Knowledge-based music retrieval for places of interest

Kaminskas, Marius; Fernández-Tobías, Ignacio; Ricci, Francesco; Cantador, Iván

doi:10.1145/2390848.2390854

Cited by 23 publications

(16 citation statements)

References 18 publications

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“…Spreading activation is used to infer a user interest model from POIs ratings in the past. There are some studies that use Spreading Activation in recommender system (Fink and Kobsa, 2002;Middleton et al, 2004;Cantador et al, 2008Cantador et al, , 2011Sieg et al, 2010;Wang et al, 2010;Liu and Maes, 2005;BlancoFernandez et al, 2008;Kaminskas et al, 2012).…”

Section: Ontology-based User Profile Learnermentioning

confidence: 99%

An Ontology-Based Tourism Recommender System Based on Spreading Activation Model

Bahramian¹,

Abbaspour²

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:A tourist has time and budget limitations; hence, he needs to select points of interest (POIs) optimally. Since the available information about POIs is overloading, it is difficult for a tourist to select the most appreciate ones considering preferences. In this paper, a new travel recommender system is proposed to overcome information overload problem. A recommender system (RS) evaluates the overwhelming number of POIs and provides personalized recommendations to users based on their preferences. A content-based recommendation system is proposed, which uses the information about the user's preferences and POIs and calculates a degree of similarity between them. It selects POIs, which have highest similarity with the user's preferences. The proposed content-based recommender system is enhanced using the ontological information about tourism domain to represent both the user profile and the recommendable POIs. The proposed ontologybased recommendation process is performed in three steps including: ontology-based content analyzer, ontology-based profile learner, and ontology-based filtering component. User's feedback adapts the user's preferences using Spreading Activation (SA) strategy. It shows the proposed recommender system is effective and improves the overall performance of the traditional content-based recommender systems.

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Section: Ontology-based User Profile Learnermentioning

confidence: 99%

An Ontology-Based Tourism Recommender System Based on Spreading Activation Model

Bahramian¹,

Abbaspour²

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…1). We refer the reader to (Kaminskas et al, 2012) for more details on the networks building process and ranking algorithm.…”

Section: Graph-based Rankingmentioning

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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“…[26] takes several resources as inputs and performs a cross-domain processing. [11] proposes a recommendation method starting from a defined domain, with a defined type, which retrieves recommendations in another domain with another defined type. The authors tested it over DBpedia using the scenario of musical recommendations starting from tourists' attractions (e.g.…”

Section: Related Workmentioning

confidence: 99%

“…Yovisto 9 [29] is a video platform offering an exploratory search feature that proposes a ranked list of topics related to the search results. It is also noticeable that a major search player, Google 10 , launched recently an exploratory search feature ("explore your search", "things not strings" 11 ). This functionality takes advantage of the Google Knowledge Graph 11 semantic network.…”

Section: Related Workmentioning

confidence: 99%

Composite interests' exploration thanks to on-the-fly linked data spreading activation

Marie

Corby

Gandon

et al. 2013

Proceedings of the 24th ACM Conference on Hypertext and Social Media

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Exploratory search systems are built specifically to help the user in his cognitive consuming search tasks like learning or topic investigation. Some of these systems are built on the top of linked data and use semantics to provide cognitively-optimized search experiences. Thanks to their richness and to their connected nature linked data datasets can serve as a ground for advanced exploratory search. We propose to address the case of mixed interests' exploration in the form of composite queries (several unitary interests combined) e.g. exploring results and make discoveries related to both The Beatles and Ken Loach. . The main contribution of this paper is the proposition of a novel method that processes linked-data for exploratory search purpose. It makes use of a semantic spreading activation algorithm coupled with a sampling technique. Its particularity is to not require any results preprocessing. Consequently this method offers a high level of flexibility for querying and allows, among others, the expression of composite interests' queries on remote linked data sources. This paper also details the analysis of the algorithm behavior over DBpedia and describes an implementation: the Discovery Hub application. It is an exploratory search engine that notably supports composite queries. Finally the results of a user evaluation are presented.

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Knowledge-based music retrieval for places of interest

Cited by 23 publications

References 18 publications

An Ontology-Based Tourism Recommender System Based on Spreading Activation Model

An Ontology-Based Tourism Recommender System Based on Spreading Activation Model

Ontology-Based Identification of Music for Places

Composite interests' exploration thanks to on-the-fly linked data spreading activation

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