论文标题
基于双功能分配模型的推荐系统
A Recommender System Based on a Double Feature Allocation Model
论文作者
论文摘要
协作过滤建议系统通过在用户和项目中发现共同的功能来预测用户的偏好。我们使用贝叶斯双功能分配模型（即随机对子集的模型）实施了这种推理。我们使用印度自助餐过程（IBP）将用户和项目链接到功能。这里的功能是用户的子集和项目的匹配子集。通过培训特定的评级效应，我们预测评分。我们使用Movielens数据来证明模型中的后验推断，并且与先前评级的项目相比，用户对看不见项目的偏好预测。 该实现的一部分是一种新型的半共识蒙特卡洛方法，可容纳大量用户和项目，这是相关应用程序的典型情况。所提出的方法在多个用户中实现并行的后验采样，同时在各个碎片之间共享与项目相关的全局参数。
A collaborative filtering recommender system predicts user preferences by discovering common features among users and items. We implement such inference using a Bayesian double feature allocation model, that is, a model for random pairs of subsets. We use an Indian buffet process (IBP) to link users and items to features. Here a feature is a subset of users and a matching subset of items. By training feature-specific rating effects, we predict ratings. We use MovieLens Data to demonstrate posterior inference in the model and prediction of user preferences for unseen items compared to items they have previously rated. Part of the implementation is a novel semi-consensus Monte Carlo method to accomodate large numbers of users and items, as is typical for related applications. The proposed approach implements parallel posterior sampling in multiple shards of users while sharing item-related global parameters across shards.
