Student research opportunities
Improving Recommendation Quality Through Entity Resolution
Project Code: CECS_775
This project is available at the following levels:
Honours, Summer Scholar, Masters, PhD
Keywords:
Recommender Systems, Data Quality, Entity Resolution, Data Matching, Web 2.0, Web Mining
Supervisors:
Assoc Professor Peter ChristenDr Huizhi (Elly) Liang
Outline:
Recommender Systems are a kind of effective tool to help users solve the information overload issue. They have been popularly used in various kinds of websites such as Amazon.com to suggest most relevant items (e.g., books, movies) to users. Accuracy and diversity are two important measures of recommendation quality. In web 2.0, the read only web becomes a read and write web. Users can publish their opinions and reviews, share resources (e.g., book marks, photos, videos) and experiences, and build social networks. However, the existence of various kinds of versions of the same item (e.g., video clips, books, and photos) bring challenges to make quality recommendations. Entity resolution, also known as data matching, or record linkage, is the process of identifying which records in databases refer to the same real-world entity. This project will explore how to detect the items that belong to the same entity, and how to improve recommendation quality through adopting advanced entity resolution approaches.
Goals of this project
The objectives of this project are to design algorithms to detect item records that belong to the same entity, design algorithms to make quality recommendations, conduct experimental evaluations on large scale datasets crawled from real-world web 2.0 applications such as Delicious.com, and Twitter.com.
Requirements/Prerequisites
Having attended courses in algorithms and data structures
Good programming skills such as Java or Python.
knowledge about data mining or machine learning is a plus.
Student Gain
Students will learn about recommender systems, data mining and entity resolution techniques, web mining and personalization approaches
Background Literature
1.Badrul Sarwar et. al., Item-based Collaborative Filtering Recommendation Algorithms, WWW’01.
2.Gediminas Adomavicius et. al., Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Transactions on Knowledge and Data Engineering.
3.Peter Christen, et. al., Similarity-Aware Indexing for Real-Time Entity Resolution,CIKM'09.
Links
Introduction of recommender systems (wikipedia)Introduction of entity resolution (wikipedia)
$1Million prize for better movie recommender systems
Example of book recommendation in Amazon.com (Customers Who Bought This Item Also Bought these...)
