ABSTRACT

In recent years, recommendation systems have started to gain significant attention and popularity. A recommendation system plays a significant role in various applications and services such as e-commerce, video streaming websites, etc. A critical task for a recommendation system is to model users’ preferences so that it can attain the capability to suggest personalized items for each user. The personalized list suggested by a suitable recommendation system should contain items highly relevant to the user. However, many a times, the traditional recommendation systems do not have enough data about the user or its peers because the model faces the cold-start problem. This work compares the existing three MAB algorithms: LinUCB, Hybrid-LinUCB, and CoLin based on evaluating regret. These algorithms are first tested on the synthetic data and then used on the real-world datasets from different areas: Yahoo Front Page Today Module, Lastfm, and MovieLens20M. The experiment results show that CoLin outperforms Hybrid-LinUBC and LinUCB, reporting cumulated regret of 8.950 for LastFm and 60.34 for MovieLens20M and 34.10 for Yahoo FrontPage Today Module.

摘要

近年来，推荐系统已开始获得广泛关注和普及。推荐系统在各种应用程序和服务（例如电子商务，视频流网站等）中起着重要作用。推荐系统的关键任务是对用户的偏好进行建模，从而使其能够为每个推荐个性化商品提供能力。用户。合适的推荐系统建议的个性化列表应包含与用户高度相关的项目。但是，很多时候，传统推荐系统没有足够的关于用户或其同级的数据，因为该模型面临着冷启动问题。这项工作基于评估遗憾，比较了现有的三种MAB算法：LinUCB，Hybrid-LinUCB和CoLin。这些算法首先在合成数据上进行测试，然后在不同领域的真实数据集上使用：Yahoo Front Page Today模块，Lastfm和MovieLens20M。实验结果表明，CoLin的性能优于Hybrid-LinUBC和LinUCB，它们的累积后悔值分别为LastFm为8.950，MovieLens20M为60.34，Yahoo FrontPage Today模块为34.10。