Making recommendations based on profitability analysis in a hybrid system

Jing Qiu, Min Xu, Yu Qiu
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Abstract

The study on recommender systems always blinds to the profitability difference among customers. In our research, we have extended our previous work of segmenting customers based on their contributions by proposing a hybrid recommendation system which uses multi-agent system, collaborative filtering together to serve the customers with high profitability, while using simplified top-N to generate high efficient recommendations for the lower profitability customers. Also, agent-based recommendation strategy selection architecture has been proposed and examined in the hybrid recommendation system.
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根据混合系统的盈利能力分析提出建议
推荐系统的研究往往忽略了客户之间的盈利能力差异。在我们的研究中,我们扩展了之前基于客户贡献进行客户细分的工作,提出了一种混合推荐系统,该系统使用多智能体系统,协同过滤一起为高盈利能力的客户服务,同时使用简化的top-N为低盈利能力的客户生成高效的推荐。在混合推荐系统中,提出了基于智能体的推荐策略选择架构,并对其进行了研究。
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