Content-boosted Matrix Factorization Techniques for Recommender Systems

Many businesses are using recommender systems for marketing outreach. Recommendation algorithms can be either based on content or driven by collaborative filtering. We study different ways to incorporate content information directly into the matrix factorization approach of collaborative filtering. These content-boosted matrix factorization algorithms not only improve recommendation accuracy, but also provide useful insights about the contents, as well as make recommendations more easily interpretable

When autoencoders meet recommender systems : COFILS approach

Collaborative Filtering to Supervised Learning (COFILS) transforms a Collaborative Filtering (CF) problem into classical Supervised Learning (SL) problem. Applying COFILS reduce data sparsity and make it possible to test a variety of SL algorithms rather than matrix decomposition methods. It main steps are: extraction, mapping and prediction. Firstly, a Singular Value Decomposition (SVD) generates a set of latent variables from a ratings matrix. Next, on the mapping phase, a new data set is generated where each sample contains a set of latent variables from an user and it rated item; and a target that corresponds the user rating for that item. Finally, on the last phase, a SL algorithm is applied. One problem of COFILS is it’s dependency on SVD, that is not able to extract non-linear features from data and it is not robust to noisy data. To address this problem, we propose switching SVD to a Stacked Denoising Autoencoder (SDA) on the first phase of COFILS. With SDA, more useful and complex representations can be learned in a Deep Network with a local denoising criterion. We test our novel technique, namely Deep Learning COFILS (DL-COFILS), on MovieLens, R3 Yahoo! Music and Movie Tweetings data sets and compare to COFILS, as a baseline, and state of the art CF techniques. Our results indicate that DL-COFILS outperforms COFILS for all the data sets and with an improvement up to 5.9%. Also, DL-COFILS achieves the best result for the MovieLens 100k data set and ranks on the top three algorithms for these data sets. Thus, we show that DL-COFILS represents an advance on COFILS methodology, improving it’s results and that is a suitable method for CF problem.Collaborative Filtering to Supervised Learning (COFILS) transforma um problema de filtragem colaborativa (CF) em um problema clássico de aprendizado supervisionado (SL). Sua aplicação reduz a esparsidade e torna possível a utilização de variados algoritmos de SL em oposição aos métodos de decomposição de matrizes. Primeiramente, a Decomposição em Valores Singulares (SVD) gera um conjunto de variáveis latentes a partir da matriz de avaliações. Na fase de mapeamento, um novo conjunto de dados é gerado, do qual cada amostra contém um conjunto de variáveis latentes de um usuário e do item avaliado; e um valor que corresponde a avaliação que o usuário atribuiu a esse item. Por fim, o algoritmo de SL é aplicado. Um ponto negativo do COFILS é sua dependência ao SVD, incapaz de extrair características não-lineares e sem robustez `a dados ruidosos. Nesse caso, propomos a troca do SVD por um Stacked Denoising Autoencoder (SDA). Com o uso de um SDA, representações mais úteis e complexas podem ser aprendidas em uma rede neural profunda com um critério local de remoção de ruído. Executamos nossa técnica, chamada Deep Learning COFILS (DL-COFILS), nos conjuntos de dados MovieLens, R3 Yahoo! Music e Movie Tweetings comparando os resultados com o COFILS padrão, como baseline, e demais técnicas de estado da arte de CF. Com os resultados obtidos, é possível mencionar que DL-COFILS supera COFILS para todos os conjuntos de dados, com uma melhora de até 5.9%. Além disso, o DLCOFILS alcança o melhor resultado para o MovieLens 100k e se encontra entre os três melhores algoritmos nos demais conjuntos de dados. Dessa forma, mostraremos que DL-COFILS representa um avanço na metodologia COFILS, melhorando seus resultados e se mostrando um método adequado para CF

Statistical Significance of the Netflix Challenge

Inspired by the legacy of the Netflix contest, we provide an overview of what has been learned---from our own efforts, and those of others---concerning the problems of collaborative filtering and recommender systems. The data set consists of about 100 million movie ratings (from 1 to 5 stars) involving some 480 thousand users and some 18 thousand movies; the associated ratings matrix is about 99% sparse. The goal is to predict ratings that users will give to movies; systems which can do this accurately have significant commercial applications, particularly on the world wide web. We discuss, in some detail, approaches to "baseline" modeling, singular value decomposition (SVD), as well as kNN (nearest neighbor) and neural network models; temporal effects, cross-validation issues, ensemble methods and other considerations are discussed as well. We compare existing models in a search for new models, and also discuss the mission-critical issues of penalization and parameter shrinkage which arise when the dimensions of a parameter space reaches into the millions. Although much work on such problems has been carried out by the computer science and machine learning communities, our goal here is to address a statistical audience, and to provide a primarily statistical treatment of the lessons that have been learned from this remarkable set of data.Comment: Published in at http://dx.doi.org/10.1214/11-STS368 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

The Impact of Mobile Application Information on Application Download: A Text Mining Approach

The effects of customers’ reviews on products have been understood in the initial digital purchase context. This study extends this literature by exploring the reviews in the mobile environment. It calls for understanding the context of reviews which types of information in the review have significant impact on customers’ behavior. This study applied text mining in analyzing customers’ reviews in purchasing mobile applications and find out important applications’ features valuable for customers by discovering meaningful information in influential words. We find that for mobile applications, specific expressions in online customer review, companies’ reply to the review, and quality certifications from application store have significant impacts on the number of download. Theoretical and practical implications are discussed

ROBUST LOW-RANK MATRIX FACTORIZATION WITH MISSING DATA BY MINIMIZING L1 LOSS APPLIED TO COLLABORATIVE FILTERING

In this age of information overload and plethora of choices, people increasingly rely on automatic recommender systems to tell them what suits their needs. A very effective approach for creating recommender systems is collaborative filtering, which is the task of predicting the preference/rating that a user would assign to an item based on preference data of that user and preference data of other users. One way to conduct collaborative filtering is through dimensionality reduction. The underlying concept of the approach lies in the belief that there are only a few features (reduced dimensions) that influence the user’s choice. In this paper we use low rank matrix factorization for dimensionality reduction. Singular Value Decomposition (SVD), which is minimizing the L2 norm is the most popular technique to perform matrix factorization. However, in most recommendation system data sets, often the users only rate a small amount of items, which creates missing data. As a result SVD fails. In recent years L1 norm has gained much importance and popularity because it is robust to outliers and missing data. In this thesis we use alternate convex optimization to perform L1 norm minimization to solve the matrix factorization problem and apply it to collaborative filtering. We also review some of the major challenges that collaborative filtering faces today and some of the other techniques used. Additionally, this thesis discusses the importance and future of collaborative filtering in medical applications that concerns the database of patient history (prescriptions/symptoms) and how it can be used as a predictive task for the future of the patient

An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise

Collaborative filtering based recommender systems have proven to be extremely successful in settings where user preference data on items is abundant. However, collaborative filtering algorithms are hindered by their weakness against the item cold-start problem and general lack of interpretability. Ontology-based recommender systems exploit hierarchical organizations of users and items to enhance browsing, recommendation, and profile construction. While ontology-based approaches address the shortcomings of their collaborative filtering counterparts, ontological organizations of items can be difficult to obtain for items that mostly belong to the same category (e.g., television series episodes). In this paper, we present an ontology-based recommender system that integrates the knowledge represented in a large ontology of literary themes to produce fiction content recommendations. The main novelty of this work is an ontology-based method for computing similarities between items and its integration with the classical Item-KNN (K-nearest neighbors) algorithm. As a study case, we evaluated the proposed method against other approaches by performing the classical rating prediction task on a collection of Star Trek television series episodes in an item cold-start scenario. This transverse evaluation provides insights into the utility of different information resources and methods for the initial stages of recommender system development. We found our proposed method to be a convenient alternative to collaborative filtering approaches for collections of mostly similar items, particularly when other content-based approaches are not applicable or otherwise unavailable. Aside from the new methods, this paper contributes a testbed for future research and an online framework to collaboratively extend the ontology of literary themes to cover other narrative content.Comment: 25 pages, 6 figures, 5 tables, minor revision

Recommender System with Explanations

O propósito desta dissertação é implementar um Sistema de Recomendação de estabelecimentos de comércio a utilizadores específicos, com capacidade de explicar os resultados obtidos