BLOB：一种有机信号和Bandit信号相结合的推荐概率模型
BLOB : A Probabilistic Model for Recommendation that Combines Organic and Bandit Signals
摘要：
推荐系统的一个常见任务是从用户浏览历史中的项目中建立用户兴趣的概况，然后从同一目录中向用户推荐项目。用户的行为由两部分组成：他们在没有干预的情况下观看的项目序列（有机部分）和推荐给他们的项目序列及其结果（强盗部分）。在本文中，我们提出了贝叶斯潜在有机强盗模型（BLOB），概率的方法来结合“有机”和“强盗”的信号，以提高推荐质量的估计。由于其给出推荐性能的直接反馈，所以该强盗信号是有价值的，但是信号质量非常不均匀，因为其高度集中于被推荐器系统的过去版本认为是最优的推荐。相比之下，有机信号通常很强，覆盖大多数项目，但并不总是与推荐任务相关。为了利用有机信号来有效地学习贝叶斯模型中的强盗信号，我们确定了三种基本类型的距离，即动作-历史距离、动作-动作距离和历史-历史距离。我们使用变分自动编码器和局部重新参数化技巧实现完整模型的可扩展近似。我们使用广泛的模拟研究表明，我们的方法优于或匹配的价值都是最先进的有机为基础的推荐算法，和土匪为基础的方法（价值和政策为基础的）在有机和土匪丰富的环境。
英文摘要：
A common task for recommender systems is to build a pro le of the interests of a user from items in their browsing history and later to recommend items to the user from the same catalog. The users' behavior consists of two parts: the sequence of items that they viewed without intervention (the organic part) and the sequences of items recommended to them and their outcome (the bandit part). In this paper, we propose Bayesian Latent Organic Bandit model (BLOB), a probabilistic approach to combine the 'or-ganic' and 'bandit' signals in order to improve the estimation of recommendation quality. The bandit signal is valuable as it gives direct feedback of recommendation performance, but the signal quality is very uneven, as it is highly concentrated on the recommendations deemed optimal by the past version of the recom-mender system. In contrast, the organic signal is typically strong and covers most items, but is not always relevant to the recommendation task. In order to leverage the organic signal to e ciently learn the bandit signal in a Bayesian model we identify three fundamental types of distances, namely action-history, action-action and history-history distances. We implement a scalable approximation of the full model using variational auto-encoders and the local re-paramerization trick. We show using extensive simulation studies that our method out-performs or matches the value of both state-of-the-art organic-based recommendation algorithms, and of bandit-based methods (both value and policy-based) both in organic and bandit-rich environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
