Abstract
User similarity is one of the core issues in recommender systems. The boom in Linked Open Data (LOD) has recently stimulated the research of LOD-enabled recommender systems. Although the hybrid user similarity model recently proposed by the academic community is suitable for a sparse user-item matrix and can effectively improve recommendation accuracy, it relies solely on the historical data (item ratings). This work addresses the problem of computing user similarity by combining item ratings and background knowledge from LOD. We propose a computation method for the user similarity based on feature relevance (USFR), which is an improvement on the user similarity based on item ratings (USIR) in the hybrid user similarity model. The core idea of our improvement is replacing the item ratings in the original model with feature relevance, thereby forming our hybrid user similarity model. Experiments on benchmark datasets demonstrate the effectiveness of the proposed method and its strengths of rating prediction accuracy compared to the USIR measure. Our work also shows that the incorporation of background knowledge from LOD into a hybrid user similarity model can improve recommendation accuracy.
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References
Aggarwal, C.C.: Neighborhood-based collaborative filtering. In: Aggarwal, C.C. (ed.) Recommender Systems: The Textbook, pp. 29–69. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29659-3_2
Wang, Y., Deng, J., Gao, J., Zhang, P.: A hybrid user similarity model for collaborative filtering. Inf. Sci. 418, 102–118 (2017)
Heath, T.: Linked data - welcome to the data network. IEEE Internet Comput. 15(6), 70–73 (2011)
Lehmann, J., et al.: DBpedia - a large-scale, multilingual knowledge base extracted from Wikipedia. Semant. Web 6(2), 167–195 (2015)
Cyganiak, R., Wood, D., Lanthaler, M. (eds.): RDF 1.1 Concepts and Abstract Syntax. W3C Recommendation, 25 February 2014. http://www.w3.org/TR/rdf11-concepts/. Accessed 20 May 2018
Di Noia, T., Ostuni, V.C.: Recommender systems and linked open data. In: Faber, W., Paschke, A. (eds.) Reasoning Web 2015. LNCS, vol. 9203, pp. 88–113. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-21768-0_4
Oramas, S., Ostuni, V.C., Di Noia, T., Serra, X., Di Sciascio, E.: Sound and music recommendation with knowledge graphs. ACM Trans. Intell. Syst. Technol. (TIST) 8(2), Article No. 21 (2017)
Paulheim, H.: Knowledge graph refinement: a survey of approaches and evaluation methods. Semant. Web 8(3), 489–508 (2017)
Anelli, V.W., Di Noia, T., Lops, P., Sciascio, E.D.: Feature factorization for top-N recommendation: from item rating to features relevance. In: Proceedings of the 1st Workshop on Intelligent Recommender Systems by knowledge Transfer and Learning co-located with ACM Conference on Recommender Systems (RecSys 2017), pp. 16–21. ACM (2017)
Tomeo, P., Fernández-TobÃas, I., Di Noia, T., Cantador, I.: Exploiting linked open data in cold-start recommendations with positive-only feedback. In: Proceedings of the 4th Spanish Conference on Information Retrieval, CERI 2016, Article no. 11. ACM (2016)
Aggarwal, C.C.: Evaluating recommender systems. In: Aggarwal, C.C. (ed.) Recommender Systems: The Textbook, pp. 225–254. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29659-3_7
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)
Meymandpour, R., Davis, J.G.: A semantic similarity measure for linked data: an information content-based approach. Knowl. Based Syst. 109, 276–293 (2016)
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Xu, W., Xu, Z., Ye, L. (2018). Computing User Similarity by Combining Item Ratings and Background Knowledge from Linked Open Data. In: Meng, X., Li, R., Wang, K., Niu, B., Wang, X., Zhao, G. (eds) Web Information Systems and Applications. WISA 2018. Lecture Notes in Computer Science(), vol 11242. Springer, Cham. https://doi.org/10.1007/978-3-030-02934-0_43
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DOI: https://doi.org/10.1007/978-3-030-02934-0_43
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