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Web Information Recommendation Evaluation Model Based on Multifactor Decision Making

There is a certain gap between the information recommendation of web platform and the real demand of users, because recommendation systems lack historical transaction data and excellent recommendation algorithm. In order to improve users' satisfaction for information recommendation on web platform, a web information recommendation evaluation model is proposed, in which users' experience as the evaluation basis of information recommendation service quality. Firstly, a web information recommendation evaluation model is constructed, which consists of three factors: all customers' feedback on the commodity, direct relevance analysis between customer and commodity, and the customer's all transaction feedback. Secondly, the particle swarm optimization algorithm is applied to solve the optimal weight of each factor in the information recommendation evaluation model, then the evaluation result of information recommendation is calculated, which can provide decision-making basis for reasonably dealing with the information recommendation. Finally, simulation results show that the web information recommendation evaluation model based on multifactor decision making is effective, and it can improve users' satisfaction for web information recommendation significantly.