176 citations · 605 across the 32 of their papers we have counts for
32 papers
Book Readership During Movie Releases: An Exploratory Analysis
Sushobhan Parajuli, Vittoria Vineis, Samira Vaez Barenji +1
Exogenous events can temporarily change the relevance of items in recommender systems, but these shifts are often not visible in historical interaction data until after users have…
Overview of the TREC 2025 Product Search and Recommendation Track
Dean E. Alvarez, Surya Kallumadi, Daniel Campos +4
In the past few years, consumers have moved the bulk of their product exploration and purchasing efforts online seeking speed, convenience, and price comparison with ease unimagina…
On the Convergent Validity of Offline Evaluation Designs for Recommender Systems
Sushobhan Parajuli, Samira Vaez Barenji, Michael D. Ekstrand
Offline evaluation on historical interaction logs is the most common evaluation methodology for recommender systems. However, such evaluations depend on sparse, incomplete, or bias…
What News Recommendation Research Did (But Mostly Didn't) Teach Us About Building A News Recommender
Karl Higley, Robin Burke, Michael D. Ekstrand +1
One of the goals of recommender systems research is to provide insights and methods that can be used by practitioners to build real-world systems that deliver high-quality recommen…
We're Still Doing It (All) Wrong: Recommender Systems, Fifteen Years Later
Alan Said, Maria Soledad Pera, Michael D. Ekstrand
In 2011, Xavier Amatriain sounded the alarm: recommender systems research was "doing it all wrong" [1]. His critique, rooted in statistical misinterpretation and methodological sho…
Recommending With, Not For: Co-Designing Recommender Systems for Social Good
Michael D. Ekstrand, Afsaneh Razi, Aleksandra Sarcevic +3
Recommender systems are usually designed by engineers, researchers, designers, and other members of development teams. These systems are then evaluated based on goals set by the af…