Abstract
As one of the most successful recommendation algorithm, collaborative filtering algorithm still faces many challenges, such as accuracy, extensibility, and sparsity. In the algorithm, ratings produced in different period are treated equally, so changes of users’ interests have been ignored. This paper considers the influence of time factor on users’ interests, and presents a new algorithm that involves time decay factor in the collaborative filtering algorithm, the new algorithm makes a more accurate recommendation by reducing the weight of old data. Deploying the collaborative filtering algorithm with time weight on parallel computing frame of MapReduce also achieves the extensibility of algorithm and improves the processing performance of large data sets.
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Su, H., Lin, X., Yan, B., Zheng, H. (2015). The Collaborative Filtering Algorithm with Time Weight Based on MapReduce. In: Wang, Y., Xiong, H., Argamon, S., Li, X., Li, J. (eds) Big Data Computing and Communications. BigCom 2015. Lecture Notes in Computer Science(), vol 9196. Springer, Cham. https://doi.org/10.1007/978-3-319-22047-5_31
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DOI: https://doi.org/10.1007/978-3-319-22047-5_31
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