Jianling Wang

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 …

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 …