Abstract
As for the low recommendation degree of the traditional collaborative filtering recommendation algorithm, a high efficient recommendation algorithm was put forward based on LDA. A new interest layer is added between the user layer and the project layer to produce the user - interest - item interest model. The interest information was extracted by expanded LDA, which makes use of the evaluation rank of the user on the project. Moreover, because of the constant change of the users’ interests, pagerank algorithm is adopted, which takes account of the time characteristic, to support the random walk style to extend interests. Then items are recommended and user ratings are predicted according to the interest information. The experiments improve that the improved algorithm has better effect on recommendation by comparing with traditional SVD, LDA algorithms.
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Acknowledgments
The work is supported by the National Science Foundation of China (NSFC) under Grant No. 61202376 and 61572325, Shanghai Natural Science Foundation under Grant No. 15ZR1429100, Innovation Program of Shanghai Municipal Education Commission under Grant No. 13YZ075, Shanghai Key Science and Technology Project in Information Technology Field under Grant No. 14511107902, Shanghai Leading Academic Discipline Project under Grant No. XTKX2012, and Shanghai Engineering Research Center Project under Grant No. GCZX14014 and C14001.
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© 2016 Springer International Publishing Switzerland
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Hu, D., Chen, L. (2016). A High Efficient Recommendation Algorithm Based on LDA. In: Zu, Q., Hu, B. (eds) Human Centered Computing. HCC 2016. Lecture Notes in Computer Science(), vol 9567. Springer, Cham. https://doi.org/10.1007/978-3-319-31854-7_61
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DOI: https://doi.org/10.1007/978-3-319-31854-7_61
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