Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks

Session-based recommendations are highly relevant in many modern on-line services (e.g. e-commerce, video streaming) and recommendation settings. Recently, Recurrent Neural Networks have been shown to perform very well in session-based settings. While in many session-based recommendation domains user identifiers are hard to come by, there are also domains in which user profiles are readily available. We propose a seamless way to personalize RNN models with cross-session information transfer and devise a Hierarchical RNN model that relays end evolves latent hidden states of the RNNs across user sessions. Results on two industry datasets show large improvements over the session-only RNNs

An efficient closed frequent itemset miner for the MOA stream mining system

Mining itemsets is a central task in data mining, both in the batch and the streaming paradigms. While robust, efficient, and well-tested implementations exist for batch mining, hardly any publicly available equivalent exists for the streaming scenario. The lack of an efficient, usable tool for the task hinders its use by practitioners and makes it difficult to assess new research in the area. To alleviate this situation, we review the algorithms described in the literature, and implement and evaluate the IncMine algorithm by Cheng, Ke, and Ng (2008) for mining frequent closed itemsets from data streams. Our implementation works on top of the MOA (Massive Online Analysis) stream mining framework to ease its use and integration with other stream mining tasks. We provide a PAC-style rigorous analysis of the quality of the output of IncMine as a function of its parameters; this type of analysis is rare in pattern mining algorithms. As a by-product, the analysis shows how one of the user-provided parameters in the original description can be removed entirely while retaining the performance guarantees. Finally, we experimentally confirm both on synthetic and real data the excellent performance of the algorithm, as reported in the original paper, and its ability to handle concept drift.Postprint (published version

Cross-domain recommendations without overlapping data: Myth or reality?

Cross-domain recommender systems adopt different tech- niques to transfer learning from source domain to target domain in order to alleviate the sparsity problem and im- prove accuracy of recommendations. Traditional techniques require the two domains to be linked by shared character- istics associated to either users or items. In collaborative filtering (CF) this happens when the two domains have over- lapping users or item (at least partially). Recently, Li et al. [7] introduced codebook transfer (CBT), a cross-domain CF technique based on co-clustering, and presented experimen- tal results showing that CBT is able to transfer knowledge between non-overlapping domains. In this paper, we dis- prove these results and show that CBT does not transfer knowledge when source and target domains do not overlap

Methods for frequent pattern mining in data streams within the MOA system

IncMine is a robust, efficient, practical, usable and extendable solution to perform Frequent Itemset mining over data streams. It is implementend under the Massive Online Analysis framework. It includes an analysis over its performances and its reaction to synthetic and real concept drift

50 años de institucionalidad centroamericana : Una mirada desde Sudamérica

Analizar y estudiar la integración lleva a tener en cuenta aspectos muchos más amplios que los meramente económico-comerciales. Todas las teorías que buscan explicar la integración, sino lo enuncian explícitamente, por lo menos contemplan como posibilidad que es un fenómeno político. Nuestro trabajo se enfoca en el análisis de los avances, desafíos y complejidades de las instituciones del proceso de integración centroamericano, lo cual necesariamente nos involucra en cuestiones de política las cuales se pueden tornar en temas problemáticos.Instituto de Relaciones Internacionales (IRI

50 años de institucionalidad centroamericana : Una mirada desde Sudamérica

Analizar y estudiar la integración lleva a tener en cuenta aspectos muchos más amplios que los meramente económico-comerciales. Todas las teorías que buscan explicar la integración, sino lo enuncian explícitamente, por lo menos contemplan como posibilidad que es un fenómeno político. Nuestro trabajo se enfoca en el análisis de los avances, desafíos y complejidades de las instituciones del proceso de integración centroamericano, lo cual necesariamente nos involucra en cuestiones de política las cuales se pueden tornar en temas problemáticos.Instituto de Relaciones Internacionales (IRI

Toward building a content-based video recommendation system based on low-level features

One of the challenges in video recommendation systems is the New Item problem, which happens when the system is unable to recommend video items, that no information is available about them. For example, in the popular movie-sharing websites, such as Youtube, every-day, hundred millions of hours of videos are uploaded and big portion of these videos may not contain any meta-data, to be used by the system to generate recommendations. In this paper, we address this problem by proposing a method, that is based on automatic analysis of the video content in order to extract a number representative low-level visual features. Such features are then used to generate personalized content-based recommendations. Our evaluation shows that our proposed method can outperform the baselines, by producing more relevant recommendations. Hence, a set low-level features extracted automatically can be more descriptive and informative of the video content than a set of high-level expert annotated features