针对个性化推荐系统中用户的多个不同需求，提出一种基于免疫算法的求解方法。该算法将要求解的个性化 推荐列表建模成一个最大化推荐准确性和多样性的多目标优化问题，采用基于用户的协同过滤技术对用户进行分类，设计了 适合推荐问题求解的抗体编码方式、克隆、变异算子。仿真实验结果表明，所提算法能够有效求得个性化推荐的最佳解，达到 可以同时为多个用户提供多个不同推荐的需求。

To meet different needs of users in the recommendation system, an immune clone algorithm is proposed. The problem is modeled as a multi-objective optimization problem to maximize the accuracy and diversity in the recommendation lists. It uses collaborative filtering technology to finish the user classification. Antibody coding and the immune operator that suitable for solving the problem are designed in the algorithm. The results show that the proposed algorithm can effectively obtain the optimal solutions of personalized recommendation, which satisfies with different needs of multiple users at the same time.