一种基于自适应近邻选择的协同过滤推荐算法  

A Collaborative Filtering Recommendation Algorithm Based on Adaptive Nearest Neighbor Selection

作　　者：彭玉[1] 程小平[2] PENG Yu, CHENG Xiao-ping （1. Sichuan Top Vocational Institute of Information Technology, Chengdu 611743, China;2.Department of Computer and Information Science, Southwest University, Chongqing 400715, China）

机构地区：[1]四川托普信息技术职业学院,四川成都611743 [2]西南大学计算机与信息科学学院,重庆400715

出　　处：《电脑知识与技术》2016年第6期127-130,共4页Computer Knowledge and Technology

摘　　要：评分数据稀疏性问题和新用户冷开始问题对推荐系统性能造成很大的影响,为了提高推荐精度本文提出了一种基于自适应组合协同过滤推荐方法ANCF。该算法首先通过推荐系统收集到的用户特征数据和项目特征数据来改善原始相似度计算,然后再组合用户邻居集和项目邻居集来为用户未评分的项目进行预测评分。通过自适应协调因子协调处理两方面的影响。通过实验表明,该算法可以充分挖掘用户群邻居和项目群邻居对推荐结果的预测作用,提高推荐系统的预测精度。Based on the analysis of the sparse problem and the cold start problem in the traditional collaborative filtering recom- mendation, a collaborative filtering recommendation algorithm based on adaptive nearest neighbor selection is proposed. The al- gorithm considers the influence factors of user characteristics and project attributes, and then calculates the nearest neighbor sets of target users and target projects by using the score similarity model. According to the situation of the sparse score data, the sim- ilarity measurement results of two aspects are handled by the adaptive coordination factors, so as to get the final project forecast score. Experiments show that the proposed algorithm can effectively balance the instability effects based on the user group score and the recommendation based on the project group, and effectively alleviate the problems caused by the sparse user rating data, so as to improve the prediction accuracy of the recommendation system.

关 键 词：基于项目协同过滤 基于用户协同过滤 推荐系统 相似性 属性特征 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论;自动化与计算机技术—计算机科学与技术]