dbrec — Music Recommendations Using DBpedia

Abstract. This paper describes the theoretical background and the im-plementation of dbrec, a music recommendation system built on top of DBpedia, offering recommendations for more than 39,000 bands and solo artists. We discuss the various challenges and lessons learnt while build-ing it, providing relevant insights for people developing applications con-suming Linked Data. Furthermore, we provide a user-centric evaluation of the system, notably by comparing it to last.fm

Tag based Video Recommendations and Re-Ranking

This paper gives a brief overview of various videos recommendation and Re-ranking techniques. It presents an advice framework which has been created to study examination addresses in the field of news feature suggestion and personalization. The framework is concentrated around semantically advanced feature information that allow look into on semantic models for flexible intelligent frameworks. It is frequently conceivable to enhance the recovery execution by repositioning the examples. We proposed a re-positioning strategy that enhances the execution of semantic feature indexing and recovery by re-assessing the scores of the shots by the homogeneity and the way of the feature they fit in with. Contradistinction with past works the proposed strategy gives a system to the re-positioning through the homogeneous circulation of feature shots content in a worldly arrangement

Video Recommendations and Re-Ranking based on Semantic Tags

This paper gives a brief overview of various videos recommendation and Re-ranking techniques. It presents an advice framework which has been created to study examination addresses in the field of news feature suggestion and personalization. The framework is concentrated around semantically advanced feature information that allow look into on semantic models for flexible intelligent frameworks. It is frequently conceivable to enhance the recovery execution by repositioning the examples. We proposed a re-positioning strategy that enhances the execution of semantic feature indexing and recovery by re-assessing the scores of the shots by the homogeneity and the way of the feature they fit in with. Contradistinction with past works the proposed strategy gives a system to the re-positioning through the homogeneous circulation of feature shots content in a worldly arrangement

Ontology-based identification of music for places

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-36309-2_37Proceedings of the International Conference on Information and Communication Technologies in Tourism in Innsbruck, Austria, January 22-25, 2013Place 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 linked to music, which contributes to shaping the perception and the meaning of a place. In this paper we propose a computational approach for identifying musicians and music suited for a place of interest (POI). We present a knowledge-based framework built upon the DBpedia ontology, and a graph-based algorithm that scores musicians with respect to their semantic relatedness to a POI and suggests the top scoring ones. We found that users appreciate and judge as valuable the musician suggestions generated by the proposed approach. Moreover, users perceived compositions of the suggested musicians as suited for the POIs

Recommender Systems based on Linked Data

Backgrounds: The increase in the amount of structured data published using the principles of Linked Data, means that now it is more likely to find resources in the Web of Data that describe real life concepts. However, discovering resources related to any given resource is still an open research area. This thesis studies Recommender Systems (RS) that use Linked Data as a source for generating recommendations exploiting the large amount of available resources and the relationships among them. Aims: The main objective of this study was to propose a recommendation tech- nique for resources considering semantic relationships between concepts from Linked Data. The specific objectives were: (i) Define semantic relationships derived from resources taking into account the knowledge found in Linked Data datasets. (ii) Determine semantic similarity measures based on the semantic relationships derived from resources. (iii) Propose an algorithm to dynami- cally generate automatic rankings of resources according to defined similarity measures. Methodology: It was based on the recommendations of the Project management Institute and the Integral Model for Engineering Professionals (Universidad del Cauca). The first one for managing the project, and the second one for developing the experimental prototype. Accordingly, the main phases were: (i) Conceptual base generation for identifying the main problems, objectives and the project scope. A Systematic Literature Review was conducted for this phase, which highlighted the relationships and similarity measures among resources in Linked Data, and the main issues, features, and types of RS based on Linked Data. (ii) Solution development is about designing and developing the experimental prototype for testing the algorithms studied in this thesis. Results: The main results obtained were: (i) The first Systematic Literature Re- view on RS based on Linked Data. (ii) A framework to execute and an- alyze recommendation algorithms based on Linked Data. (iii) A dynamic algorithm for resource recommendation based on on the knowledge of Linked Data relationships. (iv) A comparative study of algorithms for RS based on Linked Data. (v) Two implementations of the proposed framework. One with graph-based algorithms and other with machine learning algorithms. (vi) The application of the framework to various scenarios to demonstrate its feasibility within the context of real applications. Conclusions: (i) The proposed framework demonstrated to be useful for develop- ing and evaluating different configurations of algorithms to create novel RS based on Linked Data suitable to users’ requirements, applications, domains and contexts. (ii) The layered architecture of the proposed framework is also useful towards the reproducibility of the results for the research community. (iii) Linked data based RS are useful to present explanations of the recommen- dations, because of the graph structure of the datasets. (iv) Graph-based algo- rithms take advantage of intrinsic relationships among resources from Linked Data. Nevertheless, their execution time is still an open issue. Machine Learn- ing algorithms are also suitable, they provide functions useful to deal with large amounts of data, so they can help to improve the performance (execution time) of the RS. However most of them need a training phase that require to know a priory the application domain in order to obtain reliable results. (v) A log- ical evolution of RS based on Linked Data is the combination of graph-based with machine learning algorithms to obtain accurate results while keeping low execution times. However, research and experimentation is still needed to ex- plore more techniques from the vast amount of machine learning algorithms to determine the most suitable ones to deal with Linked Data

Content Recommendation Through Linked Data

Nowadays, people can easily obtain a huge amount of information from the Web, but often they have no criteria to discern it. This issue is known as information overload. Recommender systems are software tools to suggest interesting items to users and can help them to deal with a vast amount of information. Linked Data is a set of best practices to publish data on the Web, and it is the basis of the Web of Data, an interconnected global dataspace. This thesis discusses how to discover information useful for the user from the vast amount of structured data, and notably Linked Data available on the Web. The work addresses this issue by considering three research questions: how to exploit existing relationships between resources published on the Web to provide recommendations to users; how to represent the user and his context to generate better recommendations for the current situation; and how to effectively visualize the recommended resources and their relationships. To address the first question, the thesis proposes a new algorithm based on Linked Data which exploits existing relationships between resources to recommend related resources. The algorithm was integrated into a framework to deploy and evaluate Linked Data based recommendation algorithms. In fact, a related problem is how to compare them and how to evaluate their performance when applied to a given dataset. The user evaluation showed that our algorithm improves the rate of new recommendations, while maintaining a satisfying prediction accuracy. To represent the user and their context, this thesis presents the Recommender System Context ontology, which is exploited in a new context-aware approach that can be used with existing recommendation algorithms. The evaluation showed that this method can significantly improve the prediction accuracy. As regards the problem of effectively visualizing the recommended resources and their relationships, this thesis proposes a visualization framework for DBpedia (the Linked Data version of Wikipedia) and mobile devices, which is designed to be extended to other datasets. In summary, this thesis shows how it is possible to exploit structured data available on the Web to recommend useful resources to users. Linked Data were successfully exploited in recommender systems. Various proposed approaches were implemented and applied to use cases of Telecom Italia

A Hybrid Multi-strategy Recommender System Using Linked Open Data

In this paper, we discuss the development of a hybrid multi-strategy book recommendation system using Linked Open Data. Our approach builds on training individual base recommenders and using global popularity scores as generic recommenders. The results of the individual recommenders are combined using stacking regression and rank aggregation. We show that this approach delivers very good results in different recommendation settings and also allows for incorporating diversity of recommendations

A survey of exploratory search systems based on LOD resources

The fact that the existing Web allows people to effortlessly share data over the Internet has resulted in the accumulation of vast amounts of information available on the Web.Therefore, a powerful search technology that will allow retrieval of relevant information is one of the main requirements for the success of the Web which is complicated further due to use of many different formats for storing information. Semantic Web technology plays a major role in resolving this problem by permitting the search engines to retrieve meaningful information. Exploratory search system, a special information seeking and exploration approach, supports users who are unfamiliar with a topic or whose search goals are vague and unfocused to learn and investigate a topic through a set of activities. In order to achieve exploratory search goals Linked Open Data (LOD) can be used to help search systems in retrieving related data, so the investigation task runs smoothly.This paper provides an overview of the Semantic Web Technology, Linked Data and search strategies, followed by a survey of the state of the art Exploratory Search Systems based on LOD.Finally the systems are compared in various aspects such as algorithms, result rankings and explanations

Knowledge-based identification of music suited for places of interest

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)

A Systematic Literature Review of Linked Data-based Recommender Systems

Recommender Systems (RS) are software tools that use analytic technologies to suggest different items of interest to an end user. Linked Data is a set of best practices for publishing and connecting structured data on the Web. This paper presents a systematic literature review to summarize the state of the art in recommender systems that use structured data published as Linked Data for providing recommendations of items from diverse domains. It considers the most relevant research problems addressed and classifies RS according to how Linked Data has been used to provide recommendations. Furthermore, it analyzes contributions, limitations, application domains, evaluation techniques, and directions proposed for future research. We found that there are still many open challenges with regard to RS based on Linked Data in order to be efficient for real applications. The main ones are personalization of recommendations; use of more datasets considering the heterogeneity introduced; creation of new hybrid RS for adding information; definition of more advanced similarity measures that take into account the large amount of data in Linked Data datasets; and implementation of testbeds to study evaluation techniques and to assess the accuracy scalability and computational complexity of RS