Authors:
Denis Kotkov
1
;
Jari Veijalainen
1
and
Shuaiqiang Wang
2
Affiliations:
1
University of Jyvaskyla, Finland
;
2
The University of Manchester
Keyword(s):
Recommender Systems, Learning to Rank, Serendipity, Novelty, Unexpectedness, Algorithms, Evaluation.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Collective Intelligence
;
Enterprise Information Systems
;
Recommendation Systems
;
Searching and Browsing
;
Software Agents and Internet Computing
;
Web Information Systems and Technologies
;
Web Interfaces and Applications
Abstract:
Most recommender systems suggest items to a user that are popular among all users and similar to items the user usually consumes. As a result, a user receives recommendations that she/he is already familiar with or would find anyway, leading to low satisfaction. To overcome this problem, a recommender system should suggest novel, relevant and unexpected, i.e. serendipitous items. In this paper, we propose a serendipity-oriented algorithm, which improves serendipity through feature diversification and helps overcome the overspecialization problem. To evaluate our algorithm and compare it with others, we employ a serendipity metric that captures each component of serendipity, unlike the most common metric.