M. Elahi, M. Braunhofer, Francesco Ricci, Marko Tkalcic,
"Personality-based active learning for collaborative filtering recommender systems"
: AI*IA 2013: Advances in Artificial Intelligence, Serie Lecture Notes in Computer Science (LNCS), Vol. 8249, Seite(n) 360-371, 2013, ISSN: 1611-3349
Original Titel:
Personality-based active learning for collaborative filtering recommender systems
Sprache des Titels:
Englisch
Original Buchtitel:
AI*IA 2013: Advances in Artificial Intelligence
Original Kurzfassung:
Recommender systems (RSs) suffer from the cold-start or
new user/item problem, i.e., the impossibility to provide a new user with
accurate recommendations or to recommend new items. Active learning
(AL) addresses this problem by actively selecting items to be presented
to the user in order to acquire her ratings and hence improve the output
of the RS. In this paper, we propose a novel AL approach that exploits
the user?s personality - using the Five Factor Model (FFM) - in order to
identify the items that the user is requested to rate. We have evaluated
our approach in a user study by integrating it into a mobile, contextaware
RS that provides users with recommendations for places of interest
(POIs). We show that the proposed AL approach significantly increases
the number of ratings acquired from the user and the recommendation
accuracy.
Forschungs-