Wang Chang-Dong, Deng Zhi-Hong, Lai Jian-Huang, Yu Philip S
IEEE Trans Cybern. 2019 Jul;49(7):2678-2692. doi: 10.1109/TCYB.2018.2841924. Epub 2018 Jun 21.
Collaborative filtering (CF) algorithms have been widely used to build recommender systems since they have distinguishing capability of sharing collective wisdoms and experiences. However, they may easily fall into the trap of the Matthew effect, which tends to recommend popular items and hence less popular items become increasingly less popular. Under this circumstance, most of the items in the recommendation list are already familiar to users and therefore the performance would seriously degenerate in finding cold items, i.e., new items and niche items. To address this issue, in this paper, a user survey is first conducted on the online shopping habits in China, based on which a novel recommendation algorithm termed innovator-based CF is proposed that can recommend cold items to users by introducing the concept of innovators. Specifically, innovators are a special subset of users who can discover cold items without the help of recommender system. Therefore, cold items can be captured in the recommendation list via innovators, achieving the balance between serendipity and accuracy. To confirm the effectiveness of our algorithm, extensive experiments are conducted on the dataset provided by Alibaba Group in Ali Mobile Recommendation Algorithm Competition, which is collected from the real e-commerce environment and covers massive user behavior log data.
协同过滤（CF）算法因其具有共享集体智慧和经验的独特能力，已被广泛用于构建推荐系统。然而，它们很容易陷入马太效应的陷阱，这种效应倾向于推荐热门商品，因此不太受欢迎的商品变得越来越不受欢迎。在这种情况下，推荐列表中的大多数商品用户已经熟悉，因此在发现冷门商品（即新商品和小众商品）时性能会严重退化。为了解决这个问题，本文首先对中国的网购习惯进行了用户调查，在此基础上提出了一种名为基于创新者的协同过滤的新颖推荐算法，该算法可以通过引入创新者的概念向用户推荐冷门商品。具体来说，创新者是用户的一个特殊子集，他们可以在没有推荐系统帮助的情况下发现冷门商品。因此，可以通过创新者在推荐列表中捕捉到冷门商品，从而实现意外发现和准确性之间的平衡。为了证实我们算法的有效性，我们在阿里巴巴集团提供的数据集上进行了大量实验，该数据集来自真实的电子商务环境，涵盖了海量的用户行为日志数据。
