Designing and Evaluating a Recommender System within the Book Domain
Thesis
Aloud, Monira . 2008
Publication Work Type:
MSc
Publishing City:
Colchester, United Kingdom
Thesis Type:
MSc
School:
School of Computer Science and Electronic Engineering
Publication Abstract:
Today the World Wide Web provides users with a vast array of information, and commercial activity on the Web has increased to the point where hundreds of new companies are adding web pages daily. This has led to the problem of information overload. Recommender systems have been developed to overcome this problem by providing recommendations that help individual users identify content of interest by using the opinions of a community of users
and/or the user’s preferences. The aim of this thesis was to design and evaluate different approaches for producing personalised recommendations within the book domain. To achieve this goal, the project first investigated existing recommender systems and profiling techniques. The next step was to build users’ profiles by monitoring users’ behaviour, and develop three different approaches for producing recommendations. Finally, an evaluation of the system recommendations’ accuracy was done, by first conducting live user experiments and then performing offline analysis to measure the recommendations’ accuracy using appropriate methods for testing. The system evaluation results show that the accuracy of the system recommendations is very good and that a recommender system based on the combination of content-based and collaborative filtering approaches provides more accurate recommendations for the book domain.
and/or the user’s preferences. The aim of this thesis was to design and evaluate different approaches for producing personalised recommendations within the book domain. To achieve this goal, the project first investigated existing recommender systems and profiling techniques. The next step was to build users’ profiles by monitoring users’ behaviour, and develop three different approaches for producing recommendations. Finally, an evaluation of the system recommendations’ accuracy was done, by first conducting live user experiments and then performing offline analysis to measure the recommendations’ accuracy using appropriate methods for testing. The system evaluation results show that the accuracy of the system recommendations is very good and that a recommender system based on the combination of content-based and collaborative filtering approaches provides more accurate recommendations for the book domain.
Attachment:
| Attachment | Size |
|---|---|
 MoniraAloud-MScThesis.pdf | 2.04 MB |