  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 has 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.
