Search Results for author:
Found 2 papers, 1 papers with code
User Welfare Optimization in Recommender Systems with Competing Content Creators
Driven by the new economic opportunities created by the creator economy, an increasing number of content creators rely on and compete for revenue generated from online content recommendation platforms.
Regularizing Matrix Factorization with User and Item Embeddings for Recommendation
Following recent successes in exploiting both latent factor and word embedding models in recommendation, we propose a novel Regularized Multi-Embedding (RME) based recommendation model that simultaneously encapsulates the following ideas via decomposition: (1) which items a user likes, (2) which two users co-like the same items, (3) which two items users often co-liked, and (4) which two items users often co-disliked.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
