type InProceedings.
- 70 label "Personalized Recommendation on Dynamic Content Using Predictive Bilinear Models".