Papers Collaborative Filtering

Bayesian Matrix Completion via Adaptive Relaxed Spectral Regularization

Bayesian matrix completion has been studied based on a low-rank matrix factorization formulation with promising results. However, little work has been done on Bayesian matrix completion based on the more direct spectral …

Efficient Thompson Sampling for Online Matrix-Factorization Recommendation

Matrix factorization (MF) collaborative filtering is an effective and widely used method in recommendation systems. However, the problem of finding an optimal trade-off between exploration and exploitation (otherwise kno…

Collaborative Filtering with Graph Information: Consistency and Scalable Methods

Low rank matrix completion plays a fundamental role in collaborative filtering applications, the key idea being that the variables lie in a smaller subspace than the ambient space. Often, additional information about the…

A Collaborative Filtering Approach to Real-Time Hand Pose Estimation

Collaborative filtering aims to predict unknown user ratings in a recommender system by collectively assessing known user preferences. In this paper, we first draw analogies between collaborative filtering and the pose e…

Neural Network Matrix Factorization

Data often comes in the form of an array or matrix. Matrix factorization techniques attempt to recover missing or corrupted entries by assuming that the matrix can be written as the product of two low-rank matrices. In o…

Human Curation and Convnets: Powering Item-to-Item Recommendations on Pinterest

This paper presents Pinterest Related Pins, an item-to-item recommendation system that combines collaborative filtering with content-based ranking. We demonstrate that signals derived from user curation, the activity of …

Fast Collaborative Filtering from Implicit Feedback with Provable Guarantees

Building recommendation algorithms is one of the most challenging tasks in Machine Learning. Although most of the recommendation systems are built on explicit feedback available from the users in terms of rating or text,…

Some like it hot - visual guidance for preference prediction

For people first impressions of someone are of determining importance. They are hard to alter through further information. This begs the question if a computer can reach the same judgement. Earlier research has already p…

Modeling User Exposure in Recommendation

Collaborative filtering analyzes user preferences for items (e.g., books, movies, restaurants, academic papers) by exploiting the similarity patterns across users. In implicit feedback settings, all the items, including …

Learning From Missing Data Using Selection Bias in Movie Recommendation

Recommending items to users is a challenging task due to the large amount of missing information. In many cases, the data solely consist of ratings or tags voluntarily contributed by each user on a very limited subset of…

Boolean Matrix Factorization and Noisy Completion via Message Passing

Boolean matrix factorization and Boolean matrix completion from noisy observations are desirable unsupervised data-analysis methods due to their interpretability, but hard to perform due to their NP-hardness. We treat th…

Personalized Machine Translation: Predicting Translational Preferences

A Joint Model of Product Properties, Aspects and Ratings for Online Reviews

AUC Optimisation and Collaborative Filtering

In recommendation systems, one is interested in the ranking of the predicted items as opposed to other losses such as the mean squared error. Although a variety of ways to evaluate rankings exist in the literature, here …

Application of Kullback-Leibler divergence for short-term user interest detection

Classical approaches in recommender systems such as collaborative filtering are concentrated mainly on static user preference extraction. This approach works well as an example for music recommendations when a user behav…

Dynamic Matrix Factorization with Priors on Unknown Values

Advanced and effective collaborative filtering methods based on explicit feedback assume that unknown ratings do not follow the same model as the observed ones (\emph{not missing at random}). In this work, we build on th…

Regret Guarantees for Item-Item Collaborative Filtering

There is much empirical evidence that item-item collaborative filtering works well in practice. Motivated to understand this, we provide a framework to design and analyze various recommendation algorithms. The setup amou…

From Social Network to Semantic Social Network in Recommender System

Due the success of emerging Web 2.0, and different social network Web sites such as Amazon and movie lens, recommender systems are creating unprecedented opportunities to help people browsing the web when looking for rel…

Preference Completion: Large-scale Collaborative Ranking from Pairwise Comparisons

In this paper we consider the collaborative ranking setting: a pool of users each provides a small number of pairwise preferences between $d$ possible items; from these we need to predict preferences of the users for ite…

SOLAR: Scalable Online Learning Algorithms for Ranking