融合显/隐式反馈的社会化协同排序推荐算法

Social collaborative ranking recommendation algorithm by exploiting both explicit and implicit feedback

传统的基于评分预测的社会化协同过滤推荐算法存在预测值与真实排序不匹配的固有缺陷,而基于排序预测的社会化协同排序推荐算法更符合真实的应用场景。然而,现有的大多数基于排序预测的社会化协同排序推荐算法要么仅仅关注显式反馈数据,要么仅仅关注隐式反馈数据,没有充分挖掘这些数据的价值。为充分挖掘用户的社交网络和推荐对象的显/隐式评分信息,同时克服基于评分预测的社会化协同过滤推荐算法存在的固有缺陷,在xCLiMF模型和TrustSVD模型基础上,提出一种新的融合显/隐式反馈的社会化协同排序推荐算法SPR_SVD++。该算法同时挖掘用户评分矩阵和社交网络矩阵中的显/隐式信息,并优化排序学习的评价指标预期倒数排名(ERR)。在真实数据集上的实验结果表明,采用归一化折损累计增益(NDCG)和ERR作为评价指标,SPR_SVD++算法均优于最新的TrustSVD、MERR_SVD++和SVD++算法。可见SPR_SVD++算法性能好、可扩展性强,在互联网信息推荐领域有很好的应用前景。

The traditional social collaborative filtering algorithms based on rating prediction have the inherent deficiency in which the prediction value does not match the real sort,and social collaborative ranking algorithms based on ranking prediction are more suitable to practical application scenarios.However,most existing social collaborative ranking algorithms focus on explicit feedback data only or implicit feedback data only,and not make full use of the information in the dataset.In order to fully exploit both the explicit and implicit scoring information of users’social networks and recommendation objects,and to overcome the inherent deficiency of traditional social collaborative filtering algorithms based on rating prediction,a new social collaborative ranking model based on the newest xCLiMF model and TrustSVD model,namely SPR_SVD++,was proposed.In the algorithm,both the explicit and implicit information of user scoring matrix and social network matrix were exploited simultaneously and the learning to rank’s evaluation metric Expected Reciprocal Rank(ERR)was optimized.Experimental results on real datasets show that SPR_SVD++algorithm outperforms the existing state-of-the-art algorithms TrustSVD,MERR_SVD++and SVD++over two different evaluation metrics Normalized Discounted Cumulative Gain(NDCG)and ERR.Due to its good performance and high expansibility,SPR_SVD++algorithm has a good application prospect in the Internet information recommendation field.