An adaptive fuzzy based recommender system for enterprise search

This thesis discusses relevance feedback including implicit parameters, explicit parameters and user query and how they could be used to build a recommender system to enhance the search performance in the enterprise. It presents an approach for the development of an adaptive fuzzy logic based recommender system for enterprise search. The system is designed to recommend documents and people based on the user query in a task specific search environment. The proposed approach provides a new mechanism for constructing and integrating a task, user and document profiles into a unified index thorough the use of relevance feedback and fuzzy rule based summarisation. The three profiles are fuzzy based and are created using the captured relevance feedback. In the task profile, each task was modelled as a sequence of weighted terms which were used by the users to complete the task. In the user profile, the user was modelled as a sequence of weighted terms which were used to search for the required information. In the document profile the document was modelled as a group of weighted terms which were used by the users to retrieve the document. Fuzzy sets and rules were used to calculate the term weight based on the term frequency in the user queries. An empirical research was carried out to capture the relevance feedback from 35 users on 20 predefined simulated enterprise search tasks and to investigate the correlation between the implicit and explicit relevance feedback. Based on the results, an adaptive linear predictive model was developed to estimate the document relevancy from the implicit feedback parameters. The predicted document relevancy was then used to train the fuzzy system which created and integrated the three profiles, as briefly described above. The captured data set was used to develop and train the fuzzy system. The proposed system achieved 89% accuracy performance classifying the relevant documents. With regard to the implementation, Apache Sorl, Apache Tikka, Oracle 11g and Java were used to develop a prototype system. The overall retrieval accuracy performance of the proposed system was tested by carrying out a comparative retrieval accuracy performance evaluation based on Precision (P), Recall (R) and ranking analysis. The values of P and R of the proposed system were compared with two other systems being the standard inverted index based Solr system and the semantic indexing based lucid system. The proposed system enhanced the value of P significantly where the average of P value has been increased from 0.00428 to 0.064 as compared with the

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