Abstract
Item Splitting splits an item into two items rated under two alternative contextual conditions respectively for improving the prediction accuracy of contextual recommendations. To get more specialized rating data, Complex Splitting is proposed to further improve the accuracy of recommendations. The key of the approach is to select multiple contextual conditions for splitting user or item. We translate it into a contextual conditions combinatorial optimization problem based on discrete binary particle swarm optimization (BPSO) algorithm. The item or user is split into two different items or users according to those contextual conditions in optimal combination. We evaluate our algorithm through a real world dataset and the experimental results demonstrate its validity and reliability.
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Acknowledgement
This research is supported by the National Natural Science Fund (Grant no. 41362015), a Science and Technology Project of Education Department of Jiangxi Province (Grant nos. GJJ14431, GJJ14432, and GJJ14458), and the Youth Science Foundation Project of the Science and Technology Department of Jiangxi Province (Grant no. 20122BAB211035).
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Yang, S., Peng, Q., Chen, L. (2016). The BPSO Based Complex Splitting of Context-Aware Recommendation. In: Li, K., Li, J., Liu, Y., Castiglione, A. (eds) Computational Intelligence and Intelligent Systems. ISICA 2015. Communications in Computer and Information Science, vol 575. Springer, Singapore. https://doi.org/10.1007/978-981-10-0356-1_46
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DOI: https://doi.org/10.1007/978-981-10-0356-1_46
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