Abstract
Massive books with social information, e.g. reviews, rates and tags, have emerged in large numbers on the web. However, there are several limitations in traditional search methods for social books, as social books include complicated and various social information. Relevance feedback is always an important and concerned technique in information retrieval. Therefore in this paper we propose a search system based on pseudo-relevance feedback (PRF) for expanding and enriching the social information of queries. In our system, First, Galago is used to get the initial rank list. Then relevance models are performed to select candidate high-frequent words that can be benefit to queries. Next, the original queries and these selected words are combined into new queries by linear smoothing. With evaluation on the INEX2012 / 2013 Social Book Search Track database, our proposed system has an encouraged performance (nDCG@10) compared to several state-of-the-art (contest) systems.
The research was partly supported by the National Natural Science Foundation of China (61105018, 61175020).
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Geng, B., Zhou, F., Qu, J., Zhang, BW., Cui, XP., Yin, XC. (2014). Social Book Search with Pseudo-Relevance Feedback. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8835. Springer, Cham. https://doi.org/10.1007/978-3-319-12640-1_25
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DOI: https://doi.org/10.1007/978-3-319-12640-1_25
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