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- 194 type InProceedings.
- 194 label "Ranking Community Answers via Analogical Reasoning".
- 194 sameAs 194.
- 194 abstract "Due to the lexical gap between questions and answers, automatically detecting right answers becomes very challenging for community question-answering sites. In this paper, we propose an analogical reasoning-based method. It treats questions and answers as relational data and ranks an answer by measuring the analogy of its link to a query with the links embedded in previous relevant knowledge; the answer that links in the most analogous way to the new question is assumed to be the best answer. We based our experiments on 29.8 million Yahoo!Answer questionanswer threads and showed the effectiveness of the approach.".
- 194 hasAuthorList authorList.
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- 194 keyword "Poster Session".
- 194 title "Ranking Community Answers via Analogical Reasoning".
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- 195 type InProceedings.
- 195 label "Probabilistic Question Recommendation for Question Answering Communities".
- 195 sameAs 195.
- 195 abstract "User-Interactive Question Answering (QA) communities such as Yahoo! Answers are growing in popularity. However, as these QA sites always have thousands of new questions posted daily, it is difficult for users to find the questions that are of interest to them. Consequently, this may delay the answering of the new questions. This gives rise to question recommendation techniques that help users locate interesting questions. In this paper, we adopt the Probabilistic Latent Semantic Analysis (PLSA) model for question recommendation and propose a novel metric to evaluate the performance of our approach. The experimental results show our recommendation approach is effective.".
- 195 hasAuthorList authorList.
- 195 isPartOf proceedings.
- 195 keyword "Poster Session".
- 195 title "Probabilistic Question Recommendation for Question Answering Communities".
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- 196 type InProceedings.
- 196 label "Buzz-Based Recommender System".
- 196 sameAs 196.
- 196 abstract "In this paper, we describe a buzz-based recommender system based on a large source of queries in an eCommerce application. The system detects bursts in query trends. These bursts are linked to external entities like news and inventory information to find the queries currently in-demand which we refer to as buzz queries. The system follows the paradigm of limited quantity merchandising, in the sense that on a per-day basis the system shows recommendations around a single buzz query with the intent of increasing user curiosity, and improving activity and stickiness on the site. A semantic neighborhood of the chosen buzz query is selected and appropriate recommendations are made on products that relate to this neighborhood.".
- 196 hasAuthorList authorList.
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- 196 keyword "Poster Session".
- 196 title "Buzz-Based Recommender System".
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- 197 type InProceedings.
- 197 label "Discovering User Profiles".
- 197 sameAs 197.
- 197 abstract "In this paper we describe techniques for the discovery and construction of user profiles. Leveraging from the emergent data web, our system addresses the problem of sparseness of user profile information currently faced by both asserted and inferred profile systems. A profile mediator, that dynamically builds the most suitable user profile for a particular service or interaction in real-time, is employed in our prototype implementation.".