Abstract
Collaborative Filtering (CF) techniques are the mostly applied methods in real world recommender systems. There are two typical types of CF, which are memory-based and model-based CF algorithms. However, these two CF methods in fact pay attention to different parts of ratings data. Memory-based CF methods are adept at finding local similar users, while model-based CF algorithms emphasize achieving global optimization. In this paper, we integrate a neighborhood approach and Probabilistic Matrix Factorization (PMF) into a hybrid CF model, DPMFNeg, which combines the advantages of memory-based and model-based CF algorithms. We explore the performance of our method on two test datasets – MoiveLens-100K and MoiveLens-1M. The results show that DPMFNeg performs better than other methods on those datasets in terms of MAE and RMSE.
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Yang, W., Ma, J., Huang, S., Yang, T. (2014). DPMFNeg: A Dynamically Integrated Model for Collaborative Filtering. In: Chen, L., Jia, Y., Sellis, T., Liu, G. (eds) Web Technologies and Applications. APWeb 2014. Lecture Notes in Computer Science, vol 8709. Springer, Cham. https://doi.org/10.1007/978-3-319-11116-2_53
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DOI: https://doi.org/10.1007/978-3-319-11116-2_53
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11115-5
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