A Hybrid User Profile Model for Personalized Recommender System with Linked Open Data

Abstract

In order to better enhance user experience on the web, varies applications such as search engines have integrated with recommender systems. Users will get some relevant items recommended when browsing the result page. However, building such recommendation services needs a large amount of item information, which makes it hard to start a new recommender service. Due to the development of Semantic Web and Linked Data, a vast amount of RDF data can be accessed via the Internet and naturally used as a knowledge base for recommender systems. In this paper, we study modeling user profiles in semantic environment and building a personalized recommender system exclusively on Linked Open Data. First, we define two concepts related to user browsing history and propose a hybrid user profile model (Hay-UPM). Second, we design a generic and personalized recommender system to utilize the semantic information between the items and user profile model to make recommendations. Finally, we conduct our experiment on the movie dataset of DBpedia and MovieLens. The result shows Hy-UPMhas a better Mean Reciprocal Rank performance compared with other recommendation methods.