284 research outputs found

    Introducing linked open data in graph-based recommender systems

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    Thanks to the recent spread of the Linked Open Data (LOD) initiative, a huge amount of machine-readable knowledge encoded as RDF statements is today available in the so-called LOD cloud. Accordingly, a big effort is now spent to investigate to what extent such information can be exploited to develop new knowledge-based services or to improve the effectiveness of knowledge-intensive platforms as Recommender Systems (RS). To this end, in this article we study the impact of the exogenous knowledge coming from the LOD cloud on the overall performance of a graph-based recommendation framework. Specifically, we propose a methodology to automatically feed a graph-based RS with features gathered from the LOD cloud and we analyze the impact of several widespread feature selection techniques in such recommendation settings. The experimental evaluation, performed on three state-of-the-art datasets, provided several outcomes: first, information extracted from the LOD cloud can significantly improve the performance of a graph-based RS. Next, experiments showed a clear correlation between the choice of the feature selection technique and the ability of the algorithm to maximize specific evaluation metrics, as accuracy or diversity of the recommendations. Moreover, our graph-based algorithm fed with LOD-based features was able to overcome several baselines, as collaborative filtering and matrix factorization

    Recommending on graphs: a comprehensive review from a data perspective

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    Recent advances in graph-based learning approaches have demonstrated their effectiveness in modelling users' preferences and items' characteristics for Recommender Systems (RSS). Most of the data in RSS can be organized into graphs where various objects (e.g., users, items, and attributes) are explicitly or implicitly connected and influence each other via various relations. Such a graph-based organization brings benefits to exploiting potential properties in graph learning (e.g., random walk and network embedding) techniques to enrich the representations of the user and item nodes, which is an essential factor for successful recommendations. In this paper, we provide a comprehensive survey of Graph Learning-based Recommender Systems (GLRSs). Specifically, we start from a data-driven perspective to systematically categorize various graphs in GLRSs and analyze their characteristics. Then, we discuss the state-of-the-art frameworks with a focus on the graph learning module and how they address practical recommendation challenges such as scalability, fairness, diversity, explainability and so on. Finally, we share some potential research directions in this rapidly growing area.Comment: Accepted by UMUA

    Context-Aware Personalized Point-of-Interest Recommendation System

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    The increasing volume of information has created overwhelming challenges to extract the relevant items manually. Fortunately, the online systems, such as e-commerce (e.g., Amazon), location-based social networks (LBSNs) (e.g., Facebook) among many others have the ability to track end users\u27 browsing and consumption experiences. Such explicit experiences (e.g., ratings) and many implicit contexts (e.g., social, spatial, temporal, and categorical) are useful in preference elicitation and recommendation. As an emerging branch of information filtering, the recommendation systems are already popular in many domains, such as movies (e.g., YouTube), music (e.g., Pandora), and Point-of-Interest (POI) (e.g., Yelp). The POI domain has many contextual challenges (e.g., spatial (preferences to a near place), social (e.g., friend\u27s influence), temporal (e.g., popularity at certain time), categorical (similar preferences to places with same category), locality of POI, etc.) that can be crucial for an efficient recommendation. The user reviews shared across different social networks provide granularity in users\u27 consumption experience. From the data mining and machine learning perspective, following three research directions are identified and considered relevant to an efficient context-aware POI recommendation, (1) incorporation of major contexts into a single model and a detailed analysis of the impact of those contexts, (2) exploitation of user activity and location influence to model hierarchical preferences, and (3) exploitation of user reviews to formulate the aspect opinion relation and to generate explanation for recommendation. This dissertation presents different machine learning and data mining-based solutions to address the above-mentioned research problems, including, (1) recommendation models inspired from contextualized ranking and matrix factorization that incorporate the major contexts and help in analysis of their importance, (2) hierarchical and matrix-factorization models that formulate users\u27 activity and POI influences on different localities that model hierarchical preferences and generate individual and sequence recommendations, and (3) graphical models inspired from natural language processing and neural networks to generate recommendations augmented with aspect-based explanations

    Vertrauensbasierte Empfehlungen in mehrschichtigen Netzwerken

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    The huge interest in social networking applications - Friendster.com, for example, has more than 40 million users - led to a considerable research interest in using this data for generating recommendations. Especially recommendation techniques that analyze trust networks were found to provide very accurate and highly personalized results. The main contribution of this thesis is to extend the approach to trust-based recommendations, which up to now have been made for unlinked items such as products or movies, to linked resources, in particular documents. Therefore, a second type of network, namely a document reference network, is considered apart from the trust network. This is, for example, the citation network of scientific publications or the hyperlink graph of webpages. Recommendations for documents are typically made by reference-based visibility measures which consider a document to be the more important, the more often it is referenced by important documents. Document and trust networks, as well as further networks such as organization networks are integrated in a multi-layer network. This architecture makes it possible to combine classical measures for the visibility of a document with trust-based recommendations, giving trust-enhanced visibility measures. Moreover, an approximation approach is introduced which considers the uncertainty induced by duplicate documents. These measures are evaluated in simulation studies. The trust-based recommender system for scientific publications SPRec implements a two-layer architecture and provides personalized recommendations via a Web interface.Soziale Netzwerke mit ihren Millionen von Nutzern haben zu einem großen Interesse an der Fragestellung geführt, wie die Informationen aus solchen sozialen Netzwerken in Empfehlungssystemen genutzt werden können. Aktuelle Forschungsarbeiten haben gezeigt, dass vor allem Techniken, die soziale Vertrauensnetzwerke zur Grundlage nehmen, sehr gute Ergebnisse liefern. Die vorliegende Dissertation erweitert Ansätze zu vertrauensbasierten Empfehlungen, die bisher nur isolierte Objekte wie beispielsweise Produkte oder Filme berücksichtigt haben, zu Ansätzen für vernetzte Ressourcen, insbesondere Dokumente. Daher wird neben dem Vertrauensnetzwerk eine zweite Art von Netzwerk betrachtet, ein Dokumentennetzwerk. Beispiele für Dokumentennetzwerke sind Zitationsnetzwerke wissenschaftlicher Publikationen oder der Hyperlink-Graph zwischen Webseiten. Dokumentenempfehlungen werden typischerweise mit referenzbasierten Sichtbarkeitsmaßen berechnet, die ein Dokument als wichtig erachten, wenn es von vielen wichtigen Dokumenten referenziert wird. Vertrauensnetzwerke und Dokumentennetzwerke werden in einer zweischichtigen Architektur integriert. Weitere Netzwerke, wie zum Beispiel Organisationsnetzwerke bauen sie zu einer mehrschichtigen Architektur aus. In dieser Architektur können klassische Maße für Dokumentensichtbarkeit mit vertrauensbasierten Empfehlungen kombiniert werden, nämlich in den sogenannten vertrauensbasierten Sichtbarkeitsmaßen. Darüberhinaus führt die Dissertation einen Ansatz ein, um die vertrauensbasierte Sichtbarkeit dann approximieren zu können, wenn das Dokumentennetzwerk Duplikate von Dokumenten enthält. Die entwickelten Sichtbarkeitsmaße werden in einer Simulationsstudie analysiert. Das webbasierte Empfehlungssystem für wissenschaftliche Veröffentlichungen SPRec implementiert die vertrauensbasierten Sichtbarkeitsmaße und generiert personalisierte Empfehlungen

    Knowledge-based identification of music suited for places of interest

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    The final publication is available at Springer via http://dx.doi.org/10.1007/s40558-014-0004-xPlace is a notion closely linked with the wealth of human experience, and invested by values, attitudes, and cultural influences. In particular, many places are strongly related to music, which contributes to shaping the perception and meaning of a place. In this paper we propose a computational approach to identify musicians and music suited for a place of interest (POI)––which is based on a knowledge-based framework built upon the DBpedia ontology––and a graph-based algorithm that scores musicians with respect to their semantic relatedness with a POI and suggests the top scoring ones. Through empirical experiments we show that users appreciate and judge the musician recommendations generated by the proposed approach as valuable, and perceive compositions of the suggested musicians as suited for the POIs.This work was supported by the Spanish Government (TIN201128538C02) and the Regional Government of Madrid (S2009TIC1542)

    Knowledge-based Biomedical Data Science 2019

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    Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 table

    Workshop proceedings:CBRecSys 2014. Workshop on New Trends in Content-based Recommender Systems

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