Papers Collaborative Filtering

Improving Collaborative Filtering based Recommenders using Topic Modelling

Standard Collaborative Filtering (CF) algorithms make use of interactions between users and items in the form of implicit or explicit ratings alone for generating recommendations. Similarity among users or items is calcu…

Low-Rank Modeling and Its Applications in Image Analysis

Low-rank modeling generally refers to a class of methods that solve problems by representing variables of interest as low-rank matrices. It has achieved great success in various fields including computer vision, data min…

Bayesian Models of Graphs, Arrays and Other Exchangeable Random Structures

The natural habitat of most Bayesian methods is data represented by exchangeable sequences of observations, for which de Finetti's theorem provides the theoretical foundation. Dirichlet process clustering, Gaussian proce…

Time-varying Learning and Content Analytics via Sparse Factor Analysis

We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for education applications. We develop a novel message passing-based, blind, approximate Kalman filter for s…

Missing Value Imputation With Unsupervised Backpropagation

Many data mining and data analysis techniques operate on dense matrices or complete tables of data. Real-world data sets, however, often contain unknown values. Even many classification algorithms that are designed to op…

Exact and Stable Recovery of Pairwise Interaction Tensors

Tensor completion from incomplete observations is a problem of significant practical interest. However, it is unlikely that there exists an efficient algorithm with provable guarantee to recover a general tensor from a …

Deep content-based music recommendation

Automatic music recommendation has become an increasingly relevant problem in recent years, since a lot of music is now sold and consumed digitally. Most recommender systems rely on collaborative filtering. However, this…

An FCA-based Boolean Matrix Factorisation for Collaborative Filtering

We propose a new approach for Collaborative Filtering which is based on Boolean Matrix Factorisation (BMF) and Formal Concept Analysis. In a series of experiments on real data (Movielens dataset) we compare the approach …

Well-Argued Recommendation: Adaptive Models Based on Words in Recommender Systems

One-class Collaborative Filtering with Random Graphs: Annotated Version

The bane of one-class collaborative filtering is interpreting and modelling the latent signal from the missing class. In this paper we present a novel Bayesian generative model for implicit collaborative filtering. It fo…

A Random Walk Approach to Selectional Preferences Based on Preference Ranking and Propagation

Mining User Relations from Online Discussions using Sentiment Analysis and Probabilistic Matrix Factorization

Personalized Academic Research Paper Recommendation System

A huge number of academic papers are coming out from a lot of conferences and journals these days. In these circumstances, most researchers rely on key-based search or browsing through proceedings of top conferences and …

Efficient Computation of Mean Truncated Hitting Times on Very Large Graphs

Previous work has shown the effectiveness of random walk hitting times as a measure of dissimilarity in a variety of graph-based learning problems such as collaborative filtering, query suggestion or finding paraphrases.…

Fast methods for denoising matrix completion formulations, with applications to robust seismic data interpolation

Recent SVD-free matrix factorization formulations have enabled rank minimization for systems with millions of rows and columns, paving the way for matrix completion in extremely large-scale applications, such as seismic …

Exploiting Social Tags for Cross-Domain Collaborative Filtering

One of the most challenging problems in recommender systems based on the collaborative filtering (CF) concept is data sparseness, i.e., limited user preference data is available for making recommendations. Cross-domain c…

Empirical Analysis of Predictive Algorithms for Collaborative Filtering

Collaborative filtering or recommender systems use a database about user preferences to predict additional topics or products a new user might like. In this paper we describe several algorithms designed for this task, in…

CFW: A Collaborative Filtering System Using Posteriors Over Weights Of Evidence

We describe CFW, a computationally efficient algorithm for collaborative filtering that uses posteriors over weights of evidence. In experiments on real data, we show that this method predicts as well or better than othe…

Automatic Feature Induction for Stagewise Collaborative Filtering

Recent approaches to collaborative filtering have concentrated on estimating an algebraic or statistical model, and using the model for predicting missing ratings. In this paper we observe that different models have rela…

Learning Label Trees for Probabilistic Modelling of Implicit Feedback

User preferences for items can be inferred from either explicit feedback, such as item ratings, or implicit feedback, such as rental histories. Research in collaborative filtering has concentrated on explicit feedback, r…