基于物品的改进协同过滤算法及应用

Improved Item-Based Collaborative Filtering Algorithm and Its Application

针对电视产品信息资源量过载导致用户选择困难的问题,本文主要研究了基于物品的协同过滤算法在电视产品推荐系统中的改进及应用,将个性化推荐技术和电视产品系统有机结合来满足用户和运营商的需求.在推荐过程中,首先收集用户的偏好建立数据模型,以用户观看电视产品的时长作为用户偏好的显式特征,然后在传统的协同过滤算法中引入点播金额权重进行改进,并采用欧几里德距离法计算物品相似度,最后根据邻居集合预测目标用户对电视产品的观看时长,得到推荐结果.实验表明,通过引入点播金额权重这一改进能够提高推荐的准确性.

Aiming at the problem that the overload of information resources of TV products leads to the difficulty of user selection,this study mainly focuses on the improvement and application of article-based collaborative filtering algorithm in television product recommendation system,and combines the personalized recommendation technology with TV product system to meet the need of users and operations.In the recommendation process,the user’s preference data model is first collected,and the duration of the user watching the television product is taken as the explicit characteristics of the user’s preference.Then,it is improved by introducing the weight of on-demand amount in the traditional collaborative filtering algorithm,and the Euclidean distance method is used to calculate the similarity of the items.Finally,the viewing time of the target user on the television products is predicted according to the neighbor set,and a recommendation result is obtained.Experiments show that the introduction of on-demand amount weights can improve the accuracy of recommendations.