基于评分预测与排序预测的协同过滤推荐算法  

Collaborative Filtering Recommendation Algorithm Based on Rating Prediction and Ranking Prediction

作　　者：李改[1] 陈强 李磊[3] 

机构地区：[1]顺德职业技术学院电子与信息工程学院,广东顺德528333 [2]广东第二师范学院计算机科学系,广东广州510303 [3]中山大学数据科学与计算机学院,广东广州510006

出　　处：《电子学报》2017年第12期3070-3075,共6页Acta Electronica Sinica

基　　金：国家自然科学基金(No.61370186,No.61640222);广东省自然科学基金项目(No.2016A030310018);广东省科技计划项目(No.2014A010103040,No.2014B010116001);广州市科技计划项目(No.201604010049,No.201510010203);广东第二师范学院教授博士科研专项(No.2015ARF25)

摘　　要：协同过滤推荐算法在电子商务领域运用广泛.之前的研究要么仅从评分预测的角度来研究,要么仅从排序预测的角度来研究.为了兼顾这两个方面,本文在传统的基于评分预测的PMF(Probabilistic Matrix Factorization)算法和基于排序预测的xCLiMF(Extended Collaborative Less-is-More Filtering)算法的基础上提出了一种基于评分预测与排序预测的协同过滤推荐算法URA(Unified Recommendation Algorithm),该方法通过在PMF和xCLiMF算法中共享用户和推荐对象的特征空间,利用PMF算法来学习高精度的用户和推荐对象的特征向量,从而进一步增强排序推荐性能.实验验证,该方法在评价指标NDCG和ERR下均优于PMF和xCLiMF算法,且复杂度与评分点个数线性相关.URA算法可运用于互联网信息推荐领域的大数据处理.Collaborative filtering（ CF） recommendation algorithm is widely used in the field of e-commerce. The previous researches on CF focused on either rating prediction or ranking prediction. In order to take into account these two aspects,a collaborative filtering recommendation algorithm based on rating prediction and ranking prediction（ Unified Recommendation Algorithm,URA） is proposed. URA shares common latent features of users and items in PMF（ Probabilistic Matrix Factorization, rating-oriented） and xCLiMF（ Extended Collaborative Less-is-More Filtering, ranking-oriented） algorithms,and PMF learns improved latent features of users and items in URA, so that URA improves the performance of ranking recommendation. Experimental results showed that our proposed URA Algorithm outperformed PMF and xCLiMF algorithms over evaluation metrics NDCG and ERR, and that the complexity of URA is shown to be linear with the number of observed ratings. URA is suitable for big data processing in the field of internet information recommendation.

关 键 词：推荐系统 协同排序 协同过滤 评分预测 排序预测 

分 类 号：TP302[自动化与计算机技术—计算机系统结构]