Music recommendation systems based on Collaborative Filtering methods have been extensively developed over the last years. Typically, they work by analyzing the past usersong relationships, and provide informed guesses based on the overall information collected from other users. Although the music listening behavior is a repetitive and time-dependent process, these methods have not taken this into account and only consider user-song interaction for recommendation. In this work, we explore the usage of temporal context and session diversity in Session-based Collaborative Filtering techniques for music recommendation. We compared two techniques to capture the users' listening patterns over time: one explicitly extracts temporal properties and session diversity, to group and compare the similarity of sessions; the other uses a generative topic modeling algorithm, which is able to implicitly model temporal patterns. We evaluated the developed algorithms by measuring the Hit Ratio, and the Mean Reciprocal Rank. Results reveal that the inclusion of temporal information, either explicitly or implicitly, increases significantly the accuracy of the recommendation, while compared to the traditional session-based CF.

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Transactional memory systems providing snapshot isolation enable concurrent access to shared data without incurring aborts on read-write conflicts. Reducing aborts is extremely relevant as it leads to higher concurrency, greater performance, and better predictability. Unfortunately, snapshot isolation does not provide serializability as it allows certain anomalies that can lead to subtle consistency violations. While some mechanisms have been proposed to verify the correctness of a program utilizing snapshot isolation transactions, it remains difficult to repair incorrect applications. To reduce the programmer's burden in this case, we present a technique based on dynamic code and graph dependency analysis that automatically corrects existing snapshot isolation anomalies in transactional memory programs. Our evaluation shows that corrected applications retain the performance benefits characteristic of snapshot isolation over conventional transactional memory systems.

Transactional memory is a new trend in concurrency control that was boosted by the advent of multi-core processors and the near to come many-core processors. It promises the performance of finer grain with the simplicity of coarse grain threading. However, there is a clear absence of software development tools oriented to the transactional memory programming model, which is confirmed by the very small number of related scientific works published until now.This paper describes ongoing work. We propose a very low overhead monitoring framework, developed specifically for monitoring TM computations, that collects the transactional events into a single log file, sorted in a global order. This framework is then used by a visualization tool to display different types of charts from two categories: statistical charts and thread-time space diagrams. These last diagrams are interactive, allowing to identify conflicting transactions. We use the visualization tool to analyse the behavior of two different, but similar, testing applications, illustrating how it can be used to better understand the behavior of these transactional memory applications.

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Distributed transactional memory (DTM) presents itself as a highly expressive and programmer friendly model for concurrency control in distributed programming. Current DTM systems make use of both data distribution and replication as a way of providing scalability and fault tolerance, but both techniques have advantages and drawbacks. As such, each one is suitable for different target applications, and deployment environments. In this paper we address the support of different data replication models in DTM. To that end we propose ReDstm, a modular and non-intrusive framework for DTM, that supports multiple data replication models in a general purpose programming language (Java). We show its application in the implementation of distributed software transactional memories with different replication models, and evaluate the framework via a set of well-known benchmarks, analysing the impact of the different replication models on memory usage and transaction throughput.