Exploiting the web of data in model-based recommender systems

Abstract

The availability of a huge amount of interconnected data in the so called Web of Data (WoD) paves the way to a new generation of applications able to exploit the information encoded in it. In this paper we present a model-based recommender system leveraging the datasets publicly available in the Linked Open Data (LOD) cloud as DBpedia and Linked-MDB. The proposed approach adapts support vector machine (SVM) to deal with RDF triples. We tested our system and showed its effectiveness by a comparison with different recommender systems techniques -both content-based and collaborative filtering ones.

Using Graph Metrics for Linked Open Data Enabled Recommender Systems

“…Di Noia et al [5] propose a model-based recommender system that relies on LOD, and can use any arbitrary classifier to perform the recommendations. First, they map the items from the local dataset to DBpedia, and then extract the direct property-value pairs.…”

Mobile Movie Recommendations with Linked Data

“…They define the Linked Data Semantic Distance in order to find semantic distances between resources and then compute recommendations. In [11,12] a model-based approach and a memory-based one to compute CB recommendations are presented leveraging LOD datasets. Several strategies are described and compared to select ontological properties (in the movie domain) to be used during the computation of recommended items.…”

A New Profile Learning Model for Recommendation System based on Machine Learning Technique

“…The application of Machine Learning techniques is a standard way to perform the task of learning user profiles in recommender systems [5], such as Clustering [6,7], Genetic Algorithms [8,9], Neural Networks [10,11], and Classification Techniques [12][13][14][15]. Unfortunately, these techniques suffer from vital drawbacks.…”

“…In Neural Networks [10,11], time to train NN is probably identified as the biggest disadvantage. In classification technique [12][13][14], they suffer from low accuracy and high computation cost respectively.…”

“…In [14], a model-based approach for a content-based recommender system exploiting exclusively Linked Open Data cloud to represent both the information on the items and on the user profiles. The main idea is to show how a model-based approach can be easily adapted to cope with the semantic Web, and to use a Support Vector Machine (SVM) classifier for learning the user profile.…”