Search Results for author:
Found 7 papers, 3 papers with code
Learning the Structure of Auto-Encoding Recommenders
In this paper, we introduce structure learning for autoencoder recommenders by taking advantage of the inherent item groups present in the collaborative filtering domain.
Cleaned Similarity for Better Memory-Based Recommenders
In this paper, we analyze the spectral properties of the Pearson and the cosine similarity estimators, and we use tools from random matrix theory to argue that they suffer from noise and eigenvalues spreading.
Matrix Factorization Equals Efficient Co-occurrence Representation
We then use insights from random matrix theory (RMT) to show that picking the top eigenvectors corresponds to removing sampling noise from user/item co-occurrence matrices.
Using Taste Groups for Collaborative Filtering
In this paper, we propose as a novel method for addressing the lack of negative examples in implicit feedback.
Learning Hierarchical Item Categories from Implicit Feedback Data for Efficient Recommendations and Browsing
Categories created in this fashion are based on users' co-consumption of items.
Conformative Filtering for Implicit Feedback Data
Implicit feedback is the simplest form of user feedback that can be used for item recommendation.
Latent Tree Models for Hierarchical Topic Detection
The variables at other levels are binary latent variables, with those at the lowest latent level representing word co-occurrence patterns and those at higher levels representing co-occurrence of patterns at the level below.
