Papers Collaborative Filtering

Top-N recommendations in the presence of sparsity: An NCD-based approach

Making recommendations in the presence of sparsity is known to present one of the most challenging problems faced by collaborative filtering methods. In this work we tackle this problem by exploiting the innately hierarc…

Splash: User-friendly Programming Interface for Parallelizing Stochastic Algorithms

Stochastic algorithms are efficient approaches to solving machine learning and optimization problems. In this paper, we propose a general framework called Splash for parallelizing stochastic algorithms on multi-node dist…

Defending Grey Attacks by Exploiting Wavelet Analysis in Collaborative Filtering Recommender Systems

"Shilling" attacks or "profile injection" attacks have always major challenges in collaborative filtering recommender systems (CFRSs). Many efforts have been devoted to improve collaborative filtering techniques which ca…

Detecting Abnormal Profiles in Collaborative Filtering Recommender Systems

Personalization collaborative filtering recommender systems (CFRSs) are the crucial components of popular e-commerce services. In practice, CFRSs are also particularly vulnerable to "shilling" attacks or "profile injecti…

Re-scale AdaBoost for Attack Detection in Collaborative Filtering Recommender Systems

Collaborative filtering recommender systems (CFRSs) are the key components of successful e-commerce systems. Actually, CFRSs are highly vulnerable to attacks since its openness. However, since attack size is far smaller …

Reducing offline evaluation bias of collaborative filtering algorithms

Recommendation systems have been integrated into the majority of large online systems to filter and rank information according to user profiles. It thus influences the way users interact with the system and, as a consequ…

Mining Semantic Affordances of Visual Object Categories

Affordances are fundamental attributes of objects. Affordances reveal the functionalities of objects and the possible actions that can be performed on them. Understanding affordances is crucial for recognizing human acti…

A Faster Algorithm to Build New Users Similarity List in Neighbourhood-based Collaborative Filtering

Neighbourhood-based Collaborative Filtering (CF) has been applied in the industry for several decades, because of the easy implementation and high recommendation accuracy. As the core of neighbourhood-based CF, the task …

AutoRec: Autoencoders Meet Collaborative Filtering

This paper proposes AutoRec, a novel autoencoder framework for collaborative filtering (CF). Empirically, AutoRec’s compact and efficiently trainable model outperforms stateof-the-art CF techniques (biased matrix factori…

Multi-Linear Interactive Matrix Factorization

Recommender systems, which can significantly help users find their interested items from the information era, has attracted an increasing attention from both the scientific and application society. One of the widest appl…

Blind Compressive Sensing Framework for Collaborative Filtering

Existing works based on latent factor models have focused on representing the rating matrix as a product of user and item latent factor matrices, both being dense. Latent (factor) vectors define the degree to which a tra…

Fast Differentially Private Matrix Factorization

Differentially private collaborative filtering is a challenging task, both in terms of accuracy and speed. We present a simple algorithm that is provably differentially private, while offering good performance, using a n…

A Hybrid Generative/Discriminative Approach To Citation Prediction

Collectively Embedding Multi-Relational Data for Predicting User Preferences

Matrix factorization has found incredible success and widespread application as a collaborative filtering based approach to recommendations. Unfortunately, incorporating additional sources of evidence, especially ones th…

Learning Arbitrary Statistical Mixtures of Discrete Distributions

We study the problem of learning from unlabeled samples very general statistical mixture models on large finite sets. Specifically, the model to be learned, $\vartheta$, is a probability distribution over probability dis…

Liquid FM: Recommending Music through Viscous Democracy

Most modern recommendation systems use the approach of collaborative filtering: users that are believed to behave alike are used to produce recommendations. In this work we describe an application (Liquid FM) taking a co…

Role of Matrix Factorization Model in Collaborative Filtering Algorithm: A Survey

Recommendation Systems apply Information Retrieval techniques to select the online information relevant to a given user. Collaborative Filtering is currently most widely used approach to build Recommendation System. CF t…

An Item-Based Collaborative Filtering using Dimensionality Reduction Techniques on Mahout Framework

Collaborative Filtering is the most widely used prediction technique in Recommendation System. Most of the current CF recommender systems maintains single criteria user rating in user item matrix. However, recent studies…

Label optimal regret bounds for online local learning

We resolve an open question from (Christiano, 2014b) posed in COLT'14 regarding the optimal dependency of the regret achievable for online local learning on the size of the label set. In this framework the algorithm is s…

Sequential Relevance Maximization with Binary Feedback

Motivated by online settings where users can provide explicit feedback about the relevance of products that are sequentially presented to them, we look at the recommendation process as a problem of dynamically optimizing…