Evaluating Hybrid Music Recommender Systems

被引：9

作者：

Hornung, Thomas ^{[1
]}

Ziegler, Cai-Nicolas ^{[2
]}

Franz, Simon ^{[1
]}

Przyjaciel-Zablocki, Martin ^{[1
]}

Schaetzle, Alexander ^{[1
]}

Lausen, Georg ^{[1
]}

机构：

[1] Univ Freiburg, Georges Kohler Allee 51, D-79110 Freiburg, Germany

[2] PAYBACK GmbH, D-80339 Munich, Germany

来源：

2013 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 1 | 2013年

关键词：

D O I：

10.1109/WI-IAT.2013.9

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Taste in music is of highly subjective nature, making the recommending of music tracks a challenging research task. With TRecS, our live prototype system, we present a weighted hybrid recommender approach that amalgamates three diverse recommender techniques into one comprehensive score. Moreover, our system peppers the generated result list with recommendations based on a simple serendipity heuristic. This way, users can benefit from recommendations aligned with their current taste in music while gaining some exploratory diversification. An explanation feature helps the user understand the rationale behind each of the tracks being recommended to him. Empirical evaluations of the live system, based on an online evaluation, assess the overall recommendation quality as well as the impact of each of the three sub-recommenders.

引用

页码：57 / 64

页数：8

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