Probabilistic question recommendation for question answering communities

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.

REFERENCES

• 1: Lada A. Adamic , Jun Zhang , Eytan Bakshy , Mark S. Ackerman, Knowledge sharing and yahoo answers: everyone knows something, Proceeding of the 17th international conference on World Wide Web, April 21-25, 2008, Beijing, China [doi>10.1145/1367497.1367587]
• 2: Thomas Hofmann, Probabilistic latent semantic indexing, Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, p.50-57, August 15-19, 1999, Berkeley, California, United States [doi>10.1145/312624.312649]
• 3: Alexandrin Popescul , Lyle H. Ungar , David M. Pennock , Steve Lawrence, Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments, Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, p.437-444, August 02-05, 2001

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Information filtering

General Terms:
Algorithms, Design, Experimentation

Keywords:
PLSA, question answering, question recommendation