New technique to deal with verbose queries in social book search

Abstract

Verbose query reduction and query term weighting are automatic techniques to deal with verbose queries. The objective is either to assign an appropriate weight to query terms according to their importance in the topic, or outright remove unsuitable terms from the query and keep only the suitable terms to the topic and user's need. These techniques improve performance and provide good results for ad hoc information retrieval. In this paper we propose a new approach to deal with long verbose queries in Social Information Retrieval (SIR) by taking Social Book Search as an example. In this approach, a new statistical measure was introduced to reduce and weight terms of verbose queries. Next, we expand the query by exploiting the similar books mentioned by users in their queries. We find that the proposed approach improves significantly the results.

Mining author-tag multilayer graph for social book search

“…We also perform the statistical comparison in terms of N DCG@10 and M AP with the participants. The chosen effective baselines are the run from 10 that used query expansion approaches based on association rules between sets of terms, the runs from Hafsi 11 when authors exploited some user generated content such as reviews and ratings to recommend some books, Benkoussas 12 which are proposed a method that combines the outputs of probabilistic model (InL2) and Language Model (SDM), Chaa 13 that are presented a new technique to automatically generate a stopword list in order to reduce verbose queries. According to the N DCG@10 and M AP experiments, our system is outperforms Benkoussas system (N DCG@10 =0.128, M AP =0.101) The other three pairs of comparison shows similar results and the exact result values are shown in Table 2.…”

Combining Tags and Reviews to Improve Social Book Search Performance

“…The best results were obtained when using DFR model with textual information only. In [4,5], the authors use only tags as textual information about books. They investigate the representation of the query by transforming the long verbose queries to a reduced queries before applying the BM15 retrieval model.…”