Abstract
A User-User Collaborative Filtering (CF) algorithm predicts the rating of a particular item for a given user based on the judgment of other users, who are similar to the given user. Hence, measuring similarity between two users turns out to be a crucial and challenging task as the similarity function is the core component of the item rating prediction function for a particular user. In this paper, we investigate the effectiveness of a multilayer feed-forward artificial neural network as a similarity measurement function. We model similarity between two users as a function that consists of a set of adaptive weights and attempt to train a neural network to optimize the weights. Specifically, our contribution lies in designing an error function for the neural network, which optimizes the network and sets weights in such a way that enables the neural network to produce a reasonable similarity value between two users as its output. Through experimentation on Movielens dataset, we conclude that neural network, as a similarity function, gains more accuracy and coverage compared to the Genetic Algorithm (GA) based similarity architecture proposed by Bobadilla et al.
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References
Krulwich, B.: Lifestyle finder: Intelligent user profiling using large-scale demographic data. AI Magazine 18(2), 37 (1997)
Lang, K.: Newsweeder: Learning to filter netnews. In: Proceedings of the Twelfth International Conference on Machine Learning, Citeseer (1995)
Miller, B.N., Konstan, J.A., Riedl, J.: Pocketlens: Toward a personal recommender system. ACM Transactions on Information Systems (TOIS) 22(3), 437–476 (2004)
Wei, K., Huang, J., Fu, S.: A survey of e-commerce recommender systems. In: 2007 International Conference on Service Systems and Service Management, pp. 1–5. IEEE (2007)
Bobadilla, J., Serradilla, F., Hernando, A.: Collaborative filtering adapted to recommender systems of e-learning. Knowledge-Based Systems 22(4), 261–265 (2009)
Porcel, C., Herrera-Viedma, E.: Dealing with incomplete information in a fuzzy linguistic recommender system to disseminate information in university digital libraries. Knowledge-Based Systems 23(1), 32–39 (2010)
Mitchell, T.M.: Machine learning, vol. 45. McGraw Hill, Burr Ridge (1997)
Herlocker, J.L., Konstan, J.A., Riedl, J.T., Terveen, L.G.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems (TOIS) 22(1), 5–53 (2004)
Manolopoulus, Y., Nanopoulus, A., Papadopoulus, A.N., Symeonidis, P.: Collaborative recommender systems: combining effectiveness and efficiency. Exp. Syst. Appl. 34(4), 2995–3013 (2008)
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Bobadilla, J., Serradilla, F., Bernal, J.: A new collaborative filtering metric that improves the behavior of recommender systems. Knowl. Based Syst. 23(6), 520–528 (2010)
Ingoo, H., Kyong, J.O., Tae, H.R.: The collaborative filtering recommendation based on SOM cluster-indexing CBR. Exp. Syst. Appl. 25, 413–423 (2003)
Bobadilla, J., Ortega, F., Hernando, A., Alcalá, J.: Improving collaborative filtering recommender system results and performance using genetic algorithms. Knowl. Based Syst. 24(8), 1310–1316 (2011)
Goldberg, D.E.: Genetic algorithms in search, optimization, and machine learning (1989) ISBN: 0-201-15767-5
Salton, G., McGill, M.: Introduction to Modern Information Retrieval. McGraw-Hill, New York (1983)
Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: an open architecture for collaborative filtering of netnews. In: Proceedings of the ACM Conference on Computer Supported Cooperative Work, New York, NY, USA, pp. 175–186 (1994)
Goldberg, K., Roeder, T., Gupta, D., Perkins, C.: Eigentaste: a constant time collaborative filtering algorithm. Information Retrieval 4(2), 133–151 (2001)
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems 22(1), 5–53 (2004)
Karypis, G.: Evaluation of item-based top-N recommendation algorithms. In: Proceedings of the International Conference on Information and Knowledge Management (CIKM 2001), Atlanta, Ga, USA, pp. 247–254 (November 2001)
Deshpande, M., Karypis, G.: Item-based top-N recommendation algorithms. ACM Transactions on Information Systems 22(1), 143–177 (2004)
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Mannan, N.B., Sarwar, S.M., Elahi, N. (2014). A New User Similarity Computation Method for Collaborative Filtering Using Artificial Neural Network. In: Mladenov, V., Jayne, C., Iliadis, L. (eds) Engineering Applications of Neural Networks. EANN 2014. Communications in Computer and Information Science, vol 459. Springer, Cham. https://doi.org/10.1007/978-3-319-11071-4_14
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DOI: https://doi.org/10.1007/978-3-319-11071-4_14
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