Research And Simulation Of Recommender Systems Based On Multi-Agent And Deep Reinforcement Learning

As the scale of online shopping continues to expand,product recommendation systems have become an important tool for enterprise marketing.Traditional product recommendation systems either focus too much on consumer experience to the neglect of enterprises interests,or focus too much on enterprises interests and recommend high-margin products to the detriment of consumer experience,thus making it difficult to enhance long-term enterprises interests.To address this problem,this paper constructs a consumer-enterprise fairness recommendation algorithm to explore a new path to enhance long-term enterprises interests,taking into account consumer experience and enterprises interests.In addition,consumer personality has an important impact on consumer behavioural decisions and recommendation effects,but previous research on personality has problems such as tedious work and one-sided personality portrayal,so this paper improves the personality research method and portrays consumer personality at a fine-grained level.Finally,based on multi-agent technology,the constructed recommendation algorithm and consumer personality are incorporated into a product recommendation simulation model,and the impact of the fairness orientation of the recommendation algorithm on the long-term interests of the enterprise and the impact of consumer personality on consumer behavioural decisions and recommendation effects are analysed through simulation experiments.The main work and contributions of this paper are as follows:(1)A Balance-Slate-Q recommendation algorithm that achieves consumer-firm fairness perception is constructed to address the difficulty of enhancing long-term enterprises interests brought about by traditional commodity recommendation system research that ignores or focuses too much on enterprises interests.Firstly,the MDP for this problem is constructed.in which a fairness-perceptive reward function that integrates consumer interests and enterprise interests is designed,and the quantification of the reward component of enterprise interests is improved to initially realise fairness perception;then the action evaluation method,action selection strategy and algorithm optimisation method of the Slate-Q algorithm are improved to construct the Balance-Slate-Q recommendation algorithm for(2)To explore the impact of fairness orientation of recommendation algorithm.(2)To address the problems of tedious work and one-sided personality portrayal in previous consumer personality studies,a consumer personality model is constructed.Through an in-depth analysis of consumer personality theory,a fine-grained model of consumer personality is constructed from two aspects:psychological characteristics and behavioural models influenced by psychological characteristics.In particular,five psychological traits are abstracted from common consumer behaviours,and the DBN consumer behaviour model is improved to build a behavioural model influenced by psychological traits,which solves the problem of cumbersome work and lays the foundation for exploring the influence of personality.(3)Using multi-agent modelling and simulation methods,by incorporating the Balance-Slate-Q algorithm into the Enterprise Agent and the personality model into the Consumer Agent,the product recommendation simulation model is constructed in conjunction with the product model,and the influence of fairness and consumer personality is explored in a dynamic simulation environment.Simulation experiments are conducted on the RecSim platform,and the results show that the fairness orientation of the Balance-Slate-Q recommendation algorithm can achieve a significant increase in the long-term benefits of the enterprise in the recommendation system with a slight decrease in consumer benefits(fairness factor of 0.9)when it is biased towards consumer benefits.In terms of consumer personality,consumers’ interest threshold,cart threshold and curiosity can have a greater impact on recommendation effectiveness;consumers’ rationality and cart threshold are positively correlated with the enterprises benefit indicator,and consumers’ interest threshold and curiosity are negatively correlated with the enterprises benefit indicator;hedonicity is positively correlated with the consumer benefit indicator,and interest threshold,cart threshold and curiosity are positively correlated with the consumer benefit indicator.