Serendipity In Recommender System: A Holistic Overview

Recommender system is a software built to retrieve relevant information based on user interest [1]. Recommender system can determine useru0027s interest by looking at several resources such as useru0027s consumed items, similar users, and search logs. The massive amount of information about users and items coupled with extensive research in increasing recommender systemu0027s accuracy resulted in an over-specialization problem [2]. In which, recommender systems tend to recommend obvious items or previously known items. These obvious recommendations result in failing to arouse usersu0027 long-term interest. For example, a high accuracy travel recommender system will never recommend new places to a user outside of the already visited places. To mitigate this problem, researchers shifted from focusing on accuracy to achieve user satisfaction to a more user central measures known as beyond accuracy measures. These measures believed to increase user satisfaction, and long-term interest.