向战略用户推荐
Recommending to Strategic Users
摘要：
推荐系统在数字经济中无处不在。在许多部署的系统中，一个重要的假设是用户消费反映了静态意义上的用户偏好：用户消费他们喜欢的内容，没有其他考虑。然而，正如我们在一项大规模在线调查中所记录的那样，用户确实会有策略地选择内容，以影响他们未来获得推荐的内容类型。我们将这种用户行为建模为推荐系统和用户之间的两阶段噪声信号博弈：推荐系统最初承诺推荐策略，在冷启动阶段向用户呈现内容，用户选择战略性消费以影响他们将在推荐阶段推荐的内容类型。我们表明，在平衡，用户从事的行为，强调他们的差异，不同的偏好配置文件的用户。此外，（统计上的）少数群体由于担心失去他们的少数群体内容展示，可能不会消费主流用户喜欢的内容。接下来，我们提出三种可能提高推荐质量的干预措施（平均和少数群体）在考虑战略消费时：（1）采用一种推荐系统策略，该策略使用来自先验的偏好，（2）与普遍喜欢的用户进行通信，（“主流”）内容将不被用作推荐的基础，以及（3）提供足够个性化但期望在开始时被喜欢的内容。最后，我们描述了一种方法，通知应用理论建模与调查结果。
英文摘要：
Recommendation systems are pervasive in the digital economy. An important assumption in many deployed systems is that user consumption reflects user preferences in a static sense: users consume the content they like with no other considerations in mind. However, as we document in a large-scale online survey, users do choose content strategically to influence the types of content they get recommended in the future. We model this user behavior as a two-stage noisy signalling game between the recommendation system and users: the recommendation system initially commits to a recommendation policy, presents content to the users during a cold start phase which the users choose to strategically consume in order to affect the types of content they will be recommended in a recommendation phase. We show that in equilibrium, users engage in behaviors that accentuate their differences to users of different preference profiles. In addition, (statistical) minorities out of fear of losing their minority content exposition may not consume content that is liked by mainstream users. We next propose three interventions that may improve recommendation quality (both on average and for minorities) when taking into account strategic consumption: (1) Adopting a recommendation system policy that uses preferences from a prior, (2) Communicating to users that universally liked ("mainstream") content will not be used as basis of recommendation, and (3) Serving content that is personalized-enough yet expected to be liked in the beginning. Finally, we describe a methodology to inform applied theory modeling with survey results.
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