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Collaborative Filtering

48 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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Latest papers without code

Assessing Personally Perceived Image Quality via Image Features and Collaborative Filtering

CVPR 2019 Jari Korhonen

During the past few years, different methods for optimizing the camera settings and post-processing techniques to improve the subjective quality of consumer photos have been studied extensively.


01 Jun 2019

Evaluating recommender systems for AI-driven data science

22 May 2019William La Cava et al

The recommender system learns online as results are generated.


22 May 2019

Cleaned Similarity for Better Memory-Based Recommenders

17 May 2019Farhan Khawar et al

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.


17 May 2019

Power of the Few: Analyzing the Impact of Influential Users in Collaborative Recommender Systems

14 May 2019Farzad Eskandanian et al

Like other social systems, in collaborative filtering a small number of "influential" users may have a large impact on the recommendations of other users, thus affecting the overall behavior of the system.


14 May 2019

Embarrassingly Shallow Autoencoders for Sparse Data

8 May 2019Harald Steck

Combining simple elements from the literature, we define a linear model that is geared toward sparse data, in particular implicit feedback data for recommender systems.


08 May 2019

Deep Generative Models for Sparse, High-dimensional, and Overdispersed Discrete Data

2 May 2019He Zhao et al

In this paper, we provide an in-depth analysis on how self- and cross-excitation are modelled in existing models and propose a novel variational autoencoder framework, which is able to explicitly capture self-excitation and also better model cross-excitation.


02 May 2019

Herding Effect based Attention for Personalized Time-Sync Video Recommendation

2 May 2019Wenmian Yang et al

Time-sync comment (TSC) is a new form of user-interaction review associated with real-time video contents, which contains a user's preferences for videos and therefore well suited as the data source for video recommendations.


02 May 2019

Clustering-Based Collaborative Filtering Using an Incentivized/Penalized User Model

1 May 2019Cong Tran et al

As collaborative filtering (CF) is one of the most prominent and popular techniques used for recommender systems, we propose a new clustering-based CF (CBCF) method using an incentivized/penalized user (IPU) model only with ratings given by users, which is thus easy to implement.


01 May 2019

Collaborative Filtering via High-Dimensional Regression

30 Apr 2019Harald Steck

While the SLIM approach obtained high ranking-accuracy in many experiments in the literature, it is also known for its high computational cost of learning its parameters from data.


30 Apr 2019

Adaptive Matrix Completion for the Users and the Items in Tail

22 Apr 2019Mohit Sharma et al

In this work, we show that the skewed distribution of ratings in the user-item rating matrix of real-world datasets affects the accuracy of matrix-completion-based approaches.


22 Apr 2019