Serendipity-oriented recommender systems expose users to unfamiliar items to counter filter bubbles, yet mere exposure does not ensure that users will understand or appreciate the content they encounter. We propose Peer Recommendation, a framework in which a user and an AI agent (Peer) with distinct preferences collaboratively explore unfamiliar content. Unlike conventional conversational recommender systems where the user is a passive recipient, our framework positions the user as both a recommender and a recipient: the user and the Peer mutually recommend songs to each other through chat-based dialogue, collaboratively building a shared playlist. In an exploratory within-subjects experiment (N=14), we compared three conditions: (1) a Close Peer, (2) a Distant Peer, and (3) a baseline agent without an explicit preference profile. The Close Peer significantly increased users' interest expansion and perceived value of the activity compared to the baseline, with medium-to-large effect sizes. The Distant Peer showed no significant difference at the aggregate level; however, qualitative analysis revealed varied responses, with some participants strongly preferring the Distant Peer. These findings suggest that the "otherness" of a recommendation partner is essential for moving beyond mere exposure toward genuine engagement, and that the appropriate degree of preference distance may vary and need to be adapted to individual users.

翻译：以偶然发现为导向的推荐系统通过向用户展示不熟悉的物品来对抗过滤气泡，然而仅仅展示并不能确保用户理解或欣赏所遇到的内容。我们提出了同伴推荐框架，在该框架中，用户与具有不同偏好的人工智能代理（同伴）共同探索不熟悉的内容。与用户被动接收信息的传统对话式推荐系统不同，我们的框架将用户定位为既是推荐者又是接收者：用户和同伴通过基于聊天的对话相互推荐歌曲，共同构建共享播放列表。在一项探索性受试者内实验（N=14）中，我们比较了三种条件：（1）亲密同伴、（2）疏远同伴和（3）无明确偏好配置文件的基线代理。与基线相比，亲密同伴显著提升了用户的兴趣拓展和活动感知价值，效应量介于中等至大之间。疏远同伴在总体水平上未显示出显著差异；然而，定性分析揭示了不同的反应，一些参与者强烈偏好疏远同伴。这些发现表明，推荐伙伴的“他者性”对于超越单纯展示、迈向真正参与至关重要，且适当的偏好距离程度可能因人而异，需要针对个体用户进行调整。