Content Based Cross-Domain Recommendation Using Linked Open Data

Abstract

A recommender system, irrespective of the
approach that has been used to implement it suffers from
the cold-start situation. Not being able to predict items to a
new user due to not having access to his previous
preferences, and not being able to recommend a new item to
users due to not having any prior ratings on the
particular item is the two cold-start problems. Even though
content-based recommender systems are immune to item
cold-start problem, they are comparatively less used due to
lack of up-to-date data sources that provide item features
and also due to the high amount of pre-processing required
when using existing data sources for retrieving meta-data.
In this paper we present a content-based cross domain
recommendation system using Linked Open Data to
address the issue of cold-start situation. The evaluation
proves that this approach can be used as a solution to a coldstart
situation and also the prevailing issue of content-based
recommender systems which forced them to take the
backseat will no longer be applicable when Linked Open
Data is used.