Macquarie University
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Incorporating user rating credibility in recommender systems

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posted on 2022-03-28, 02:17 authored by Naime Ranjbar Kermany
There has been a lot of research efforts aimed at improving the recommendation accuracy with Collaborative Filtering (CF); yet there is still a lack of investigation into the integration of CF algorithms with the analysis of users' rating behaviors. Considering that by incorporating the rating credibility, the impact of the ratings given by neighbors with low credibility should be decreased. In this work, we develop an integrated solution for CF recommendation by incorporating the credibility of users' ratings, demographic information of the people, and ontological semantics of items. The demographic information of users and ontological semantics of items are used in the similarity measurement of users/items to alleviate the issues of sparsity and cold-start in CF algorithms. To our knowledge, this is the first time an integration of the rating credibility, demographic information of users, and ontological semantics of items is created in order to improve the performance of CF recommendation system. Experiments are conducted on the real-world datasets of MovieLens and Yahoo!Movie. Comparing with baseline methods, the experimental results show that the proposed approach significantly improves the quality of recommendation in terms of accuracy, precision, recall, F-measure, and standard deviation of the errors.


Table of Contents

1. Introduction -- 2. Literature studies and related work -- 3. An integrated method for recommendaton -- 4. Experiments and discussion -- 5. Conclusion -- List of symbols -- References.


Bibliography: pages 53-60 Empirical thesis.

Awarding Institution

Macquarie University

Degree Type

Thesis MRes


MRes, Macquarie University, Faculty of Science and Engineering, Department of Computing

Department, Centre or School

Department of Computing

Year of Award


Principal Supervisor

Jian Yang


Copyright Naime Ranjbar Kermany 2019. Copyright disclaimer:




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