145 citations · 356 across the 26 of their papers we have counts for
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cs.IR2018
Detecting Changes in User Preferences using Hidden Markov Models for Sequential Recommendation Tasks
Farzad Eskandanian, Bamshad Mobasher
Recommender systems help users find relevant items of interest based on the past preferences of those users. In many domains, however, the tastes and preferences of users change ov…
cs.IR2018
Popularity-Aware Item Weighting for Long-Tail Recommendation
Himan Abdollahpouri, Robin Burke, Bamshad Mobasher
Many recommender systems suffer from the popularity bias problem: popular items are being recommended frequently while less popular, niche products, are recommended rarely if not a…