Improving Ontology Recommendation and Reuse in WebCORE by Collaborative Assessments

In this work, we present an extension of CORE [8], a tool for Collaborative Ontology Reuse and Evaluation. The system receives an informal description of a specific semantic domain and determines which ontologies from a repository are the most appropriate to describe the given domain. For this task, the environment is divided into three modules. The first component receives the problem description as a set of terms, and allows the user to refine and enlarge it using WordNet. The second module applies multiple automatic criteria to evaluate the ontologies of the repository, and determines which ones fit best the problem description. A ranked list of ontologies is returned for each criterion, and the lists are combined by means of rank fusion techniques. Finally, the third component uses manual user evaluations in order to incorporate a human, collaborative assessment of the ontologies. The new version of the system incorporates several novelties, such as its implementation as a web application; the incorporation of a NLP module to manage the problem definitions; modifications on the automatic ontology retrieval strategies; and a collaborative framework to find potential relevant terms according to previous user queries. Finally, we present some early experiments on ontology retrieval and evaluation, showing the benefits of our system

Content-aware Neural Hashing for Cold-start Recommendation

Content-aware recommendation approaches are essential for providing meaningful recommendations for \textit{new} (i.e., \textit{cold-start}) items in a recommender system. We present a content-aware neural hashing-based collaborative filtering approach (NeuHash-CF), which generates binary hash codes for users and items, such that the highly efficient Hamming distance can be used for estimating user-item relevance. NeuHash-CF is modelled as an autoencoder architecture, consisting of two joint hashing components for generating user and item hash codes. Inspired from semantic hashing, the item hashing component generates a hash code directly from an item's content information (i.e., it generates cold-start and seen item hash codes in the same manner). This contrasts existing state-of-the-art models, which treat the two item cases separately. The user hash codes are generated directly based on user id, through learning a user embedding matrix. We show experimentally that NeuHash-CF significantly outperforms state-of-the-art baselines by up to 12\% NDCG and 13\% MRR in cold-start recommendation settings, and up to 4\% in both NDCG and MRR in standard settings where all items are present while training. Our approach uses 2-4x shorter hash codes, while obtaining the same or better performance compared to the state of the art, thus consequently also enabling a notable storage reduction.Comment: Accepted to SIGIR 202

Prediction, Recommendation and Group Analytics Models in the domain of Mashup Services and Cyber-Argumentation Platform

Mashup application development is becoming a widespread software development practice due to its appeal for a shorter application development period. Application developers usually use web APIs from different sources to create a new streamlined service and provide various features to end-users. This kind of practice saves time, ensures reliability, accuracy, and security in the developed applications. Mashup application developers integrate these available APIs into their applications. Still, they have to go through thousands of available web APIs and chose only a few appropriate ones for their application. Recommending relevant web APIs might help application developers in this situation. However, very low API invocation from mashup applications creates a sparse mashup-web API dataset for the recommendation models to learn about the mashups and their web API invocation pattern. One research aims to analyze these mashup-specific critical issues, look for supplemental information in the mashup domain, and develop web API recommendation models for mashup applications. The developed recommendation model generates useful and accurate web APIs to reduce the impact of low API invocations in mashup application development. Cyber-Argumentation platform also faces a similarly challenging issue. In large-scale cyber argumentation platforms, participants express their opinions, engage with one another, and respond to feedback and criticism from others in discussing important issues online. Argumentation analysis tools capture the collective intelligence of the participants and reveal hidden insights from the underlying discussions. However, such analysis requires that the issues have been thoroughly discussed and participant’s opinions are clearly expressed and understood. Participants typically focus only on a few ideas and leave others unacknowledged and underdiscussed. This generates a limited dataset to work with, resulting in an incomplete analysis of issues in the discussion. One solution to this problem would be to develop an opinion prediction model for cyber-argumentation. This model would predict participant’s opinions on different ideas that they have not explicitly engaged. In cyber-argumentation, individuals interact with each other without any group coordination. However, the implicit group interaction can impact the participating user\u27s opinion, attitude, and discussion outcome. One of the objectives of this research work is to analyze different group analytics in the cyber-argumentation environment. The objective is to design an experiment to inspect whether the critical concepts of the Social Identity Model of Deindividuation Effects (SIDE) are valid in our argumentation platform. This experiment can help us understand whether anonymity and group sense impact user\u27s behavior in our platform. Another section is about developing group interaction models to help us understand different aspects of group interactions in the cyber-argumentation platform. These research works can help develop web API recommendation models tailored for mashup-specific domains and opinion prediction models for the cyber-argumentation specific area. Primarily these models utilize domain-specific knowledge and integrate them with traditional prediction and recommendation approaches. Our work on group analytic can be seen as the initial steps to understand these group interactions

Personality-based recommendation: human curiosity applied to recommendation systems using implicit information from social networks

Tesis por compendioEn el día a día, las personas suelen confiar en recomendaciones, tradicionalmente aportadas por otras personas (familia, amigos, etc.) para sus decisiones más variadas. En el mundo digital esto no es diferente, dado que los sistemas de recomendación están presentes en todas partes y de modo transparente. El principal objetivo de estos sistemas es el de ayudar en el proceso de toma de decisiones, generando recomendaciones de su interés y basadas en sus gustos. Dichas recomendaciones van desde productos en sitios web de comercio electrónico, como libros o lugares a visitar, además de qué comer o cuánto tiempo uno debe caminar al día para tener una vida sana, con quién salir o a quién seguir en las redes sociales. Esta es un área en ascensión. Por un lado, tenemos cada vez más usuarios en internet cuya vida está digitalizada, dado que lo que se hace en el "mundo real" está representado en cierto modo en el "mundo digital". Por otro lado, sufrimos una sobrecarga de información, que puede mitigarse mediante el uso de un sistema de recomendación. Sin embargo, estos sistemas también enfrentan algunos problemas, como el problema del arranque en frío y su necesidad de ser cada vez más "humanos", "personalizados" y "precisos" para satisfacer las exigencias de usuarios y empresas. En este desafiante escenario, los sistemas de recomendación basados en la personalidad se están estudiando cada vez más, ya que son capaces de enfrentar esos problemas. Algunos proyectos recientes proponen el uso de la personalidad humana en los recomendadores, ya sea en su conjunto o individualmente por rasgos. Esta tesis está dedicada a este nuevo área de recomendación basada en la personalidad, centrándose en uno de sus rasgos más importantes, la curiosidad. Además, para explotar la información ya existente en internet, obtendremos de forma implícita información de las redes sociales. Por lo tanto, este trabajo tiene como objetivo proporcionar una mejor experiencia al usuario final a través de un nuevo enfoque que ofrece una alternativa a algunos de los retos identificados en los sistemas de recomendación basados en la personalidad. Entre estas mejoras, el uso de las redes sociales para alimentar los sistemas de recomendación reduce el problema del arranque en frío y, al mismo tiempo, proporciona datos valiosos para la predicción de la personalidad humana. Por otro lado, la curiosidad no ha sido utilizada por ninguno de los sistemas de recomendación estudiados; casi todos han usado la personalidad general de un individuo a través de los Cinco Grandes rasgos de la personalidad. Sin embargo, los estudios psicológicos confirman que la curiosidad es un rasgo relevante en el proceso de elegir un item, cuestión directamente relacionada con los sistemas de recomendación. En resumen, creemos que un sistema de recomendación que mida implícitamente la curiosidad y la utilice en el proceso de recomendar nuevos ítems, especialmente en el sector turístico, podría claramente mejorar la capacidad de estos sistemas en términos de precisión, serendipidad y novedad, permitiendo a los usuarios obtener niveles positivos de satisfacción con las recomendaciones. Esta tesis realiza un estudio exhaustivo del estado del arte, donde destacamos trabajos sobre sistemas de recomendación, la personalidad humana desde el punto de vista de la psicología tradicional y positiva y finalmente cómo se combinan ambos aspectos. Luego, desarrollamos una aplicación en línea capaz de extraer implícitamente información del perfil de usuario en una red social, generando predicciones de uno o más rasgos de su personalidad. Finalmente, desarrollamos el sistema CURUMIM, capaz de generar recomendaciones en línea con diferentes propiedades, combinando la curiosidad y algunas características sociodemográficas (como el nivel de educación) extraídas de Facebook. El sistema ha sido probado y evaluado en el contexto turístico por usuarios rEn el dia a dia, les persones solen confiar en recomanacions, tradicionalment aportades per altres persones (família, amics, etc.) per a les seues decisions més variades. En el món digital això no és diferent, atès que els sistemes de recomanació estan presents a tot arreu i de manera transparent. El principal objectiu d'aquests sistemes és el d'ajudar en el procés de presa de decisions, generant recomanacions del seu interès i basades en els seus gustos. Aquestes recomanacions van des de productes en pàgines web de comerç electrònic, com a llibres o llocs a visitar, a més de què menjar o quant temps una persona ha de caminar al dia per a tindre una vida sana, amb qui eixir o a qui seguir en les xarxes socials. Aquesta és una àrea en ascensió. D'una banda, tenim cada vegada més usuaris en internet la vida de les quals està digitalitzada, atès que el que es fa en el "món real" està representat en certa manera en el "món digital". D'altra banda, patim una sobrecàrrega d'informació, que pot mitigar-se mitjançant l'ús d'un sistema de recomanació. No obstant això, aquests sistemes també enfronten alguns problemes, com el problema de l'arrencada en fred i la seua necessitat de ser cada vegada més "humans", "personalitzats" i "precisos" per a satisfer les exigències d'usuaris i empreses. En aquest desafiador escenari, els sistemes de recomanació basats en la personalitat s'estan estudiant cada vegada més, ja que són capaços d'enfrontar eixos problemes. Alguns projectes recents proposen l'ús de la personalitat humana en els recomendadors, ja siga en el seu conjunt o individualment per trets. Aquesta tesi està dedicada a aquest nou àrea de recomanació basada en la personalitat, centrant-se en un dels seus trets més importants, la curiositat. A més, per a explotar la informació ja existent en internet, obtindrem de forma implícita informació de les xarxes socials. Per tant, aquest treball té com a objectiu proporcionar una millor experiència a l'usuari final a través d'un nou enfocament que ofereix una alternativa a alguns dels reptes identificats en els sistemes de recomanació basats en la personalitat. Entre aquestes millores, l'ús de les xarxes socials per a alimentar els sistemes de recomanació redueix el problema de l'arrencada en fred i, al mateix temps, proporciona dades valuoses per a la predicció de la personalitat humana. D'altra banda, la curiositat no ha sigut utilitzada per cap dels sistemes de recomanació estudiats; quasi tots han usat la personalitat general d'un individu a través dels Cinc Grans trets de la personalitat. No obstant això, els estudis psicològics confirmen que la curiositat és un tret rellevant en el procés de triar un item, qüestió directament relacionada amb els sistemes de recomanació. En resum, creiem que un sistema de recomanació que mesure implícitament la curiositat i la utilitze en el procés de recomanar nous ítems, especialment en el sector turístic, podria clarament millorar la capacitat d'aquests sistemes en termes de precisió, sorpresa i novetat, permetent als usuaris obtindre nivells positius de satisfacció amb les recomanacions. Aquesta tesi realitza un estudi exhaustiu de l'estat de l'art, on destaquem treballs sobre sistemes de recomanació, la personalitat humana des del punt de vista de la psicologia tradicional i positiva i finalment com es combinen tots dos aspectes. Després, desenvolupem una aplicació en línia capaç d'extraure implícitament informació del perfil d'usuari en una xarxa social, generant prediccions d'un o més trets de la seua personalitat. Finalment, desenvolupem el sistema CURUMIM, capaç de generar recomanacions en línia amb diferents propietats, combinant la curiositat i algunes característiques sociodemogràfiques (com el nivell d'educació) extretes de Facebook. El sistema ha sigut provat i avaluat en el context turístic per usuaris reals. Els resultats demostren la seua capacitat perIn daily life, people usually rely on recommendations, traditionally given by other people (family, friends, etc.) for their most varied decisions. In the digital world, this is not different, given that recommender systems are present everywhere in such a way that we no longer realize. The main goal of these systems is to assist users in the decision-making process, generating recommendations that are of their interest and based on their tastes. These recommendations range from products in e-commerce websites, like books to read or places to visit to what to eat or how long one should walk a day to have a healthy life, who to date or who one should follow on social networks. And this is an increasing area. On the one hand, we have more and more users on the internet whose life is somewhat digitized, given than what one does in the "real world" is represented in a certain way in the "digital world". On the other hand, we suffer from information overload, which can be mitigated by the use of recommendation systems. However, these systems also face some problems, such as the cold start problem and their need to be more and more "human", "personalised" and "precise" in order to meet the yearning of users and companies. In this challenging scenario, personality-based recommender systems are being increasingly studied, since they are able to face these problems. Some recent projects have proposed the use of the human personality in recommenders, whether as a whole or individually by facet in order to meet those demands. Therefore, this thesis is devoted to this new area of personality-based recommendation, focusing on one of its most important traits, the curiosity. Additionally, in order to exploit the information already present on the internet, we will implicitly obtain information from social networks. Thus, this work aims to build a better experience for the end user through a new approach that offers an option for some of the gaps identified in personality-based recommendation systems. Among these gap improvements, the use of social networks to feed the recommender systems soften the cold start problem and, at the same time, it provides valuable data for the prediction of the human personality. Another found gap is that the curiosity was not used by any of the studied recommender systems; almost all of them have used the overall personality of an individual through the Big Five personality traits. However, psychological studies confirm that the curiosity is a relevant trait in the process of choosing an item, which is directly related to recommendation systems. In summary, we believe that a recommendation system that implicitly measures the curiosity and uses it in the process of recommending new items, especially in the tourism sector, could clearly improve the capacity of these systems in terms of accuracy, serendipity and novelty, allowing users to obtain positive levels of satisfaction with the recommendations. This thesis begins with an exhaustive study of the state of the art, where we highlight works about recommender systems, the human personality from the point of view of traditional and positive psychology and how these aspects are combined. Then, we develop an online application capable of implicitly extracting information from the user profile in a social network, thus generating predictions of one or more personality traits. Finally, we develop the CURUMIM system, able to generate online recommendations with different properties, combining the curiosity and some sociodemographic characteristics (such as level of education) extracted from Facebook. The system is tested and assessed within the tourism context by real users. The results demonstrate its ability to generate novel and serendipitous recommendations, while maintaining a good level of accuracy, independently of the degree of curiosity of the users.Menk Dos Santos, A. (2018). Personality-based recommendation: human curiosity applied to recommendation systems using implicit information from social networks [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/114798TESISCompendi

ディープラーニングとグラフ分析に基づく実用的なレコメンダーシステムのユーザーモデリング

Tohoku University石垣司課

Identifying experts and authoritative documents in social bookmarking systems

Social bookmarking systems allow people to create pointers to Web resources in a shared, Web-based environment. These services allow users to add free-text labels, or “tags”, to their bookmarks as a way to organize resources for later recall. Ease-of-use, low cognitive barriers, and a lack of controlled vocabulary have allowed social bookmaking systems to grow exponentially over time. However, these same characteristics also raise concerns. Tags lack the formality of traditional classificatory metadata and suffer from the same vocabulary problems as full-text search engines. It is unclear how many valuable resources are untagged or tagged with noisy, irrelevant tags. With few restrictions to entry, annotation spamming adds noise to public social bookmarking systems. Furthermore, many algorithms for discovering semantic relations among tags do not scale to the Web. Recognizing these problems, we develop a novel graph-based Expert and Authoritative Resource Location (EARL) algorithm to find the most authoritative documents and expert users on a given topic in a social bookmarking system. In EARL’s first phase, we reduce noise in a Delicious dataset by isolating a smaller sub-network of “candidate experts”, users whose tagging behavior shows potential domain and classification expertise. In the second phase, a HITS-based graph analysis is performed on the candidate experts’ data to rank the top experts and authoritative documents by topic. To identify topics of interest in Delicious, we develop a distributed method to find subsets of frequently co-occurring tags shared by many candidate experts. We evaluated EARL’s ability to locate authoritative resources and domain experts in Delicious by conducting two independent experiments. The first experiment relies on human judges’ n-point scale ratings of resources suggested by three graph-based algorithms and Google. The second experiment evaluated the proposed approach’s ability to identify classification expertise through human judges’ n-point scale ratings of classification terms versus expert-generated data

The Human Flesh Search Engine in China: a case-oriented approach to understanding online collective action

There has been a growing interest in online politics in China. The research mostly focuses on the role of the Internet in two areas, one is its creation of a public sphere and the challenges it poses to the existing communication and political system, and the other one is online censorship undertaken by Chinese authorities to reduce the scope of political discussion online and keep the domestic cyberspace from being merged with foreign cyberspace. However, some political uses of the Internet in China have tended to be overlooked. This thesis seeks to redress this lacuna in research by examining the political focus of a recent Internet phenomenon the Human Flesh Search Engine (HFSE). This term might be more at home in pages of a horror novel but was originally applied by the Chinese media to refer to the practice of online searching for people or human hunting. While existing examinations have focused on breaches of individual privacy by these so called online vigilantes this study mainly focuses on the ability of HFSE to reveal norms transgressions by public officials and lead to their removal. As the politically-focused HFSE is part of the tendency of Chinese popular protest, it is necessary to explore how the HFSE differs from and is similar to those offline protests in China. A case-oriented approach is applied to the research on HFSE. More specifically, the first part of this research puts the understanding of HFSE in Chinese historical context, with the aim of exploring the common dynamics between HFSE and those historical examples of Chinese bottom-up collective action. Then in the second part, a comparison between HFSE and recent Chinese offline popular protests is conducted in order to establish the pattern of politically-focused HFSE. In the third part, based on the empirical cases, the research on HFSE continues with an exploration of HFSE s underlying causal mechanisms to answer a key question of this research: why did HFSE occur? The study implies that there are continuities with respect to the Chinese bottom-up collective action as HFSE and Chinese rural resistances as well as urban labour strikes in the twentieth century of China tend to show similar dynamics, which are determined by the power structure they are exposed to. Moreover, the internal process of politically-focused HFSE differs largely from that of recent Chinese offline popular protests, which indicates that HFSE does not have an offline equivalent, although some of its stages can be witnessed offline. Furthermore, HFSE s occurrence is brought about by a combination of online and offline factors, which are relevant to not only the Internet and Chinese cyberspace, but also the political system that has contributed to the growth of official corruption and low government credibility in China

"I want to know what others have found interesting” : Online social filtering of news and magazine articles

As the quantity of material available on the Internet grows, problems finding the right information at the right time may follow. Recommender systems have been created to tackle this problem. This study is centered on a collaborative filtering system for news and magazine articles called Scoopinion. Scoopinion collects behavioral information about the reading habits of the users with a browser plug-in. Collaborative filtering can also be called social filtering. Reasons for the use of online social filtering of news and magazine articles and the perceptions about the process of online social filtering are investigated. Analysis was conducted using grounded theory. Research material was interviews that were conducted to ten Scoopinion-users. Interviews were semi-structured. Interviewees were all native Finns. Their ages ranged from 25 to 34. The results of the data-driven analysis were linked to the prior literature on social influence and social comparison. Findings show that online social filtering of news and magazine articles is used to gain access to material that is somehow outside individual’s normal routines of news browsing, to avert possible information overload and for entertainment in situations, where one has nothing else to do. Individuals interact with the recommendation algorithm of the Scoopinion by suggesting magazines as possible sources of recommendations or simply by reading. The reading time that the service tracks was interpreted as showing interest, but it was stated that the algorithm cannot understand whether something is evaluated as important. When a recommendation can be tied to certain individual, the perceived expertise of the recommender on the topic of the recommended article handles affects how the receiver evaluates the recommendation. Lack of clear information about the way the Scoopinion’s algorithm works led to some misunderstandings. The recommendations the service offered were in some cases falsely thought to originate partly from the reading behavior of the user’s personal social network. The most central references: On recommender systems: Schafer, J. B., Frankowski, D., Herlocker, J., & Sen, S. Collaborative filtering recommender systems (2007). On social influence: Deutsch, M., & Gerard, H. B. A study of normative and informational social influences upon individual judgment (1955); Mason, W. A., Conrey, F. R., & Smith, E. R. Situating social influence processes: Dynamic, multidirectional flows of influence within social networks (2007). On social comparison: Festinger, L. A theory of social comparison processes (1954); Suls, J., Martin, R., & Wheeler, L. Three kinds of opinion comparison: The triadic model (2000). On grounded theory: Glaser, B. Basics of Grounded Theory Analysis: Emergence vs. Forcing (1992); Strauss, A., & Corbin, J. Basics of qualitative research. Grounded theory procedures and techniques (1990).Seurauksena Internetissä olevan materiaalin kasvusta voi joskus olla vaikea löytää oikeaa tietoa oikeaan aikaan. Suosittelujärjestelmät on kehitetty avuksi tämän ongelman ratkaisemiseksi. Tämän tutkimuksen keskiössä on yhteistoiminnallista suodatustapaa käyttävä uutisten ja lehtiartikkeleiden suosittelujärjestelmä Scoopinion. Scoopinion kerää selainlisäosan avulla tietoa palvelun käyttäjien lukutottumuksista. Yhteistoiminnallista suodattamista voidaan myös kutsua sosiaaliseksi suodattamiseksi. Tutkimuksessa tarkastellaan syitä verkossa tapahtuvan uutisten ja lehtiartikkeleiden sosiaalisen suodattamisen käyttöön ja käsityksiä tästä suodattamisesta prosessina. Menetelmänä analyysissä oli grounded theory. Tutkimuksen aineistona on kymmenelle Scoopinionin käyttäjälle tehdyt haastattelut. Haastattelut olivat puolistrukturoituja. Kaikki haastateltavat olivat suomalaisia. Haastateltavien iät vaihtelivat välillä 25 ja 35. Aineistolähtöisen analyysin tulokset sidottiin aiempaan kirjallisuuteen sosiaalisesta vaikutuksesta ja sosiaalisesta vertailusta. Tuloksien mukaan verkossa tapahtuvaa uutisten ja lehtiartikkeleiden sosiaalista suodattamista käytetään omien lukurutiinien ulkopuolella olevan materiaalin saavuttamiseen, informaatioähkyn välttämiseksi ja viihteeksi tilanteissa, joissa ei ole muuta tekemistä. Scoopinionin suosittelualgoritmin kanssa ollaan vuorovaikutuksessa ehdottamalla lehtiä suositusten lähteiksi tai yksinkertaisesti lukemalla, mikäli selainlisäosa on asennettu. Palvelun seuraama lukuaika tulkittiin kiinnostuksen osoittamiseksi, mutta esille tuotiin myös, että palvelun algoritmi ei ymmärrä, onko jotakin pidetty tärkeänä. Jos suositus voidaan sitoa tiettyyn yksilöön, tämän yksilön asiantuntemus liittyen suositeltavan artikkelin aiheeseen vaikuttaa suosituksen arviointiin. Selkeän tiedon puute Scoopinionin algoritmin tavasta toimia johti joihinkin väärinkäsityksiin. Joissakin tapauksissa tehtiin väärä olettamus, jonka mukaan käyttäjän henkilökohtainen sosiaalinen verkosto vaikuttaa osaltaan palvelun tarjoamiin suosituksiin. Keskeisimmät lähteet: Suosittelujärjestelmistä: Schafer, J. B., Frankowski, D., Herlocker, J., & Sen, S. Collaborative filtering recommender systems (2007). Sosiaalisesta vaikutuksesta: Deutsch, M., & Gerard, H. B. A study of normative and informational social influences upon individual judgment (1955); Mason, W. A., Conrey, F. R., & Smith, E. R. Situating social influence processes: Dynamic, multidirectional flows of influence within social networks (2007). Sosiaalisesta vertailusta: Festinger, L. A theory of social comparison processes (1954); Suls, J., Martin, R., & Wheeler, L. Three kinds of opinion comparison: The triadic model (2000). Grounded theorysta: Glaser, B. Basics of Grounded Theory Analysis: Emergence vs. Forcing (1992); Strauss, A., & Corbin, J. Basics of qualitative research. Grounded theory procedures and techniques (1990)

Reputation-based Trust Management in Peer-to-Peer File Sharing Systems

Trust is required in file sharing peer-to-peer (P2P) systems to achieve better cooperation among peers and reduce malicious uploads. In reputation-based P2P systems, reputation is used to build trust among peers based on their past transactions and feedbacks from other peers. In these systems, reputable peers will usually be selected to upload requested files, decreasing significantly malicious uploads in the system. This thesis surveys different reputation management systems with a focus on reputation based P2P systems. We breakdown a typical reputation system into functional components. We discuss each component and present proposed solutions from the literature. Different reputation-based systems are described and analyzed. Each proposed scheme presents a particular perspective in addressing peers’ reputation. This thesis also presents a novel trust management framework and associated schemes for partially decentralized file sharing P2P systems. We address trust according to three identified dimensions: Authentic Behavior, Credibility Behavior and Contribution Behavior. Within our trust management framework, we proposed several algorithms for reputation management. In particular, we proposed algorithms to detect malicious peers that send inauthentic files, and liar peers that send wrong feedbacks. Reputable peers need to be motivated to upload authentic files by increasing the benefits received from the system. In addition, free riders need to contribute positively to the system. These peers are consuming resources without uploading to others. To provide the right incentives for peers, we develop a novel service differentiation scheme based on peers’ contribution rather than peers’ reputation. The proposed scheme protects the system against free-riders and malicious peers and reduces the service provided to them. In this thesis, we also propose a novel recommender framework for partially decentralized file sharing P2P systems. We take advantage from the partial search process used in these systems to explore the relationships between peers. The proposed recommender system does not require any additional effort from the users since implicit rating is used. The recommender system also does not suffer from the problems that affect traditional collaborative filtering schemes like the Cold start, the Data sparseness and the Popularity effect. Over all, our unified approach to trust management and recommendations allows for better system health and increased user satisfaction