论文标题
有影响力的建议系统
Influential Recommender System
论文作者
论文摘要
传统的推荐系统通常是被动的，因为他们试图将其建议调整为用户的历史兴趣。但是，对于商业应用程序（例如电子商务，广告位置和新闻门户网站）来说，这是非常理想的，可以扩大用户的兴趣，以便他们接受他们最初不知道或感兴趣的项目以增加客户互动。在本文中，我们提出了有影响力的推荐系统（IRS），这是一种新的建议范式，旨在通过向用户逐步向用户推荐一系列精心选择的项目（称为影响路径）来主动引导用户喜欢给定的物品。我们提出了有影响力的推荐网络（IRN），该网络是一个基于变压器的顺序模型，用于编码项目的顺序依赖性。由于不同的人对外部影响的反应有所不同，因此我们介绍了个性化的印象性掩码（PIM），以模拟用户对外部影响的接受程度，以为用户生成最有效的影响路径。为了评估IRN，我们设计了几个性能指标，以衡量影响路径是否可以平稳地扩展用户兴趣，以包括客观项目，同时保持用户对建议的满意度。实验结果表明，IRN明显优于基线推荐人，并证明了其影响用户兴趣的能力。
Traditional recommender systems are typically passive in that they try to adapt their recommendations to the user's historical interests. However, it is highly desirable for commercial applications, such as e-commerce, advertisement placement, and news portals, to be able to expand the users' interests so that they would accept items that they were not originally aware of or interested in to increase customer interactions. In this paper, we present Influential Recommender System (IRS), a new recommendation paradigm that aims to proactively lead a user to like a given objective item by progressively recommending to the user a sequence of carefully selected items (called an influence path). We propose the Influential Recommender Network (IRN), which is a Transformer-based sequential model to encode the items' sequential dependencies. Since different people react to external influences differently, we introduce the Personalized Impressionability Mask (PIM) to model how receptive a user is to external influence to generate the most effective influence path for the user. To evaluate IRN, we design several performance metrics to measure whether or not the influence path can smoothly expand the user interest to include the objective item while maintaining the user's satisfaction with the recommendation. Experimental results show that IRN significantly outperforms the baseline recommenders and demonstrates its capability of influencing users' interests.
