7 citations · 23 across the 8 of their papers we have counts for
5 papers · 1 filter
Minimizing Mindless Mentions: Recommendation with Minimal Necessary User Reviews
Danny Stax, Manel Slokom, Martha Larson
Recently, researchers have turned their attention to recommender systems that use only minimal necessary data. This trend is informed by the idea that recommender systems should us…
Doing Data Right: How Lessons Learned Working with Conventional Data should Inform the Future of Synthetic Data for Recommender Systems
Manel Slokom, Martha Larson
We present a case that the newly emerging field of synthetic data in the area of recommender systems should prioritize `doing data right'. We consider this catchphrase to have two…
Partially Synthetic Data for Recommender Systems: Prediction Performance and Preference Hiding
Manel Slokom, Martha Larson, Alan Hanjalic
This paper demonstrates the potential of statistical disclosure control for protecting the data used to train recommender systems. Specifically, we use a synthetic data generation…
Adversarial Item Promotion: Vulnerabilities at the Core of Top-N Recommenders that Use Images to Address Cold Start
Zhuoran Liu, Martha Larson
E-commerce platforms provide their customers with ranked lists of recommended items matching the customers' preferences. Merchants on e-commerce platforms would like their items to…
Factorization Machines for Data with Implicit Feedback
Babak Loni, Martha Larson, Alan Hanjalic
In this work, we propose FM-Pair, an adaptation of Factorization Machines with a pairwise loss function, making them effective for datasets with implicit feedback. The optimization…