论文标题
通过细心的多人协作过滤来解释建议
Explainable Recommendations via Attentive Multi-Persona Collaborative Filtering
论文作者
论文摘要
推荐系统的两个主要挑战是对用户进行异质口味进行建模，并提供可解释的建议。在本文中，我们提出了神经专注的多人合作滤波（AMP-CF）模型，作为两个问题的统一解决方案。 AMP-CF将用户分解为几个潜在的“角色”（配置文件），这些“角色”（配置文件）识别和辨别用户的不同口味和倾向。然后，揭示的角色用于生成和解释用户的最终建议列表。 AMP-CF将用户建模为角色的细心混合物，从而使动态用户表示，该表示根据所考虑的项目进行更改。我们在电影，音乐，视频游戏和社交网络领域的五个协作过滤数据集上演示了AMP-CF。作为另一个贡献，我们提出了一种新颖的评估方案，以根据用户历史项目中“品味”的基础分布的距离进行比较建议列表中的不同项目。实验结果表明，AMP-CF与其他最先进的模型具有竞争力。最后，我们提供了定性的结果，以展示AMP-CF解释其建议的能力。
Two main challenges in recommender systems are modeling users with heterogeneous taste, and providing explainable recommendations. In this paper, we propose the neural Attentive Multi-Persona Collaborative Filtering (AMP-CF) model as a unified solution for both problems. AMP-CF breaks down the user to several latent 'personas' (profiles) that identify and discern the different tastes and inclinations of the user. Then, the revealed personas are used to generate and explain the final recommendation list for the user. AMP-CF models users as an attentive mixture of personas, enabling a dynamic user representation that changes based on the item under consideration. We demonstrate AMP-CF on five collaborative filtering datasets from the domains of movies, music, video games and social networks. As an additional contribution, we propose a novel evaluation scheme for comparing the different items in a recommendation list based on the distance from the underlying distribution of "tastes" in the user's historical items. Experimental results show that AMP-CF is competitive with other state-of-the-art models. Finally, we provide qualitative results to showcase the ability of AMP-CF to explain its recommendations.
