dbrec — Music Recommendations Using DBpedia

Passant, Alexandre

doi:10.1007/978-3-642-17749-1_14

Cited by 135 publications

(94 citation statements)

References 9 publications

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“…Research in the the area of content-based recommendation systems have shown that incorporating semantic information is valuable for the user and leads to measurable improvements (Passant, 2010;Di Noia et al, 2012;Heitmann and Hayes, 2010). De Clercq et al (2014) incorporated semantic frames from FrameNet into the recommendation system for books.…”

Section: Related Workmentioning

confidence: 99%

Personality Profiling of Fictional Characters using Sense-Level Links between Lexical Resources

Flek

Gurevych

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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This study focuses on personality prediction of protagonists in novels based on the Five-Factor Model of personality. We present and publish a novel collaboratively built dataset of fictional character personality and design our task as a text classification problem. We incorporate a range of semantic features, including WordNet and VerbNet sense-level information and word vector representations. We evaluate three machine learning models based on the speech, actions and predicatives of the main characters, and show that especially the lexical-semantic features significantly outperform the baselines. The most predictive features correspond to reported findings in personality psychology.

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Section: Related Workmentioning

confidence: 99%

Personality Profiling of Fictional Characters using Sense-Level Links between Lexical Resources

Flek

Gurevych

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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“…Then, with such relations, users and items were incorporated into a graph, upon which a probabilistic recommendation model was built. Passant (2010) developed dbrec -a system built upon DBpedia that computes semantic distances between concepts to recommend related music bands and solo artists.…”

Section: Related Workmentioning

confidence: 99%

Ontology-Based Identification of Music for Places

Kaminskas

Fernández-Tobías

Cantador

et al. 2013

Information and Communication Technologies in Tourism 2013

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Esta es la versión de autor de la comunicación de congreso publicada en: This is an author produced version of a paper published in: Abstract: Place 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.

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“…Then, with such relations, users and items were incorporated into a graph, upon which a probabilistic recommendation model was built. Passant [12] developed dbrec -a system built upon DBpedia that computes semantic distances between concepts to recommend related music bands and solo artists.…”

Section: Related Workmentioning

confidence: 99%

Knowledge-based music retrieval for places of interest

Kaminskas

Fernández-Tobías

Ricci

et al. 2012

Proceedings of the Second International ACM Workshop on Music Information Retrieval With User-Centered and Multimodal Strategie

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In this paper we address a particular recommendation task: retrieving musicians suited for a place of interest (POI). We present a knowledge-based framework built upon the DBpedia ontology linking items from different domains. Graphbased algorithms are used for ranking and filtering items in a target domain (music) with respect to their relatedness to an input item in a source domain (POIs). By conducting user studies we found that users appreciate and judge more valuable the suggestions generated by the proposed approach when a novel weight spreading activation algorithm is used to compute the matching between musicians and POIs. Moreover, users perceive compositions of the suggested musicians as suited for the POIs.

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dbrec — Music Recommendations Using DBpedia

Cited by 135 publications

References 9 publications

Personality Profiling of Fictional Characters using Sense-Level Links between Lexical Resources

Personality Profiling of Fictional Characters using Sense-Level Links between Lexical Resources

Ontology-Based Identification of Music for Places

Knowledge-based music retrieval for places of interest

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