Abstract
We developed a novel approach for including metadata generated from Linked Open Data into Recommendation Systems by proposing a probabilistic view of Collective Matrix Factorization. The Linked Open Data cloud is being conceived and published to improve the usability and performance of various applications including Recommender Systems. While most previous works focus on exploiting Linked Open Data on content based Recommendation System, we include the semantic information into the collaborative filtering recommendation approach. With an unsupervised method, we generated different metadata representations for items from Linked Open Data and incorporated them into Probabilistic Matrix Factorization to get a double matrix factorization to boost the performance. Experiments showed that our proposed approach performs comparably well and in some scenarios generate significantly better results than Probabilistic Matrix Factorization methods when there is no semantic data inclusion.
X. Sun—The first two authors contribute equally
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Kushwaha, N., Sun, X., Vyas, O.P., Krohn-Grimberghe, A. (2016). SemPMF: Semantic Inclusion by Probabilistic Matrix Factorization for Recommender System. In: de la Prieta, F., et al. Trends in Practical Applications of Scalable Multi-Agent Systems, the PAAMS Collection. PAAMS 2016. Advances in Intelligent Systems and Computing, vol 473. Springer, Cham. https://doi.org/10.1007/978-3-319-40159-1_27
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DOI: https://doi.org/10.1007/978-3-319-40159-1_27
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