Recommender systems have developed in parallel with the web. They were initially based on demographic, content-based and collaborative filtering. Currently, these systems are incorporating social information. In the future, they will use implicit, local and personal information from the Internet of things. This article provides an overview of recommender systems as well as collaborative filtering methods and algorithms; it also explains their evolution, provides an original classification for these systems, identifies areas of future implementation and develops certain areas selected for past, present or future importance.

http://romisatriawahono.net/lecture/rm/survey/information%20retrieval/Bobadilla%20-%20Recommender%20Systems%20-%202013.pdf

Recommender systems survey

Concept drift primarily refers to an online supervised learning scenario when the relation between the input data and the target variable changes over time. Assuming a general knowledge of supervised learning in this article, we characterize adaptive learning processes; categorize existing strategies for handling concept drift; overview the most representative, distinct, and popular techniques and algorithms; discuss evaluation methodology of adaptive algorithms; and present a set of illustrative applications. The survey covers the different facets of concept drift in an integrated way to reflect on the existing scattered state of the art. Thus, it aims at providing a comprehensive introduction to the concept drift adaptation for researchers, industry analysts, and practitioners.

/pdf/a-survey-on-concept-drift-adaptation-xv7b4z5tkr.pdf

A survey on concept drift adaptation

Matrix Factorization Techniques for Recommender Systems

Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recently advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix. Extensive experiments on three real-world datasets from different domains show that CDL can significantly advance the state of the art.

/pdf/collaborative-deep-learning-for-recommender-systems-59tevxd83s.pdf

Collaborative Deep Learning for Recommender Systems

Among different recommendation techniques, collaborative filtering usually suffer from limited performance due to the sparsity of user-item interactions. To address the issues, auxiliary information is usually used to boost the performance. Due to the rapid collection of information on the web, the knowledge base provides heterogeneous information including both structured and unstructured data with different semantics, which can be consumed by various applications. In this paper, we investigate how to leverage the heterogeneous information in a knowledge base to improve the quality of recommender systems. First, by exploiting the knowledge base, we design three components to extract items' semantic representations from structural content, textual content and visual content, respectively. To be specific, we adopt a heterogeneous network embedding method, termed as TransR, to extract items' structural representations by considering the heterogeneity of both nodes and relationships. We apply stacked denoising auto-encoders and stacked convolutional auto-encoders, which are two types of deep learning based embedding techniques, to extract items' textual representations and visual representations, respectively. Finally, we propose our final integrated framework, which is termed as Collaborative Knowledge Base Embedding (CKE), to jointly learn the latent representations in collaborative filtering as well as items' semantic representations from the knowledge base. To evaluate the performance of each embedding component as well as the whole system, we conduct extensive experiments with two real-world datasets from different scenarios. The results reveal that our approaches outperform several widely adopted state-of-the-art recommendation methods.

Collaborative Knowledge Base Embedding for Recommender Systems

Scientific documentation research leads to the computation of large amounts of information from published works of the scientific community. It is necessary to explain these processes and create application frameworks. This paper provides the following: a) An Information System designed to extract scientific information from published papers, b) Accurate explanations of the main processing stages including data mining, natural language processing, and machine learning, and c) Categorized and explained results coming from the Artificial Intelligence case study. The results in this paper include the following: a) Topics and research area rankings and b) Quantity versus quality comparisons of topics and research areas.

Analisis de la producción científica basado en las tendencias en temas de investigación. Un estudio de caso sobre inteligencia artificial

The essential purpose of this paper is to describe a framework, in a simple and complete way, which enables the concurrent visualization of audio/video streaming combined with its corresponding synchronous cooperative vectorial information. The architecture presented uses Java JMF API, which offers a simple, robust, extendable, multi-platform solution. It also requires no direct maintenance as regards the incorporation of future video cameras and codecs that come out on the market. Instead of the typical separate presentation of videoconferencing and its corresponding preset additional information, we offer the possibility of combining the video image with real-time vectorial information. All the combined bitmap and vectorial information is shared and can be interactively modified by the clients which are using the cooperative visualization framework. The current prototype RTCIM (Real Time Cooperative Information Manager) offers a complete API to implement cooperative applications based on multiple audio/video sessions.

Cooperative visualization framework based on video streaming and real-time vectorial information

espanolEn este articulo se presenta una reV1S10n de diversas publicaciones centradas en el campo de la fonetica acustica y relacionadas con estudios sobre la posicion y evolucion de los formantes del habla. En total se exponen 52 publicaciones comentadas que pueden servir como referencia para disenar nuevos experimentos de fonetica acustica, evitar repeticiones en las investigaciones y asimilar los conceptos fundamentales relacionados con el area EnglishThis artic1e presents a publication review centered in the acoustic phonetics area, more specifically in the speech formants position and evolution studies field. As a whole, 52 cornmented publications are exposed. They can serve as a reference to design new acoustic phonetics experiments, to avoid research duplications and to learn the most important concepts of the area

Posición y evaluación de los formantes del habla: Estado del arte

Real-time multimedia streaming applications are becoming increasingly popular on the Internet; however, the users of these streaming services often find their quality to be insufficient. When the services are based on multipoint videoconferencing, it is difficult to reach a constant, suitable level of quality on the video transmissions. In this paper, we propose to increase the overall quality of multipoint videoconferencing by dynamically acting on the JPEG quality parameter of each individual video conference. To do this, we assign a fuzzy agent controller to each single video conference. Since the agent’s fuzzy logic is based on frame ratios (and JPEG qualities), the videoconference qualities will be dynamically equilibrated when a bottleneck is reached in any of the hardware resources supporting the system. This work includes a complete set of tests where the results of the fuzzy agents are compared with the optimum values reached by each multipoint videoconference: the proposed fuzzy architecture provides very good dynamic control of the video conference qualities; moreover, its use will be particularly interesting in mobile environments, where the devices are heterogeneous and present limited processing capabilities.

Dynamic Quality Control Based on Fuzzy Agents for Multipoint Videoconferencing

Recommendation to groups of users is a challenging subfield of recommendation systems. Its key concept is how and where to make the aggregation of each set of user information into an individual entity, such as a ranked recommendation list, a virtual user, or a multi-hot input vector encoding. This paper proposes an innovative strategy where aggregation is made in the multi-hot vector that feeds the neural network model. The aggregation provides a probabilistic semantic, and the resulting input vectors feed a model that is able to conveniently generalize the group recommendation from the individual predictions. Furthermore, using the proposed architecture, group recommendations can be obtained by simply feedforwarding the pre-trained model with individual ratings; that is, without the need to obtain datasets containing group of user information, and without the need of running two separate trainings (individual and group). This approach also avoids maintaining two different models to support both individual and group learning. Experiments have tested the proposed architecture using three representative collaborative filtering datasets and a series of baselines; results show suitable accuracy improvements compared to the state of the art. 

/pdf/neural-group-recommendation-based-on-a-probabilistic-141dajqg.pdf

Jesús Bobadilla

Papers

Analisis de la producción científica basado en las tendencias en temas de investigación. Un estudio de caso sobre inteligencia artificial

Cooperative visualization framework based on video streaming and real-time vectorial information

Posición y evaluación de los formantes del habla: Estado del arte

Dynamic Quality Control Based on Fuzzy Agents for Multipoint Videoconferencing

Neural group recommendation based on a probabilistic semantic aggregation