Abstract
This paper presents a recommendation algorithm based on matrix operations (RAMO), which integrates collaborative filtering algorithm with information network-based approach. RAMO exploits information from different objects to increase the recommendation accuracy. Furthermore, a distributed recommendation algorithm DRAMD is proposed based on matrix decomposition using the framework MapReduce. DRAMD can be run across multiple cluster nodes to reduce the computation time. Test results on MovieLens dataset show that the algorithms not only have better recommendation effectiveness but improve the efficiency of the computation.
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© 2015 Springer International Publishing Switzerland
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Wu, S., Lu, D., Du, Y., Feng, X. (2015). Distributed Recommendation Algorithm Based on Matrix Decomposition on MapReduce Framework. In: Zhang, S., Wirsing, M., Zhang, Z. (eds) Knowledge Science, Engineering and Management. KSEM 2015. Lecture Notes in Computer Science(), vol 9403. Springer, Cham. https://doi.org/10.1007/978-3-319-25159-2_40
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DOI: https://doi.org/10.1007/978-3-319-25159-2_40
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Online ISBN: 978-3-319-25159-2
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