Besides accuracy, diversity of recommendation list is also important for users. Hence, the optimization of the recommendation system can be abstracted as a multi-objective problem because accuracy and diversity are contradictory goals. Available multi-objective optimization based recommendation schemes return the Pareto set for the target users. However, the scale of Pareto solutions is uncontrollable. If a Pareto set contains too many solutions, it will not be quite useful for users to make the final decision. In this paper, multi-objective immune algorithm is used to improve recommendation accuracy and diversity, then we can get the pareto set. Further, we introduce PROMETHEE into the recommendation system to get a more precise evaluation of Pareto solutions. By combining PROMETHEE with Pareto, we redefine recommendation as Top-n PROMETHEE Pareto optimization problem and a multi-objective immune optimization and decision-making algorithm is presented. The experimental results show that the proposed algorithm, compared with other existing algorithms, can generate more diverse and accurate recommendation list and provide more precise decision-making for the user.
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This work was supported by the National Natural Science Foundation of China (Grant Nos. 61972456, 41772123); Natural Science Foundation of Tianjin (No. 19JCYBJC15800).
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Chai, Z., Li, Y. & Zhu, S. P-MOIA-RS: a multi-objective optimization and decision-making algorithm for recommendation systems. J Ambient Intell Human Comput 12, 443–454 (2021). https://doi.org/10.1007/s12652-020-01997-x
- Recommendation accuracy
- Recommendation diversity
- Multi-objective immune optimization
- PARETO Refinement
- Recommendation system