Recommender systems are widely applied in digital platforms such as news websites to personalize services based on user preferences. In news websites, most of users are anonymous and the only available data is sequences of items in anonymous sessions. Due to this, typical collaborative filtering methods, which are highly applied in many applications, are not effective in news recommendations. In this context, session-based recommenders are able to recommend next items given the sequence of previous items in the active session. Neighborhood-based session-based recommenders have been shown to be highly effective compared to more sophisticated approaches. In this study, we propose scenarios to make these session-based recommender systems diversity-aware and to address the filter bubble phenomenon. The filter bubble phenomenon is a common concern in news recommendation systems and it occurs when the system narrows the information and deprives users of diverse information. The results of applying the proposed scenarios show that these diversification scenarios improve the diversity measures in these session-based recommender systems based on four news datasets.

推荐系统广泛应用于新闻网站等数字平台，以根据用户偏好提供个性化服务。在新闻网站中，大多数用户都是匿名的，唯一可用的数据是匿名会话中的项目序列。因此，在许多应用中高度应用的典型协同过滤方法在新闻推荐中无效。在这种情况下，基于会话的推荐器能够根据活动会话中先前项目的顺序推荐下一个项目。与更复杂的方法相比，基于邻居的基于会话的推荐已被证明是非常有效的。在这项研究中，我们提出了一些方案，使这些基于会话的推荐系统具有多样性意识并解决过滤器气泡现象。过滤气泡现象是新闻推荐系统中常见的问题，它发生在系统缩小信息范围并剥夺用户多样化信息时。应用所提出场景的结果表明，这些多样化场景改进了这些基于四个新闻数据集的基于会话的推荐系统中的多样性度量。