Abstract

Abstract

Abstract
Machine learning processes consist in collecting data, obtaining a model and applying it to a given task. Given a new task, the standard approach is to restart the learning process and obtain a new model. However, previous learning experience can be exploited to assist the new learning process. The two most studied approaches for this are metalearning and transfer learning. Metalearning can be used for selecting the predictive model to use over a determined dataset. Transfer learning allows the reuse of knowledge from previous tasks. Our aim is to use metalearning to support transfer learning and reduce the computational cost without loss in terms of performance, as well as the user effort needed for the algorithm selection. In this paper we propose some methods for mapping the transfer of weights between neural networks to improve the performance of the target network, and describe some experiments performed in order to test our hypothesis.

Abstract
Classic Collaborative Filtering (CF) algorithms rely on the assumption that data are static and we usually disregard the temporal effects in natural user-generated data. These temporal effects include user preference drifts and shifts, seasonal effects, inclusion of new users, and items entering the systemand old ones leavinguser and item activity rate fluctuations and other similar time-related phenomena. These phenomena continuously change the underlying relations between users and items that recommendation algorithms essentially try to capture. In the past few years, a new generation of CF algorithms has emerged, using the time dimension as a key factor to improve recommendation models. In this overview, we present a comprehensive analysis of these algorithms and identify important challenges to be faced in the near future.(C) 2015 John Wiley & Sons, Ltd.

Abstract
We describe GPU implementations of the matrix recommender algorithms CCD++ and ALS. We compare the processing time and predictive ability of the GPU implementations with existing multi- core versions of the same algorithms. Results on the GPU are better than the results of the multi- core versions (maximum speedup of 14.8).

Abstract
Temporal information retrieval has been a topic of great interest in recent years. Its purpose is to improve the effectiveness of information retrieval methods by exploiting temporal information in documents and queries. In this article, we present a survey of the existing literature on temporal information retrieval. In addition to giving an overview of the field, we categorize the relevant research, describe the main contributions, and compare different approaches. We organize existing research to provide a coherent view, discuss several open issues, and point out some possible future research directions in this area. Despite significant advances, the area lacks a systematic arrangement of prior efforts and an overview of state-of-the-art approaches. Moreover, an effective end-to-end temporal retrieval system that exploits temporal information to improve the quality of the presented results remains undeveloped.