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

doi:10.11772/j.issn.1001-9081.2021060908

Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (12): 3515-3520.DOI: 10.11772/j.issn.1001-9081.2021060908

Special Issue: 第十八届中国机器学习会议(CCML 2021)

• The 18th China Conference on Machine Learning • Previous Articles Next Articles

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

Gai LI¹^,²(), Lei LI², Jiaqiang ZHANG¹

^1.School of Intelligent Manufacturing，Shunde Polytechnic，Foshan Guangdong 528300，China
^2.School of Computer Science and Technology，Sun Yat-sen University，Guangzhou Guangdong 510006，China

Received:2021-05-12 Revised:2021-07-29 Accepted:2021-08-09 Online:2021-12-28 Published:2021-12-10
Contact: Gai LI
About author:LI Lei， born in 1951， Ph. D.， professor. His research interests include artificial intelligence， database， machine learning.
ZHANG Jiaqiang， born in 1999. His research interests include recommendation system， data mining.
Supported by:
the Special Fund for Scientific and Technological Innovation Cultivation of College Students in Guangdong Province in 2020(pdjh2020b1363);the “Innovation and Strengthening University” Special Innovation Project of the Department of Education of Guangdong Province in 2020(2020KTSCX367);the Guangdong University Innovation Team Project in 2020 （Natural Science）(2020KCXTD051)

融合显/隐式反馈的社会化协同排序推荐算法

李改¹^,²(), 李磊², 张佳强¹

^1.顺德职业技术学院智能制造学院，广东佛山 528333
^2.中山大学计算机学院，广州 510006

通讯作者: 李改
作者简介:李磊（1951—），男，吉林长春人，教授，博士生导师，博士，主要研究方向：人工智能、数据库、机器学习
张佳强（1999—），男，河南南阳人，主要研究方向：推荐系统、数据挖掘。
基金资助:
2020年广东省大学生科技创新培育专项资金资助项目(pdjh2020b1363);2020年广东省教育厅“创新强校工程”特色创新类项目(2020KTSCX367);2020年广东省普通高校创新团队项目（自然科学）(2020KCXTD051)

Abstract

Abstract:

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.

Key words: recommendation system, collaborative filtering, social collaborative ranking, implicit feedback, explicit feedback

摘要：

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。

CLC Number:

TP301.6

Gai LI, Lei LI, Jiaqiang ZHANG. Social collaborative ranking recommendation algorithm by exploiting both explicit and implicit feedback[J]. Journal of Computer Applications, 2021, 41(12): 3515-3520.

李改, 李磊, 张佳强. 融合显/隐式反馈的社会化协同排序推荐算法[J]. 《计算机应用》唯一官方网站, 2021, 41(12): 3515-3520.

Figures/Tables 3

References 15

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

融合显/隐式反馈的社会化协同排序推荐算法

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