Abstract
Conventional recommendation systems often use binary relations matrices, which could not represent raw data sets efficiently and uniformly. The graph model can represent multiple relationships and form a unified standard of the feature space to recommend the candidate items. Existing graph-based work is generally based on the path to establish the feature space of the data, only concerned about the impact of an item on the description of the user. Utilizing combination features to construct user profiles, this paper concentrates on the contribution maked by combination of items and designs a user-based collaborative filtering algorithm (CFC), and validates the validity of the algorithm in the prototype of the proposed system. The experimental results show that the recommendation algorithms can significantly improve accuracy of the recommendation.
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This work is supported by the National Natural Science Foundation of China (Grant No. 61572247).
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Hu, X., Liu, Q., Li, L., Liu, P. (2018). A High Precision Recommendation Algorithm Based on Combination Features. In: Liu, C., Zou, L., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10829. Springer, Cham. https://doi.org/10.1007/978-3-319-91455-8_7
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