Authors:
Ramazan Esmeli
1
;
Mohamed Bader-El-Den
1
and
Hassana Abdullahi
2
Affiliations:
1
School of Computing, University of Portsmouth, Lion Terrace, Portsmouth, U.K.
;
2
School of Mathematics and Physics, University of Portsmouth, Lion Terrace, Portsmouth, U.K.
Keyword(s):
Cold-start Sessions, Recommender Systems, Session-based Recommender Systems.
Abstract:
Cold-Start problem is one of the main challenges for the recommender systems. There are many methods developed for traditional recommender systems to alleviate the drawback of cold-start user and item problems. However, to the best of our knowledge, in session based recommender systems cold-start session problem still needs to be investigated. In this paper, we propose a session similarity-based method to alleviate drawback of cold-start sessions in e-commerce domain, in which there are no interacted items in the sessions that can help to identify users’ preferences. In the proposed method, product recommendations are given based on the most similar sessions that are found using session features such as session start time, location, etc. Computational experiments on two real-world datasets show that when the proposed method applied, there is a significant improvement on the performance of recommender systems in terms of recall and precision metrics comparing to random recommendations
for cold-start sessions.
(More)