15 citations · 23 across the 4 of their papers we have counts for
4 papers
Flatter is better: Percentile Transformations for Recommender Systems
Masoud Mansoury, Robin Burke, Bamshad Mobasher
It is well known that explicit user ratings in recommender systems are biased towards high ratings, and that users differ significantly in their usage of the rating scale. Implemen…
Power of the Few: Analyzing the Impact of Influential Users in Collaborative Recommender Systems
Farzad Eskandanian, Nasim Sonboli, Bamshad Mobasher
Like other social systems, in collaborative filtering a small number of "influential" users may have a large impact on the recommendations of other users, thus affecting the overal…
Modeling the Dynamics of User Preferences for Sequence-Aware Recommendation Using Hidden Markov Models
Farzad Eskandanian, Bamshad Mobasher
In a variety of online settings involving interaction with end-users it is critical for the systems to adapt to changes in user preferences. User preferences on items tend to chang…
Weighted Random Walk Sampling for Multi-Relational Recommendation
Fatemeh Vahedian, Robin Burke, Bamshad Mobasher
In the information overloaded web, personalized recommender systems are essential tools to help users find most relevant information. The most heavily-used recommendation framework…