James Caverlee

Fairness among New Items in Cold Start Recommender Systems

2021

This paper investigates recommendation fairness among new items. While previous efforts have studied fairness in recommender systems and shown success in improving fairness, they mainly focus on scenarios where unfairness arises due to biased prior …

Fighting Mainstream Bias in Recommender Systems via Local Fine Tuning

2022 · Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

In collaborative filtering, the quality of recommendations critically relies on how easily a model can find similar users for a target user. Hence, a niche user who prefers items out of the mainstream may receive …

Mamba4Rec: Towards Efficient Sequential Recommendation with Selective State Space Models

2024 · arXiv (Cornell University)

Sequential recommendation aims to estimate the dynamic user preferences and sequential dependencies among historical user behaviors. Although Transformer-based models have proven to be effective for sequential recommendation, they suffer from the inference inefficiency problem stemming …

TAPER

2016

We address the challenge of personalized recommendation of high quality content producers in social media. While some candidates are easily identifiable (say, by being "favorited" many times), there is a long-tail of potential candidates for …

Quality-aware neural complementary item recommendation

2018

Complementary item recommendation finds products that go well with one another (e.g., a camera and a specific lens). While complementary items are ubiquitous, the dimensions by which items go together can vary by both product …

Fairness-Aware Tensor-Based Recommendation

2018

Tensor-based methods have shown promise in improving upon traditional matrix factorization methods for recommender systems. But tensors may achieve improved recommendation quality while worsening the fairness of the recommendations. Hence, we propose a novel fairness-aware …

Measuring and Mitigating Item Under-Recommendation Bias in Personalized Ranking Systems

2020

Recommendation algorithms typically build models based on user-item interactions (e.g., clicks, likes, or ratings) to provide a personalized ranked list of items. These interactions are often distributed unevenly over different groups of items due to …

Popularity-Opportunity Bias in Collaborative Filtering

2021

This paper connects equal opportunity to popularity bias in implicit recommenders to introduce the problem of popularity-opportunity bias. That is, conditioned on user preferences that a user likes both items, the more popular item is …