基于知识的兴趣地点音乐检索

MIRUM '12 Pub Date : 2012-11-02 DOI:10.1145/2390848.2390854
Marius Kaminskas, Ignacio Fernández-Tobías, F. Ricci, Iván Cantador
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引用次数: 38

摘要

在本文中,我们解决了一个特殊的推荐任务:检索适合某个兴趣点(POI)的音乐家。我们提出了一个基于知识的框架,该框架建立在DBpedia本体之上,连接来自不同领域的项目。基于图的算法用于根据目标域(音乐)中的条目与源域(poi)中的输入条目的相关性对它们进行排序和过滤。通过用户研究,我们发现当使用一种新的加权扩散激活算法来计算音乐家和poi之间的匹配时,用户会更欣赏和判断由所提出的方法产生的有价值的建议。此外,用户认为建议的音乐家的作品适合poi。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Knowledge-based music retrieval for places of interest
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. Graph-based 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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