Predicting Sparse Clients' Actions with CPOPT-Net in the Banking Environment

The digital revolution of the banking system with evolving European regulations have pushed the major banking actors to innovate by a newly use of their clients' digital information. Given highly sparse client activities, we propose CPOPT-Net, an algorithm that combines the CP canonical tensor decomposition, a multidimensional matrix decomposition that factorizes a tensor as the sum of rank-one tensors, and neural networks. CPOPT-Net removes efficiently sparse information with a gradient-based resolution while relying on neural networks for time series predictions. Our experiments show that CPOPT-Net is capable to perform accurate predictions of the clients' actions in the context of personalized recommendation. CPOPT-Net is the first algorithm to use non-linear conjugate gradient tensor resolution with neural networks to propose predictions of financial activities on a public data set

Combining Algorithms and User Experience: A Hybrid Personalized Movie Recommender Based on Perceived Similarity

Recommender systems, which filter information based on individual interests, represent a possible remedy for information overload. There are two major types of recommendation techniques—collaborative filtering and content-based. Although the content-based approach alleviates the “cold-start” problem faced by collaborative filtering, this approach generally produces lower accuracy. Thus, a hybrid strategy is often adopted. However, we identified that existing approaches are hampered by insufficient analysis of the unstructured content features of recommended products and a problematic assumption that ignores individual differences in the perception of similarity. Therefore, we propose a new recommendation framework that applies Latent Semantic Analysis to extract semantic features from unstructured text and uses Multiple Regression to identify a unique similarity weighting strategy for each user. By using a combined dataset from MovieLens and Microsoft Xbox, we developed a movie recommender as a proof-of-concept. The initial results represented a promising opportunity to combine behavioral studies and computer algorithms

A graphical shopping interface bases on product attributes

Most recommender systems present recommended products in lists to the user. By doing so, much information is lost about the mutual similarity between recommended products. We propose to represent the mutual similarities of the recommended products in a two dimensional space, where similar products are located close to each other and dissimilar products far apart. As a dissimilarity measure we use an adaptation of Gower's similarity coefficient based on the attributes of a product. Two recommender systems are developed that use this approach. The first, the graphical recommender system, uses a description given by the user in terms of product attributes of an ideal product. The second system, the graphical shopping interface, allows the user to navigate towards the product he wants. We show a prototype application of both systems to MP3-players

From timeout-based to item-by-item analysis : investigating methodologies for splitting user sessions originated from shared accounts in online platforms

Although some content providers register stream data from its users and can track their profile style for content recommendation, when two or more users share a same account, their true profile activity is obfuscated and fuzzed. This user behavior hinders the recommender systems from providers, moreover, the growing concerns on user privacy poses a risk to current models that rely on unconcealed user identity. This work proposes a way of classifying users’ stream data trough sessions, based only on its media content, opening the possibility for breaking a same account profile within multiple user profiles and consequently identifying this activity. In this work dimensionality reduction and clustering methods are used to classify user stream data into sessions that correspond to each respective user profile. Experiments show that the event-driven nature of news content can challenge the construction of a session splitting method based exclusively on content-type without user profiling.Embora as provedoras de conteúdos registram dados de acessos de seus usuários e consigam analisar seus perfis para recomendações de conteúdo, quando duas ou mais pessoas compartilham da mesma conta a atividade e perfil original e individual de cada usuário é obfuscada e difusa por essas contas compartilhadas. Este comportamento confunde os sistemas de recomendação existentes, além disso, o aumento da preocupação com a privacidade dos usuários coloca em risco os modelos atuais que são dependentes de reconhecimento explícito dos usuários. Este trabalho propõe uma maneira de classificar o fluxo de dados dos usuários em sessões baseando-se apenas em seu conteúdo, abrindo portas para quebrar a mesma conta em múltiplos perfis de usuários e consequentemente identificando esta atividade. Neste trabalho técnicas de redução de dimensionalidade e métodos de clusterização são utilizados para classificar o fluxo de dados em sessões que correspondem respectivamente a cada perfil de usuário. Experimentos mostram que a natureza guiada a eventos dos conteúdos de notícias tornam desafiador a construção de um método de quebra de sessões exclusivamente baseado em categorização de conteúdo sem perfilização de usuário

QueRIE: Collaborative Database Exploration

Interactive database exploration is a key task in information mining. However, users who lack SQL expertise or familiarity with the database schema face great difficulties in performing this task. To aid these users, we developed the QueRIE system for personalized query recommendations. QueRIE continuously monitors the user’s querying behavior and finds matching patterns in the system’s query log, in an attempt to identify previous users with similar information needs. Subsequently, QueRIE uses these “similar” users and their queries to recommend queries that the current user may find interesting. In this work we describe an instantiation of the QueRIE framework, where the active user’s session is represented by a set of query fragments. The recorded fragments are used to identify similar query fragments in the previously recorded sessions, which are in turn assembled in potentially interesting queries for the active user. We show through experimentation that the proposed method generates meaningful recommendations on real-life traces from the SkyServer database and propose a scalable design that enables the incremental update of similarities, making real-time computations on large amounts of data feasible. Finally, we compare this fragment-based instantiation with our previously proposed tuple-based instantiation discussing the advantages and disadvantages of each approach