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
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.
Emerging Synergies Between Large Language Models and Machine Learning in Ecommerce Recommendations
With the boom of e-commerce and web applications, recommender systems have become an important part of our daily lives, providing personalized recommendations based on the user's preferences.
Collaborative learning of common latent representations in routinely collected multivariate ICU physiological signals
In Intensive Care Units (ICU), the abundance of multivariate time series presents an opportunity for machine learning (ML) to enhance patient phenotyping.
InteraRec: Interactive Recommendations Using Multimodal Large Language Models
Weblogs, comprised of records detailing user activities on any website, offer valuable insights into user preferences, behavior, and interests.
Estimating Unknown Population Sizes Using the Hypergeometric Distribution
The multivariate hypergeometric distribution describes sampling without replacement from a discrete population of elements divided into multiple categories.
Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering
Using the graph-based collaborative filtering model as our backbone and following the same data augmentation methods as the existing contrastive learning model SGL, we effectively enhance the performance of the recommendation model.
From Variability to Stability: Advancing RecSys Benchmarking Practices
In the rapidly evolving domain of Recommender Systems (RecSys), new algorithms frequently claim state-of-the-art performance based on evaluations over a limited set of arbitrarily selected datasets.
Large Language Model Interaction Simulator for Cold-Start Item Recommendation
To address this challenge, we propose an LLM Interaction Simulator (LLM-InS) to model users' behavior patterns based on the content aspect.
Frequency-aware Graph Signal Processing for Collaborative Filtering
Graph Signal Processing (GSP) based recommendation algorithms have recently attracted lots of attention due to its high efficiency.
