Recommender systems can provide users with relevant items
based on each user's preferences. However, in the domain of
mobile applications (apps), existing recommender systems
merely recommend apps that users have experienced
(rated, commented, or downloaded) since this type of
information indicates each user's preference for the apps.
Unfortunately, this prunes the apps which are releavnt
but are not featured in the recommendation lists
since users have never experienced them. Motivated by this phenomenon,
our work proposes a method for recommending serendipitous apps using
graph-based techniques. Our approach can recommend apps
even if users do not specify their preferences. In addition,
our approach can discover apps that are highly diverse.
Experimental results show that our approach can recommend highly
novel apps and reduce over-personalization in a recommendation list.