基于蚁群算法的网页推荐

Web Page Recommendation Based on Ant Algorithm

推荐技术中的信息过滤系统包括内容过滤和协作过滤,纯粹的内容过滤系统和纯粹的协作过滤系统都存在各自的缺陷。作者采用了蚁群算法把内容过滤和协作过滤有机的结合起来,形成了一种新的推荐方法。实验证明:该算法可以获得基于内容过滤的优点,包括能进行覆盖所有文档和用户的早期预测;同时也能获得协作过滤的优点,即使用户评估过的文章数不断增加,系统仍能给出较为精确的预测。

Collaborative Filtering and Content-Based Filtering are techniques used in the design of recommender systems that support personalization. Information that is available about the user, along with information about the collection of users on the system, can be processed in a number of ways in order to extract useful recommendations. Collaborative Filtering and Content-Based Filtering have respective shortcomings, which will result in the fall of accuracy of their predictions. In this paper the author describes a novel algorithm in which Collaborative Filtering and Content-Based Filtering are combined with each other by the ant algorithm. In the experiment the algorithm works consistently better than collaborative filtering and Content-Based Filtering, which supports our conjecture that it tends to improve the performance.