Matches in ScholarlyData for { <https://w3id.org/scholarlydata/inproceedings/www2009/paper/195> ?p ?o. }
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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".