协同过滤和粒子群算法在饮食推荐中的应用

APPLICATION OF COLLABORATIVE FILTERING AND PARTICLE SWARM OPTIMIZATION IN DIETARY RECOMMENDATION

目前饮食健康越来越受到关注,而传统饮食推荐只单方面考虑饮食偏好或营养均衡需求。综合考虑两个方面,构建个性化饮食推荐模型。利用基于用户的协同过滤算法进行饮食推荐,解决饮食偏好问题;利用多目标粒子群优化算法对推荐食谱进行营养调优,解决营养均衡问题。实验结果表明,该模型在推荐和调优上效果显著,有效解决了个性化饮食推荐问题。

At present,healthy diet is getting more and more attention.Traditional diet recommendations only consider the need of diet preference or nutrition balance.To solve the diet recommendation problem of diet preference and nutrition balance,we constructed a personalized dietary recommendation model.User-based collaborative filtering algorithm was used to recommend the diet so as to solve the diet preference.The multi-objective particle swarm optimization was adopted to adjust nutrition well so as to solve the balance of nutrition.The experimental results show that the model has a significant effect on the recommendation and adjustment,which effectively solves the personalized diet recommendation problem.