Hybrid Recommender Systems via Spectral Learning and a Random Forest

We demonstrate spectral learning can be combined with a random forest classifier to produce a hybrid recommender system capable of incorporating meta information. Spectral learning is supervised learning in which data is in the form of one or more networks. Responses are predicted from features obtained from the eigenvector decomposition of matrix representations of the networks. Spectral learning is based on the highest weight eigenvectors of natural Markov chain representations. A random forest is an ensemble technique for supervised learning whose internal predictive model can be interpreted as a nearest neighbor network. A hybrid recommender can be constructed by first deriving a network model from a recommender\u27s similarity matrix then applying spectral learning techniques to produce a new network model. The response learned by the new version of the recommender can be meta information. This leads to a system capable of incorporating meta data into recommendations

COLANDER: Convolving Layer Network Derivation for E-Recommendations

Many consumer facing companies have large scale data sets that they use to create recommendations for their users. These recommendations are usually based off information the company has on the user and on the item in question. Based on these two sets of features, models are created and tuned to produce the best possible recommendations. A third set of data that exists in most cases is the presence of past interactions a user may have had with other items. The relationships that a model can identify between this information and the other two types of data, we believe, can improve the prediction of how a user may interact with the given item. We propose a method that can inform the model of these relationships during the training phase while only relying on the user and item data during the prediction phase. Using ideas from convolutional neural networks (CNN) and collaborative filtering approaches, our method manipulated the weights in the first layer of our network design in a way that achieves this goal

Recommender Systems

The ongoing rapid expansion of the Internet greatly increases the necessity of effective recommender systems for filtering the abundant information. Extensive research for recommender systems is conducted by a broad range of communities including social and computer scientists, physicists, and interdisciplinary researchers. Despite substantial theoretical and practical achievements, unification and comparison of different approaches are lacking, which impedes further advances. In this article, we review recent developments in recommender systems and discuss the major challenges. We compare and evaluate available algorithms and examine their roles in the future developments. In addition to algorithms, physical aspects are described to illustrate macroscopic behavior of recommender systems. Potential impacts and future directions are discussed. We emphasize that recommendation has a great scientific depth and combines diverse research fields which makes it of interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports

PICAE – Intelligent publication of audiovisual and editorial contents

The development in internet infrastructure and technology in last tow decades have given users and retailers the possibility to purchase and sell items online. This has of course broadened the horizons of what products can be offered outside of the traditional trading sense, to the point where virtually any product can be offered. These massive online markets have had a considerable impact on the habits of consumers, providing them access to a greater variety of products and information on these goods. This variety has made online commerce into a multi-billion dollar industry but it has also put the customer in a position where it is getting increasingly difficult to select the products that best fit their individual needs. In the same vein, the rise of both availability and the amounts of data that computers have been able to process in the last decades have allowed for many solutions that are computationally expensive to exist, and recommender systems are no exception. These systems are the perfect tools to overcome the information overload problem since they provide automated and personalized suggestions to consumers. The PICAE project tackles the recommendation problem in the audiovisual sector. The vast amount of audiovisual content that is available nowadays to the user can be overwhelming, which is why recommenders have been increasingly growing in popularity in this sector ---Netflix being the biggest example. PICAE seeks to provide insightful and personalized recommendations to users in a public TV setting. The PICAE project develops new models and analytical tools for recommending audiovisual and editorial content with the aim of improving the user experience, based on their profile and environment, and the level of satisfaction and loyalty. These new tools represent a qualitative improvement in the state of the art of television and editorial content recommendation. On the other hand, the project also improves the digital consumption index of these contents based on the identification of products that these new forms of consumption demand and how they must be produced, distributed and promoted to respond to the needs of this emerging market. The main challenge of the PICAE project is to resolve two differentiating aspects with respect to other existing solutions such as: variety and dynamic contents that requires a real-time analysis of the recommendation and the lack of available information about the user, who in these areas is reluctant to register, making it difficult to identify in multi-device consumption. This document will explain the contributions made in the development of the project, which can be divided in two: the development of the project, which can be divided in two: the development of a recommender system that takes into account information of both users and items and a deep analysis of the current metrics used to assess the performance of a recommender system

Algoritmo Híbrido de Recomendação

Nesta era tecnológica em que nos encontramos há cada vez mais informação disponível na internet, mas grande parte dessa informação não é relevante. Isto leva à necessidade de criar maneiras de filtrar informação, de forma a reduzir o tempo de recolha de informação útil. Esta necessidade torna o uso de sistemas de recomendação muito apelativo, visto estes personalizarem as pesquisas de forma a ajudar os seus utilizadores a fazer escolhas mais informadas. Os sistemas de recomendação procuram recomendar os itens mais relevantes aos seus utilizadores, no entanto necessitam de informação sobre os utilizadores e os itens, de forma a melhor os poder organizar e categorizar. Há vários tipos de sistemas de recomendação, cada um com as suas forças e fraquezas. De modo a superar as limitações destes sistemas surgiram os sistemas de recomendação híbridos, que procuram combinar características dos diferentes tipos de sistemas de recomendação de modo a reduzir, ou eliminar, as suas fraquezas. Uma das limitações dos sistemas de recomendação acontece quando o próprio sistema não tem informação suficiente para fazer recomendações. Esta limitação tem o nome de Cold Start e pode focar-se numa de duas áreas: quando a falta de informação vem do utilizador, conhecida como User Cold Start; e quando a falta de informação vem de um item, conhecida como Item Cold Start. O foco desta dissertação é no User Cold Start, nomeadamente na criação de um sistema de recomendação híbrido capaz de lidar com esta situação. A abordagem apresentada nesta dissertação procura combinar a segmentação de clientes com regras de associação. O objetivo passa por descobrir os utilizadores mais similares aos utilizadores numa situação de Cold Start e, através dos itens avaliados pelos utilizadores mais similares, recomendar os itens considerados mais relevantes, obtidos através de regras de associação. O algoritmo híbrido apresentado nesta dissertação procura e classifica todos os tipos de utilizadores. Quando um utilizador numa situação de Cold Start está à procura de recomendações, o sistema encontra itens para recomendar através da aplicação de regras de associação a itens avaliados por utilizadores no mesmo grupo que o utilizador na situação de Cold Start, cruzando essas regras com os itens avaliados por este último e apresentando as recomendações com base no resultado.Recommender systems, or recommenders, are a way to filter the useful information from the data, in this age where there is a lot of available data. A recommender system’s purpose is to recommend relevant items to users, and to do that, it requires information on both, data from users and from items, to better organise and categorise both of them. There are several types of recommenders, each best suited for a specific purpose, and with specific weaknesses. Then there are hybrid recommenders, made by combining one or more types of recommenders in a way that each type supresses, or at least limits, the weaknesses of the other types. A very important weakness of recommender systems occurs when the system doesn’t have enough information about something and so, it cannot make a recommendation. This problem known as a Cold Start problem is addressed in this thesis. There are two types of Cold Start problems: those where the lack of information comes from a user (User Cold Start) and those where it comes from an item (Item Cold Start). This thesis’ main focus is on User Cold Start problems. A novel approach is introduced in this thesis which combines clients’ segmentation with association rules. The goal is first, finding the most similar users to cold start users and then, with the items rated by these similar users, recommend those that are most suitable, which are gotten through association rules. The hybrid algorithm presented in this thesis finds and classifies all users’ types. When a user in a Cold Start situation is looking for recommendations, the system finds the items to recommend to him by applying association rules to the items evaluated by users in the same user group as the Cold Start user, crossing them with the few items evaluated by the Cold Start user and finally making its recommendations based on that

Collaborative-demographic hybrid for financial: product recommendation

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsDue to the increased availability of mature data mining and analysis technologies supporting CRM processes, several financial institutions are striving to leverage customer data and integrate insights regarding customer behaviour, needs, and preferences into their marketing approach. As decision support systems assisting marketing and commercial efforts, Recommender Systems applied to the financial domain have been gaining increased attention. This thesis studies a Collaborative- Demographic Hybrid Recommendation System, applied to the financial services sector, based on real data provided by a Portuguese private commercial bank. This work establishes a framework to support account managers’ advice on which financial product is most suitable for each of the bank’s corporate clients. The recommendation problem is further developed by conducting a performance comparison for both multi-output regression and multiclass classification prediction approaches. Experimental results indicate that multiclass architectures are better suited for the prediction task, outperforming alternative multi-output regression models on the evaluation metrics considered. Withal, multiclass Feed-Forward Neural Networks, combined with Recursive Feature Elimination, is identified as the topperforming algorithm, yielding a 10-fold cross-validated F1 Measure of 83.16%, and achieving corresponding values of Precision and Recall of 84.34%, and 85.29%, respectively. Overall, this study provides important contributions for positioning the bank’s commercial efforts around customers’ future requirements. By allowing for a better understanding of customers’ needs and preferences, the proposed Recommender allows for more personalized and targeted marketing contacts, leading to higher conversion rates, corporate profitability, and customer satisfaction and loyalty