  Existing Recommender Systems mainly focus on exploiting users' feedback,
e.g., ratings, and reviews on common items to detect similar users. Thus, they
might fail when there are no common items of interest among users. We call this
problem the Data Sparsity With no Feedback on Common Items (DSW-n-FCI).
Personality-based recommender systems have shown a great success to identify
similar users based on their personality types. However, there are only a few
personality-based recommender systems in the literature which either discover
personality explicitly through filling a questionnaire that is a tedious task,
or neglect the impact of users' personal interests and level of knowledge, as a
key factor to increase recommendations' acceptance. Differently, we identifying
users' personality type implicitly with no burden on users and incorporate it
along with users' personal interests and their level of knowledge. Experimental
results on a real-world dataset demonstrate the effectiveness of our model,
especially in DSW-n-FCI situations.
