Collaborative Filtering
370 papers with code • 1 benchmarks • 4 datasets
Libraries
Use these libraries to find Collaborative Filtering models and implementations
Gowalla
Yelp2018
Latest papers with no code
A Strategy Transfer and Decision Support Approach for Epidemic Control in Experience Shortage Scenarios
The approach will be applied to decision-making support in the context of COVID-19.
Wasserstein Dependent Graph Attention Network for Collaborative Filtering with Uncertainty
We utilize graph attention network and Wasserstein distance to address the limitations of LightGCN and Kullback-Leibler divergence (KL) divergence to learn Gaussian embedding for each user and item.
Enhanced Bayesian Personalized Ranking for Robust Hard Negative Sampling in Recommender Systems
In implicit collaborative filtering, hard negative mining techniques are developed to accelerate and enhance the recommendation model learning.
Enhanced Generative Recommendation via Content and Collaboration Integration
However, existing generative recommendation approaches still encounter challenges in (i) effectively integrating user-item collaborative signals and item content information within a unified generative framework, and (ii) executing an efficient alignment between content information and collaborative signals.
How Does Message Passing Improve Collaborative Filtering?
A branch of research enhances CF methods by message passing used in graph neural networks, due to its strong capabilities of extracting knowledge from graph-structured data, like user-item bipartite graphs that naturally exist in CF.
All-in-One: Heterogeneous Interaction Modeling for Cold-Start Rating Prediction
Furthermore, we visualize the inferred interactions of HIRE to confirm the contribution of our model.
Large Language Models Enhanced Collaborative Filtering
In this paper, drawing inspiration from the in-context learning and chain of thought reasoning in LLMs, we propose the Large Language Models enhanced Collaborative Filtering (LLM-CF) framework, which distils the world knowledge and reasoning capabilities of LLMs into collaborative filtering.
Bilateral Unsymmetrical Graph Contrastive Learning for Recommendation
Recent methods utilize graph contrastive Learning within graph-structured user-item interaction data for collaborative filtering and have demonstrated their efficacy in recommendation tasks.
Use of recommendation models to provide support to dyslexic students
We hence implemented and trained three collaborative-filtering recommendation models, namely an item-based, a user-based and a weighted-hybrid model, and studied their performance on a large database of 1237 students' information, collected with a self-evaluating questionnaire regarding all the most used supporting strategies and digital tools.
Self-supervised Contrastive Learning for Implicit Collaborative Filtering
Contrastive learning-based recommendation algorithms have significantly advanced the field of self-supervised recommendation, particularly with BPR as a representative ranking prediction task that dominates implicit collaborative filtering.
