Serendipitous Page Recommendation on Web IndeX System with Potential Preferences

Abstract

Most recommendation systems excessively pursue the recommendation accuracy and give rise to over-specialization. However, the existing recommendation systems research has not studied serendipity much. Hence, the serendipitous item recommendation has received more attention in recent years. The serendipitous recommendation of our research is not included in the area that the user predict easily but recommends the keywords that match the potential preferences. Potential preferences are those that are present in the user profile, which the user may not know. In this research, we recommend keywords that can express serendipity by intersecting the relation between keywords mainly. Furthermore, we propose the related page recommendation method on Web IndeX System for recommending linked pages related to these serendipitous keywords based on the user's potential preferences.