Parameterless genetic algorithms : review, comparison and improvement

This dissertation compares the performance of five existing Genetic Algorithms (GAs) that do not require the manual tuning of their parameters, and are thus called Parameterless Genetic Algorithms (pGAs). The five pGAs selected for evaluation span the three most important categories of Parameterless GAs: Deterministic, Adaptive and Self-Adaptive pGAs. The five test functions used to evaluate the performance of the pGAs include unimodal, multimodal and deceptive functions. We assess performance in terms of fitness, diversity, reliability, speed and memory load. Surprisingly , the simplest Parameterless GA tested proves to be the best overall performer. Last, but not least, we describe a new parameterless Genetic Algorithm (nGA), one that is easy to understand and implement, and which bests all five tested pGAs in terms of performance, particularly on hard and deceptive surfaces

Semantic Query Reformulation in Social PDMS

We consider social peer-to-peer data management systems (PDMS), where each peer maintains both semantic mappings between its schema and some acquaintances, and social links with peer friends. In this context, reformulating a query from a peer's schema into other peer's schemas is a hard problem, as it may generate as many rewritings as the set of mappings from that peer to the outside and transitively on, by eventually traversing the entire network. However, not all the obtained rewritings are relevant to a given query. In this paper, we address this problem by inspecting semantic mappings and social links to find only relevant rewritings. We propose a new notion of 'relevance' of a query with respect to a mapping, and, based on this notion, a new semantic query reformulation approach for social PDMS, which achieves great accuracy and flexibility. To find rapidly the most interesting mappings, we combine several techniques: (i) social links are expressed as FOAF (Friend of a Friend) links to characterize peer's friendship and compact mapping summaries are used to obtain mapping descriptions; (ii) local semantic views are special views that contain information about external mappings; and (iii) gossiping techniques improve the search of relevant mappings. Our experimental evaluation, based on a prototype on top of PeerSim and a simulated network demonstrate that our solution yields greater recall, compared to traditional query translation approaches proposed in the literature.Comment: 29 pages, 8 figures, query rewriting in PDM

P2Prec: a Social-based P2P Recommendation System for Large-scale Data Sharing

We propose P2Prec, a P2P recommendation system for large-scale data sharing, which exploits friendship links. The main idea is to recommend high quality contents related to query topics and contents of friends (or friends of friends), who are expert on the topics related to the query. Expertise is implicitly deduced based on the contents stored by a user. To exploit friendship links, we rely on Friend-Of-A-Friend (FOAF) descriptions. To disseminate information about experts, we propose new semantic-based gossip algorithms that provide scalability, robustness, simplicity and load balancing. By using information retrieval techniques, we propose an efficient query routing algorithm that recommends the best peers to serve a query. In our experimental evaluation, using the TREC09 dataset and Wiki vote social network, we show that using semantic gossiping increases recall by a factor of 2.5 compared with well known random gossiping. Furthermore, P2Prec has the ability to get reasonable recall with acceptable query processing load and network traffic

Recommandation Pair-à-Pair pour Communautés en Ligne à Grande Echelle

Recommendation systems (RS) and P2P are both complementary in easing large-scale data sharing: RS to filter and personalize users' demands, and P2P to build de-centralized large-scale data sharing systems. However, many challenges need to be overcome when building scalable, reliable and efficient RS atop P2P. In this work, we focus on large-scale communities, where users rate the con-tents they explore, and store in their local workspace high quality content related to their topics of interest. Our goal then is to provide a novel and efficient P2P-RS for this context. We exploit users' topics of interest (automatically extracted from users' contents and ratings) and social data (friendship and trust) as parameters to construct and maintain a social P2P overlay, and generate recommendations. The thesis addresses several related issues. First, we focus on the design of a scalable P2P-RS, called P2Prec, by leveraging collaborative- and content-based filter-ing recommendation approaches. We then propose the construction and maintenance of a P2P dynamic overlay using different gossip protocols. Our performance experi-mentation results show that P2Prec has the ability to get good recall with acceptable query processing load and network traffic. Second, we consider a more complex in-frastructure in order to build and maintain a social P2P overlay, called F2Frec, which exploits social relationships between users. In this new infrastructure, we leverage content- and social-based filtering, in order to get a scalable P2P-RS that yields high quality and reliable recommendation results. Based on our extensive performance evaluation, we show that F2Frec increases recall, and the trust and confidence of the results with acceptable overhead. Finally, we describe our prototype of P2P-RS, which we developed to validate our proposal based on P2Prec and F2Frec.Les systèmes de recommandation (RS) et le pair-à-pair (P2) sont complémen-taires pour faciliter le partage de données à grande échelle: RS pour filtrer et person-naliser les requêtes des utilisateurs, et P2P pour construire des systèmes de partage de données décentralisés à grande échelle. Cependant, il reste beaucoup de difficultés pour construire des RS efficaces dans une infrastructure P2P. Dans cette thèse, nous considérons des communautés en ligne à grande échelle, où les utilisateurs notent les contenus qu'ils explorent et gardent dans leur espace de travail local les contenus de qualité pour leurs sujets d'intérêt. Notre objectif est de construire un P2P-RS efficace pour ce contexte. Nous exploitons les sujets d'intérêt des utilisateurs (extraits automatiquement des contenus et de leurs notes) et les don-nées sociales (amitié et confiance) afin de construire et maintenir un overlay P2P so-cial. La thèse traite de plusieurs problèmes. D'abord, nous nous concentrons sur la conception d'un P2P-RS qui passe à l'échelle, appelé P2Prec, en combinant les ap-proches de recommandation par filtrage collaboratif et par filtrage basé sur le contenu. Nous proposons alors de construire et maintenir un overlay P2P dynamique grâce à des protocoles de gossip. Nos résultats d'expérimentation montrent que P2Prec per-met d'obtenir un bon rappel avec une charge de requêtes et un trafic réseau accep-tables. Ensuite, nous considérons une infrastructure plus complexe afin de construire et maintenir un overlay P2P social, appelé F2Frec, qui exploite les relations sociales entre utilisateurs. Dans cette infrastructure, nous combinons les aspects filtrage par contenu et filtrage basé social, pour obtenir un P2P-RS qui fournit des résultats de qualité et fiables. A l'aide d'une évaluation de performances extensive, nous mon-trons que F2Frec améliore bien le rappel, ainsi que la confiance dans les résultats avec une surcharge acceptable. Enfin, nous décrivons notre prototype de P2P-RS que nous avons implémenté pour valider notre proposition basée sur P2Prec et F2Frec

Peer-to-Peer Recommendation for Large-scale Online Communities

Les systèmes de recommandation (RS) et le pair-à-pair (P2) sont complémentaires pour faciliter le partage de données à grande échelle: RS pour filtrer et personnaliser les requêtes des utilisateurs, et P2P pour construire des systèmes de partage de données décentralisés à grande échelle. Cependant, il reste beaucoup de difficultés pour construire des RS efficaces dans une infrastructure P2P. Dans cette thèse, nous considérons des communautés en ligne à grande échelle, où les utilisateurs notent les contenus qu'ils explorent et gardent dans leur espace de travail local les contenus de qualité pour leurs sujets d'intérêt. Notre objectif est de construire un P2P-RS efficace pour ce contexte. Nous exploitons les sujets d'intérêt des utilisateurs (extraits automatiquement des contenus et de leurs notes) et les données sociales (amitié et confiance) afin de construire et maintenir un overlay P2P social. La thèse traite de plusieurs problèmes. D'abord, nous nous concentrons sur la conception d'un P2P-RS qui passe à l'échelle, appelé P2Prec, en combinant les approches de recommandation par filtrage collaboratif et par filtrage basé sur le contenu. Nous proposons alors de construire et maintenir un overlay P2P dynamique grâce à des protocoles de gossip. Nos résultats d'expérimentation montrent que P2Prec permet d'obtenir un bon rappel avec une charge de requêtes et un trafic réseau acceptables. Ensuite, nous considérons une infrastructure plus complexe afin de construire et maintenir un overlay P2P social, appelé F2Frec, qui exploite les relations sociales entre utilisateurs. Dans cette infrastructure, nous combinons les aspects filtrage par contenu et filtrage basé social, pour obtenir un P2P-RS qui fournit des résultats de qualité et fiables. A l'aide d'une évaluation de performances extensive, nous montrons que F2Frec améliore bien le rappel, ainsi que la confiance dans les résultats avec une surcharge acceptable. Enfin, nus décrivons notre prototype de P2P-RS que nous avons implémenté pour valider notre proposition basée sur P2Prec et F2Frec.Recommendation systems (RS) and P2P are both complementary in easing large-scale data sharing: RS to filter and personalize users' demands, and P2P to build decentralized large-scale data sharing systems. However, many challenges need to be overcome when building scalable, reliable and efficient RS atop P2P. In this work, we focus on large-scale communities, where users rate the contents they explore, and store in their local workspace high quality content related to their topics of interest. Our goal then is to provide a novel and efficient P2P-RS for this context. We exploit users' topics of interest (automatically extracted from users' contents and ratings) and social data (friendship and trust) as parameters to construct and maintain a social P2P overlay, and generate recommendations. The thesis addresses several related issues. First, we focus on the design of a scalable P2P-RS, called P2Prec, by leveraging collaborative- and content-based filtering recommendation approaches. We then propose the construction and maintenance of a P2P dynamic overlay using different gossip protocols. Our performance experimentation results show that P2Prec has the ability to get good recall with acceptable query processing load and network traffic. Second, we consider a more complex infrastructure in order to build and maintain a social P2P overlay, called F2Frec, which exploits social relationships between users. In this new infrastructure, we leverage content- and social-based filtering, in order to get a scalable P2P-RS that yields high quality and reliable recommendation results. Based on our extensive performance evaluation, we show that F2Frec increases recall, and the trust and confidence of the results with acceptable overhead. Finally, we describe our prototype of P2P-RS, which we developed to validate our proposal based on P2Prec and F2Frec

P2Prec: A P2P Recommendation System for Large-Scale Data Sharing

International audienceThis paper proposes P2Prec, a P2P recommendation overlay that facilitates document sharing for on-line communities. Given a query, the goal of P2PRec is to find relevant peers that can recommend documents that are relevant for the query and are of high quality. A document is relevant to a query if it covers the same topics. It is of high quality if relevant peers have rated it highly. P2PRec finds relevant peers through a variety of mechanisms including advanced content-based and collaborative filtering. The topics each peer is interested in are automatically calculated by analyzing the documents the peer holds. Peers become relevant for a topic if they hold a certain number of highly rated documents on this topic. To efficiently disseminate information about peers' topics and relevant peers, we propose new semantic-based gossip protocols. In addition, we propose an efficient query routing algorithm that selects the best peers to recommend documents based on the gossip-view entries and query topics. At the query's initiator, recommendations are selectively chosen based on similarity, rates and popularity or other recommendation criteria. In our experimental evaluation, using the TREC09 dataset, we show that using semantic gossip increases recall by a factor of 1.6 compared to well-known random gossiping. Furthermore, P2Prec has the ability to get reasonable recall with acceptable query processing load and network traffic

Peer-to-Peer Recommendation for Large-scale Online Communities

Les systèmes de recommandation (RS) et le pair-à-pair (P2) sont complémentaires pour faciliter le partage de données à grande échelle: RS pour filtrer et personnaliser les requêtes des utilisateurs, et P2P pour construire des systèmes de partage de données décentralisés à grande échelle. Cependant, il reste beaucoup de difficultés pour construire des RS efficaces dans une infrastructure P2P. Dans cette thèse, nous considérons des communautés en ligne à grande échelle, où les utilisateurs notent les contenus qu'ils explorent et gardent dans leur espace de travail local les contenus de qualité pour leurs sujets d'intérêt. Notre objectif est de construire un P2P-RS efficace pour ce contexte. Nous exploitons les sujets d'intérêt des utilisateurs (extraits automatiquement des contenus et de leurs notes) et les données sociales (amitié et confiance) afin de construire et maintenir un overlay P2P social. La thèse traite de plusieurs problèmes. D'abord, nous nous concentrons sur la conception d'un P2P-RS qui passe à l'échelle, appelé P2Prec, en combinant les approches de recommandation par filtrage collaboratif et par filtrage basé sur le contenu. Nous proposons alors de construire et maintenir un overlay P2P dynamique grâce à des protocoles de gossip. Nos résultats d'expérimentation montrent que P2Prec permet d'obtenir un bon rappel avec une charge de requêtes et un trafic réseau acceptables. Ensuite, nous considérons une infrastructure plus complexe afin de construire et maintenir un overlay P2P social, appelé F2Frec, qui exploite les relations sociales entre utilisateurs. Dans cette infrastructure, nous combinons les aspects filtrage par contenu et filtrage basé social, pour obtenir un P2P-RS qui fournit des résultats de qualité et fiables. A l'aide d'une évaluation de performances extensive, nous montrons que F2Frec améliore bien le rappel, ainsi que la confiance dans les résultats avec une surcharge acceptable. Enfin, nus décrivons notre prototype de P2P-RS que nous avons implémenté pour valider notre proposition basée sur P2Prec et F2Frec.Recommendation systems (RS) and P2P are both complementary in easing large-scale data sharing: RS to filter and personalize users' demands, and P2P to build decentralized large-scale data sharing systems. However, many challenges need to be overcome when building scalable, reliable and efficient RS atop P2P. In this work, we focus on large-scale communities, where users rate the contents they explore, and store in their local workspace high quality content related to their topics of interest. Our goal then is to provide a novel and efficient P2P-RS for this context. We exploit users' topics of interest (automatically extracted from users' contents and ratings) and social data (friendship and trust) as parameters to construct and maintain a social P2P overlay, and generate recommendations. The thesis addresses several related issues. First, we focus on the design of a scalable P2P-RS, called P2Prec, by leveraging collaborative- and content-based filtering recommendation approaches. We then propose the construction and maintenance of a P2P dynamic overlay using different gossip protocols. Our performance experimentation results show that P2Prec has the ability to get good recall with acceptable query processing load and network traffic. Second, we consider a more complex infrastructure in order to build and maintain a social P2P overlay, called F2Frec, which exploits social relationships between users. In this new infrastructure, we leverage content- and social-based filtering, in order to get a scalable P2P-RS that yields high quality and reliable recommendation results. Based on our extensive performance evaluation, we show that F2Frec increases recall, and the trust and confidence of the results with acceptable overhead. Finally, we describe our prototype of P2P-RS, which we developed to validate our proposal based on P2Prec and F2Frec.MONTPELLIER-BU Sciences (341722106) / SudocSudocFranceF

P2Prec: a Recommendation Service for P2P Content Sharing Systems

National audienceIn this paper, we propose P2Prec, a recommendation service for P2P content sharing systems that exploits users' social data. The key idea is to recommend to a user high quality documents in a specific topic using ratings of friends (or friends of friends) who are expert in that topic. To manage users' social data, we rely on Friend-Of-A-Friend (FOAF) descriptions. P2Prec has a hybrid P2P architecture to work on top of any P2P content sharing system. It combines efficient DHT indexing to manage the users' FOAF files with gossip robustness to disseminate the topics of expertise between friends. In our experimental evaluation, using the CiteSeer dataset, we show that P2Prec has the ability to get the maximum recall with very good performance. Furthermore, it increases recall and precision by a factor of 2 compared with centralized solutions

Demo of P2Prec: a Social-based P2P Recommendation System

National audienceP2Prec est un système de recommandation P2P pour le partage de contenu à grande échelle, qui exploite à la fois les contenus et les aspects sociaux. L'idée est de recommander des documents de qualité portant sur des sujets demandés dans des requêtes (par mots-clés) et des contenus d'amis (ou collègues) experts des sujets. Nous avons réalisé un prototype de P2Prec avec SON, une plate-forme open source de développements de réseaux P2P. Dans cet article, nous décrivons la démonstration des services principaux de P2Prec (installation et initialisation de pairs P2Prec, dissémination (par gossip) des sujets d'intérêt entre amis, recherche par mots-clés) avec notre prototype

P2Prec: A Social-Based P2P Recommendation System

International audienceP2Prec is a social-based P2P recommendation system for large-scale content sharing that leverages content-based and social-based recommendation. The main idea is to recommend high quality documents related to query topics and contents held by useful friends (of friends) of the users, by exploiting friendship networks. We have implemented a prototype of P2Prec using the Shared-Data Overlay Network (SON), an open source development platform for P2P networks using web services, JXTA and OSGi. In this paper, we describe the demo of P2Prec's main services (installing P2Prec peers, initializing peers, gossiping topics of interest among friends, key-word querying for contents) using our prototype implemented as an application of SON