论文标题
在推荐系统中重新访问流行度基线
A Re-visit of the Popularity Baseline in Recommender Systems
论文作者
论文摘要
实验评估通常包括受欢迎程度，以提供推荐任务的参考性能。为了了解基线的定义和评估方式，我们从包括KDD，www，Sigir和Recys以及6个开源工具包中的顶级会议中采样了12篇论文。我们注意到，广泛采用的大多数基线只是根据培训数据中的交互作用数对项目进行排名。我们认为，当前对受欢迎程度的评估（i）并未反映用户与系统互动时流行项目，并且（ii）可能建议在用户与系统进行最后一次交互后发布的项目。在广泛使用的Movielens数据集中，我们表明，如果我们在用户与系统交互的时间点考虑流行物品，那么受欢迎程度的性能可以显着提高70％或更多。我们进一步表明，在Movielens数据集上，电影趋势较低的用户倾向于跟随人群并评价更多受欢迎的电影。电影爱好者对大量电影进行评分，根据自己的喜好和兴趣对电影进行评分。通过这项研究，我们呼吁重新参观推荐系统中普及基线的基线，以更好地反映其有效性。
Popularity is often included in experimental evaluation to provide a reference performance for a recommendation task. To understand how popularity baseline is defined and evaluated, we sample 12 papers from top-tier conferences including KDD, WWW, SIGIR, and RecSys, and 6 open source toolkits. We note that the widely adopted MostPop baseline simply ranks items based on the number of interactions in the training data. We argue that the current evaluation of popularity (i) does not reflect the popular items at the time when a user interacts with the system, and (ii) may recommend items released after a user's last interaction with the system. On the widely used MovieLens dataset, we show that the performance of popularity could be significantly improved by 70% or more, if we consider the popular items at the time point when a user interacts with the system. We further show that, on MovieLens dataset, the users having lower tendencies on movies tend to follow the crowd and rate more popular movies. Movie lovers who rate a large number of movies, rate movies based on their own preferences and interests. Through this study, we call for a re-visit of the popularity baseline in recommender system to better reflect its effectiveness.
