PLSA-Based Personalized Information Retrieval with Network Regularization

PLSA-Based Personalized Information Retrieval with Network Regularization

Qiuyu Zhu, Dongmei Li, Cong Dai, Qichen Han, Yi Lin
Copyright: © 2019 |Pages: 12
DOI: 10.4018/JITR.2019010108
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With the rapid development of the Internet, the information retrieval model based on the keywords matching algorithm has not met the requirements of users, because people with various query history always have different retrieval intentions. User query history often implies their interests. Therefore, it is of great importance to enhance the recall ratio and the precision ratio by applying query history into the judgment of retrieval intentions. For this sake, this article does research on user query history and proposes a method to construct user interest model utilizing query history. Coordinately, the authors design a model called PLSA-based Personalized Information Retrieval with Network Regularization. Finally, the model is applied into academic information retrieval and the authors compare it with Baidu Scholar and the personalized information retrieval model based on the probabilistic latent semantic analysis topic model. The experiment results prove that this model can effectively extract topics and retrieves back results more satisfied for users' requirements. Also, this model improves the effect of retrieval results apparently. In addition, the retrieval model can be utilized not only in the academic information retrieval, but also in the personalized information retrieval on microblog search, associate recommendation, etc.
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With the enormous volume of Web pages containing massive information on the Internet, the technology on the information retrieval resolves the problem that people how to find profitable information to some extent. Some search engines such as Baidu and Google have already made a great progress on the information retrieval. However, there also exist some problems. At first, these search engines ignore different requests of users instead of taking different strategy to diverse users. Moreover, the majority of queries submitted to search engines are short and ambiguous, which leads to a limited query expression. Even for the same query, different users might express different preferences over the retrieved documents. Thus, the search engines do not make clear meaning of user’s query intention (Jaime et al., 2010). The two reasons above lead to the decrease of the recall ratio and the precision ratio. To resolve the problem that user’s query intention is not understood by engines effectively, the scholars put forward to implementing personalized informational retrieval according to the characteristics of textual information. For example, some scholars design information retrieval model based on the query expansion (Claudio & Giovanni, 2012; Liu et al., 2010; Li et al., 2004), some leverage on user reviews and carry out personalized information retrieval (Chen & Chen, 2015; Chen et al., 2015) and some extract user interests and comprehend user intention by query history (Matthijs & Radlinski, 2011; Bennett et al., 2012;Zhang et al., 2013; Rami et al., 2013).

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