基于混合模型的个性化阅读系统设计

为解决协同过滤推荐算法冷启动和数据稀缺的问题，提高个性化阅读系统推荐准确性，根据图书特点，提出一种融合协同过滤算法和兴趣标签算法的个性化阅读系统设计。通过交叉调和方法，给定一个适当的融合比将两种推荐算法的推荐结果进行融合，保证系统在解决冷启动问题的同时，能够增加推荐列表新鲜度，提高推荐准确度，保持个性化阅读系统优越性。结果表明，该方法即使没有评级也能合理推荐，在推荐准确性和图书种类方面优于传统方法。

Design of Personalized Reading System Based on Hybrid Model

In order to solve the problems of cold start and data scarcity of collaborative filtering recommendation algorithm and improve the accuracy of personalized reading system recommendation，a system based on fusion collaborative filtering and interest tag algorithm for personalized reading is proposed according to the characteristics of books. By cross-harmonic method with an appropriate fusion ratio and the recommendation results of the two recommended algorithms， the system can ensure the freshness of the recommendation list， improve the accuracy of recommendation， and maintain personalization while solving the cold start problem. The superiority of the reading system is thus maintained. The results show that our method can reasonably make recommendation even without rating and thus is superior to the traditional method in terms of recommendation accuracy and book type.