Papers Collaborative Filtering

A Random Walk Based Model Incorporating Social Information for Recommendations

Collaborative filtering (CF) is one of the most popular approaches to build a recommendation system. In this paper, we propose a hybrid collaborative filtering model based on a Makovian random walk to address the data sp…

Wisdom of the Crowd: Incorporating Social Influence in Recommendation Models

Recommendation systems have received considerable attention recently. However, most research has been focused on improving the performance of collaborative filtering (CF) techniques. Social networks, indispensably, provi…

Collaborative Filtering and the Missing at Random Assumption

Rating prediction is an important application, and a popular research topic in collaborative filtering. However, both the validity of learning algorithms, and the validity of standard testing procedures rest on the assum…

Probabilistic Databases of Universal Schema

Evaluating the Similarity Estimator component of the TWIN Personality-based Recommender System

With the constant increase in the amount of information available in online communities, the task of building an appropriate Recommender System to support the user in her decision making process is becoming more and more…

User Participation Prediction in Online Forums

Opinion and Suggestion Analysis for Expert Recommendations

Efficient Online Learning via Randomized Rounding

Most online algorithms used in machine learning today are based on variants of mirror descent or follow-the-leader. In this paper, we present an online algorithm based on a completely different approach, which combines `…

Divide-and-Conquer Matrix Factorization

This work introduces Divide-Factor-Combine (DFC), a parallel divide-and-conquer framework for noisy matrix factorization. DFC divides a large-scale matrix factorization task into smaller subproblems, solves each subprob…

Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning

We introduce a variational Bayesian inference algorithm which can be widely applied to sparse linear models. The algorithm is based on the spike and slab prior which, from a Bayesian perspective, is the golden standard f…

Exploring Social Influence for Recommendation - A Probabilistic Generative Model Approach

In this paper, we propose a probabilistic generative model, called unified model, which naturally unifies the ideas of social influence, collaborative filtering and content-based methods for item recommendation. To addre…

Distributed Matrix Completion and Robust Factorization

If learning methods are to scale to the massive sizes of modern datasets, it is essential for the field of machine learning to embrace parallel and distributed computing. Inspired by the recent development of matrix fact…

Efficient Transductive Online Learning via Randomized Rounding

Most traditional online learning algorithms are based on variants of mirror descent or follow-the-leader. In this paper, we present an online algorithm based on a completely different approach, tailored for transductive …

Matrix completion with column manipulation: Near-optimal sample-robustness-rank tradeoffs

This paper considers the problem of matrix completion when some number of the columns are completely and arbitrarily corrupted, potentially by a malicious adversary. It is well-known that standard algorithms for matrix c…

Practical Large-Scale Optimization for Max-norm Regularization

The max-norm was proposed as a convex matrix regularizer by Srebro et al (2004) and was shown to be empirically superior to the trace-norm for collaborative filtering problems. Although the max-norm can be computed in po…

Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm

We show that matrix completion with trace-norm regularization can be significantly hurt when entries of the matrix are sampled non-uniformly, but that a properly weighted version of the trace-norm regularizer works well …

Robust PCA via Outlier Pursuit

Singular Value Decomposition (and Principal Component Analysis) is one of the most widely used techniques for dimensionality reduction: successful and efficiently computable, it is nevertheless plagued by a well-known, w…

A Contextual-Bandit Approach to Personalized News Article Recommendation

Personalized web services strive to adapt their services (advertisements, news articles, etc) to individual users by making use of both content and user information. Despite a few recent advances, this problem remains ch…

Feature-Weighted Linear Stacking

Ensemble methods, such as stacking, are designed to boost predictive accuracy by blending the predictions of multiple machine learning models. Recent work has shown that the use of meta-features, additional inputs descri…

A Gradient Descent Algorithm on the Grassman Manifold for Matrix Completion

We consider the problem of reconstructing a low-rank matrix from a small subset of its entries. In this paper, we describe the implementation of an efficient algorithm called OptSpace, based on singular value decompositi…