Online platforms which assist users in finding a suitable match, such as online-dating and job recruiting environments, have become increasingly popular in the last decade. Many of these environments include recommender systems which, for instance in online dating, aim at helping users to discover a suitable partner who will likely be interested in them. Generating successful recommendations in such systems is challenging as the system must balance two objectives: (1) recommending users with whom the recommendation receiver is likely to initiate an interaction and (2) recommending users who are likely to reply positively to the recommendation receiver initiated interaction. Unfortunately, these objectives are partially conflicting since very often the recommendation receiver is likely to contact users who are not likely to respond positively, and vice versa. Furthermore, users in these environments vary in the extent to which they contemplate the other side’s preferences before initiating an interaction. Therefore, an effective recommender system must effectively model each user and balance these objectives. In our work, we tackle this challenge through two novel components: (1) an explanation module, which leverages an estimate of why the recommended user is likely to respond positively to the recommendation receiver; and (2) a novel reciprocal recommendation algorithm, which finds an optimal balance, individually tailored to each user, between the partially conflicting objectives mentioned above. In an extensive empirical evaluation, in both simulated and real-world dating Web platforms with 1204 human participants, we find that both components contribute to attaining these objectives and that the combinations thereof are more effective than each one on its own.

中文翻译：

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支持用户在互惠推荐系统中找到成功的匹配

在过去十年中，帮助用户寻找合适人选的在线平台（例如在线约会和招聘环境）变得越来越流行。许多这些环境包括推荐系统，例如在线约会，旨在帮助用户发现可能对他们感兴趣的合适的合作伙伴。在这样的系统中生成成功的推荐具有挑战性，因为系统必须平衡两个目标：（1）推荐推荐接收者可能与之发起交互的用户；（2）推荐可能对推荐接收者发起的交互做出积极回复的用户. 不幸的是，这些目标存在部分冲突，因为推荐接收者经常会联系不太可能积极响应的用户，反之亦然。此外，这些环境中的用户在发起交互之前考虑对方偏好的程度各不相同。因此，一个有效的推荐系统必须有效地为每个用户建模并平衡这些目标。在我们的工作中，我们通过两个新颖的组件来应对这一挑战：（1）一个解释模块，它利用对推荐用户可能对推荐接收者做出积极响应的原因的估计；(2) 一种新颖的互惠推荐算法，它在上述部分冲突的目标之间找到最佳平衡，为每个用户量身定制。在广泛的实证评估中，在模拟和真实世界的约会网络平台中，有 1204 名人类参与者，