Robin Burke

TIST Journal 2021 Journal Article

Flatter Is Better

• Masoud Mansoury
• Robin Burke
• Bamshad Mobasher

It is well known that explicit user ratings in recommender systems are biased toward high ratings and that users differ significantly in their usage of the rating scale. Implementers usually compensate for these issues through rating normalization or the inclusion of a user bias term in factorization models. However, these methods adjust only for the central tendency of users’ distributions. In this work, we demonstrate that a lack of flatness in rating distributions is negatively correlated with recommendation performance. We propose a rating transformation model that compensates for skew in the rating distribution as well as its central tendency by converting ratings into percentile values as a pre-processing step before recommendation generation. This transformation flattens the rating distribution, better compensates for differences in rating distributions, and improves recommendation performance. We also show that a smoothed version of this transformation can yield more intuitive results for users with very narrow rating distributions. A comprehensive set of experiments, with state-of-the-art recommendation algorithms in four real-world datasets, show improved ranking performance for these percentile transformations.

IJCAI Conference 2015 Conference Paper

Adapting to User Preference Changes in Interactive Recommendation

• Negar Hariri
• Bamshad Mobasher
• Robin Burke

IS Journal 2007 Journal Article

Attacks and Remedies in Collaborative Recommendation

• Bamshad Mobasher
• Robin Burke
• Runa Bhaumik
• J.J. Sandvig

Collaborative-filtering recommender systems are an electronic extension of everyday social recommendation behavior: people share opinions and decide whether or not to act on the basis of what they hear. Collaborative filtering lets you scale such interactions to groups of thousands or even millions. Publicly accessible user-adaptive systems such as collaborative recommender systems introduce security issues that must be solved if users are to perceive these systems as objective, unbiased, and accurate.

AAAI Conference 1994 Conference Paper

Tailoring Retrieval to Support Case-Based Teaching

• Robin Burke

This paper describes how a computer program can support learning by retrieving and presenting relevant stories drawn from a video case base. Although this is an information retrieval problem, it is not a problem that fits comfortably within the classical IR model (Salton and McGill, 1983) because in the classical model the computer system is too passive. The standard model of IR assumes that the user will take the initiative to formulate retrieval requests, but a teaching system must be able to initiate retrieval and formulate retrieval requests automatically. We describe a system, called SPIEL, that performs this type of retrieval, and discuss theoretical challenges addressed in implementing such a system. These challenges include the development of a representation language for indexing the system’s video library, and the development of set of retrieval strategies and recognition knowledge that allow the system to locate educationally relevant stories.