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# Collaborative Filtering Edit

60 papers with code · Miscellaneous

Collaborative filtering is a recommendation system that uses user's past behaviour (items previously purchased or selected and/or numerical ratings given to those items) as well as similar decisions made by other users. This model is then used to predict items (or ratings for items) that the user may have an interest in.

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# Latent User Linking for Collaborative Cross Domain Recommendation

19 Aug 2019

With the widespread adoption of information systems, recommender systems are widely used for better user experience.

# FCNHSMRA_HRS: Improve the performance of the movie hybrid recommender system using resource allocation approach

13 Aug 2019

Collaborative filtering offering active user suggestions based on the rating of a set of users is one of the simplest and most comprehensible and successful models for finding people in the same tastes in the recommender systems.

# CUPCF: Combining Users Preferences in Collaborative Filtering for Better Recommendation

13 Aug 2019

The experimental results based on MovieLens dataset show that, combined with the preferences of the user's nearest neighbor, the proposed system error rate compared to a number of state-of-the-art recommendation methods improved.

# Using the Open Meta Kaggle Dataset to Evaluate Tripartite Recommendations in Data Markets

12 Aug 2019

Between these entities, we identify four use cases for recommendations: (i) recommendation of datasets for users, (ii) recommendation of services for users, (iii) recommendation of services for datasets, and (iv) recommendation of datasets for services.

# Cross-Domain Collaborative Filtering via Translation-based Learning

11 Aug 2019

In our model, we learn the embedding space with translation vectors and capture high-order feature interactions in users' multiple preferences across domains.

# Iterative Collaborative Filtering for Sparse Noisy Tensor Estimation

3 Aug 2019

It turns out that $p = \Omega(n^{-3/2})$ is the conjectured lower bound as well as connectivity threshold'' of graph considered to compute similarity in our algorithm.

# MMF: Attribute Interpretable Collaborative Filtering

3 Aug 2019

Collaborative filtering is one of the most popular techniques in designing recommendation systems, and its most representative model, matrix factorization, has been wildly used by researchers and the industry.

# Scalable Bayesian Non-linear Matrix Completion

31 Jul 2019

Matrix completion aims to predict missing elements in a partially observed data matrix which in typical applications, such as collaborative filtering, is large and extremely sparsely observed.

# A difficulty ranking approach to personalization in E-learning

28 Jul 2019

EduRank constructs a difficulty ranking for each student by aggregating the rankings of similar students using different aspects of their performance on common questions.

# A Deep Learning System for Predicting Size and Fit in Fashion E-Commerce

23 Jul 2019

To alleviate this problem, we propose a deep learning based content-collaborative methodology for personalized size and fit recommendation.