Offline and Online Evaluation of News Recommender Systems at swissinfo.ch

被引：92

作者：

Garcin, Florent ^{[1
]}

Faltings, Boi ^{[1
]}

Donatsch, Olivier ^{[2
]}

Alazzawi, Ayar ^{[2
]}

Bruttin, Christophe ^{[2
]}

Huber, Amr ^{[2
]}

机构：

[1] Ecole Polytech Fed Lausanne, Artificial Intelligence Lab, Lausanne, Switzerland

[2] Swiss Broadcasting Corp, SWI Swissinfo Ch, Bern, Switzerland

来源：

PROCEEDINGS OF THE 8TH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'14) | 2014年

关键词：

recommender system; news; real-time; live; evaluation;

D O I：

10.1145/2645710.2645745

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We report on the live evaluation of various news recommender systems conducted on the website swissinfo.ch. We demonstrate that there is a major difference between offline and online accuracy evaluations. In an offline setting, recommending most popular stories is the best strategy, while in a live environment this strategy is the poorest. For online setting, context-tree recommender systems which profile the users in real-time improve the click-through rate by up to 35%. The visit length also increases by a factor of 2.5. Our experience holds important lessons for the evaluation of recommender systems with offline data as well as for the use of the click-through rate as a performance indicator.

引用

页码：169 / 176

页数：8

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