A recommender system that optimizes its recommendations solely to fit a
user's history of ratings for consumed items can create a filter bubble,
wherein the user does not get to experience items from novel, unseen
categories. One approach to mitigate this undesired behavior is to recommend
items with high potential for serendipity, namely surprising items that are
likely to be highly rated. In this paper, we propose a content-based
formulation of serendipity that is rooted in Bayesian surprise and use it to
measure the serendipity of items after they are consumed and rated by the user.
When coupled with a collaborative-filtering component that identifies similar
users, this enables recommending items with high potential for serendipity. To
facilitate the evaluation of topic-level models for surprise and serendipity,
we introduce a dataset of book reading histories extracted from Goodreads,
containing over 26 thousand users and close to 1.3 million books, where we
manually annotate 449 books read by 4 users in terms of their time-dependent,
topic-level surprise. Experimental evaluations show that models that use
Bayesian surprise correlate much better with the manual annotations of
topic-level surprise than distance-based heuristics, and also obtain better
serendipitous item recommendation performance.

我们提出了一种基于内容的机制，利用贝叶斯惊喜度量消费者收看并给出评分后物品的惊喜性，并结合协同过滤组件来推荐具有高惊喜潜力的物品，从而避免推荐系统中的筛泡现象。通过实验证明，使用贝叶斯惊喜能够更好地与主题级惊喜手动注释的相关性，并获得更好的推荐性能。