Robin Burke

Adapting Recommendations to Contextual Changes Using Hierarchical Hidden Markov Models

2015

Recommender systems help users find items of interest by tailoring their recommendations to users' personal preferences. The utility of an item for a user, however, may vary greatly depending on that user's specific situation or …

Controlling Popularity Bias in Learning-to-Rank Recommendation

2017

Many recommendation algorithms suffer from popularity bias in their output: popular items are recommended frequently and less popular ones rarely, if at all. However, less popular, long-tail items are precisely those that are often desirable …

Managing Popularity Bias in Recommender Systems with Personalized Re-Ranking.

2019 · arXiv (Cornell University)

Many recommender systems suffer from popularity bias: popular items are recommended frequently while less popular, niche products, are recommended rarely or not at all. However, recommending the ignored products in the ``long tail'' is critical …