Authors:
Denis Kotkov
;
Jari Veijalainen
and
Shuaiqiang Wang
Affiliation:
University of Jyvaskyla, Finland
Keyword(s):
Relevance, Unexpectedness, Novelty, Serendipity, Recommender Systems, Evaluation Metrics, Challenges.
Related
Ontology
Subjects/Areas/Topics:
Agents
;
Artificial Intelligence
;
Collective Intelligence
;
Enterprise Information Systems
;
Recommendation Systems
;
Software Agents and Internet Computing
Abstract:
Most recommender systems suggest items similar to a user profile, which results in boring recommendations
limited by user preferences indicated in the system. To overcome this problem, recommender systems should
suggest serendipitous items, which is a challenging task, as it is unclear what makes items serendipitous
to a user and how to measure serendipity. The concept is difficult to investigate, as serendipity includes
an emotional dimension and serendipitous encounters are very rare. In this paper, we discuss mentioned
challenges, review definitions of serendipity and serendipity-oriented evaluation metrics. The goal of the
paper is to guide and inspire future efforts on serendipity in recommender systems.