Authors:
Aleksandra Karpus
1
;
Tommaso Di Noia
2
;
Paolo Tomeo
2
and
Krzysztof Goczyla
1
Affiliations:
1
Gdańsk University of Technology, Poland
;
2
Polytechnic University of Bari, Italy
Keyword(s):
Recommender Systems, Context Awareness, Conditional Preferences, Rating Prediction, Cold-start Problem.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Collaborative Filtering
;
Context Discovery
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Symbolic Systems
;
User Profiling and Recommender Systems
Abstract:
Exploiting contextual information is considered a good solution to improve the quality of recommendations, aiming at suggesting more relevant items for a specific context. On the other hand, recommender systems research still strive for solving the cold-start problem, namely where not enough information about users and their ratings is available. In this paper we propose a new rating prediction algorithm to face the cold-start system scenario, based on user interests model called contextual conditional preferences. We present results obtained with three publicly available data sets in comparison with several state-of-the-art baselines. We show that usage of contextual conditional preferences improves the prediction accuracy, even when all users have provided a few feedbacks, and hence small amount of data is available.