Abstract
Traditional recommendation systems always consider precision as the unique evaluation standard. However, diversity and user tendency are also important for recommendation system performance. The implementation of multiple performance factors can be expressed as a multi-objective optimization problem (MOP). This paper attempts to combine multi-objective optimization algorithm with recommendation algorithm to solve this multi-objective recommendation problem. A novel multi-objective heuristic algorithm called Multi-objective Hydrologic Cycle Optimization (MOHCO) is proposed. MOHCO simulates the water flow, infiltration, evaporation and precipitation processes in nature, and aims to find a set of Pareto optimal solutions. Experimental tests on Grouplens – MovieLens 100K movie recommendation dataset demonstrate that MOHCO outperforms other heuristic algorithms including MOEAD, NSGAII, NSGAIII, MOPSO.
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Acknowledgement
This work is partially supported by the Natural Science Foundation of Guangdong Province (2016A030310074), Project supported by Innovation and Entrepreneurship Research Center of Guangdong University Student (2018A073825).
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Geng, S., Zhang, C., Yang, X., Niu, B. (2019). Multi-criteria Recommender Systems Based on Multi-objective Hydrologic Cycle Optimization. In: Tan, Y., Shi, Y., Niu, B. (eds) Advances in Swarm Intelligence. ICSI 2019. Lecture Notes in Computer Science(), vol 11656. Springer, Cham. https://doi.org/10.1007/978-3-030-26354-6_9
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DOI: https://doi.org/10.1007/978-3-030-26354-6_9
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