Vreixo Formoso

Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems

The technique of collaborative filtering is especially successful in generating personalized recommendations. More than a decade of research has resulted in numerous algorithms, although no comparison of the different strategies has been made. In fact, a universally accepted way of evaluating a collaborative filtering algorithm does not exist yet. In this work, we compare different techniques found in the literature, and we study the characteristics of each one, highlighting their principal strengths and weaknesses. Several experiments have been performed, using the most popular metrics and algorithms. Moreover, two new metrics designed to measure the precision on good items have been proposed. The results have revealed the weaknesses of many algorithms in extracting information from user profiles especially under sparsity conditions. We have also confirmed the good results of SVD-based techniques already reported by other authors. As an alternative, we present a new approach based on the interpretation of the tendencies or differences between users and items. Despite its extraordinary simplicity, in our experiments, it obtained noticeably better results than more complex algorithms. In fact, in the cases analyzed, its results are at least equivalent to those of the best approaches studied. Under sparsity conditions, there is more than a 20% improvement in accuracy over the traditional user-based algorithms, while maintaining over 90% coverage. Moreover, it is much more efficient computationally than any other algorithm, making it especially adequate for large amounts of data.

Distributed architecture for k-nearest neighbors recommender systems

Collaborative filtering is one of the most popular recommendation techniques. While the quality of the recommendations has been significantly improved in the last years, most approaches present poor efficiency and scalability. In this paper, we study several factors that affect the performance of a k-Nearest Neighbors algorithm, and we propose a distributed architecture that significantly improves both throughput and response time. Two techniques for distributing recommender systems, user and item partition, were proposed and evaluated using that simulation model. We have found that user partition is generally better, with a faster response time and higher throughput.

Performance Analysis of Distributed Web Information Retrieval Systems

The importance and size of Web search engines is increasing daily. Information retrieval systems based on a single centralized index present several problems, which lead to the use of distributed information retrieval systems to effectively search for and locate the required information. In this study, we analyze two improvements over the brokers’ bottlenecks in a distributed information retrieval system. We demonstrate that reducing the partial results sets will improve the response time of a distributed system by 53%, with a negligible probability of changing the system’s precision and recall values. Finally, we present a simple hierarchical distributed broker model that will reduce the response times for a distributed system by 55%.

Improving k-nearest neighbors algorithms: practical application of dataset analysis

In the last years, recommender systems have achieved a great popularity. Many different techniques have been developed and applied to this field. However, in many cases the algorithms do not obtain the expected results. In particular, when the applied model does not fit the real data the results are especially bad. This happens because many times models are directly applied to a domain without a previous analysis of the data. In this work we study the most popular datasets in the movie recommendation domain, in order to understand how the users behave in this particular context. We have found some remarkable facts that question the utility of the similarity measures traditionally used in k-Nearest Neighbors (kNN) algorithms. These findings can be useful in order to develop new algorithms. In particular, we modify traditional kNN algorithms by introducing a new similarity measure specially suited for sparse contexts, where users have rated very few items. Our experiments show slight improvements in prediction accuracy, which proves the importance of a thorough dataset analysis as a previous step to any algorithm development.

Open Source Tool for Management Network Information

Although there are quite a few Open Source monitoring applications, they have not reached yet the necessary maturity level. Many users have to face important problems when deploying a monitoring system for their networks. In this paper we compare the most popular open source monitoring tools, and we analyze their main limitations. As a solution for these problems we propose a new monitoring tool, that incorporates several outstanding improves, such as a centralized configuration via web, support for monitoring templates, a hierarchical structure of objects to handle the management information, and support for centralized and distributed monitoring schemes. We describe in detail its architecture, and show its use in a real environment, which makes it possible to verify the importance of the improvements that have been developed.

Open Source Tool For Network Monitoring

Even though monitoring tools are essential to the management of communications networks, Open Source applications still confront their potential users with considerable problems. This work analyses the limitations of the currently existing tools and presents the development of a new tool that solves most of those problems. The tool is based on a new architecture of objects and remote method invocation and allows both centralized and distributed monitoring. Its configuration through web interface, its support to monitoring templates, and its flexibility make it particularly interesting for a large number of users in search Chapter 3.2 Rembassy: Open Source Tool For Network Monitoring