基于粗糙集规则提取的协同过滤推荐算法

doi:10.11959/j.issn.1000-436x.2020028

Abstract

Abstract:

To address the problem that in a practical recommendation system (RS),because of the datasets are often very sparse,the traditional collaborative filtering (CF) approach cannot provide recommendations with higher quality,a novel CF based on rough set rule extraction was proposed.Firstly,the attributes of user/item and the user-item rating matrix were used to construct a decision table.Then,the core value of each rule in the table was extracted through using the decision table reduction algorithm.Finally,according to the nuclear value decision rule of the core value table,the reductions of all decision rules were utilized to predict the rating scores of un-rated items.Experimental results suggest that the proposed approach can alleviate the data sparsity problem of CF,and provide recommendations with higher accuracy.

Key words: personalized recommendation, collaborative filtering, rough set, rule extraction

CLC Number:

TP391

Yonggong REN,Yunpeng ZHANG,Zhipeng ZHANG. Collaborative filtering recommendation algorithm based on rough set rule extraction[J]. Journal on Communications, 2020, 41(1): 76-83.

Figures/Tables 4

References 17

[1]	BOBADILLA J , ORTEGA F , HERNANDO A ,et al. Recommender systems survey[J]. Knowledge-Based Systems, 2013,46(1): 109-132.
[2]	GOLDBERG D , NICHOLS D , OKI B M ,et al. Using collaborative filtering to weave an information tapestry[J]. Communications of the ACM, 1992,35(12): 61-70.
[3]	ARWAR B , KARYPIS G , KONSTAN J ,et al. Item-based collabora tive filtering recommendation algorithms[C]// The 10th International Conference on World Wide Web. 2001: 285-295.
[4]	ADOMAVICIUS G , TUZHILIN A . Toward the next generation of recommender systems:a survey of the state-of-the-art and possible extensions[J]. IEEE Transactions on Knowledge and Data Engineering, 2005,17(6): 734-749.
[5]	BREESE J , HECHERMAN D , KADIE C . Empirical analysis of predictive algorithms for collaborative filtering[C]// The 14th Conference on Uncertainty in Artificial Intelligence (UAI-98). 1998: 43-52.
[6]	CHOI K , SUH Y . A new similarity function for selecting neighbors for each target item in collaborative filtering[J]. Knowledge-Based Systems, 2013,37: 146-153.
[7]	WANG J , VRIES A P D , REINDERS M J T . Unifying user-based and item-based collaborative filtering approaches by similarity fusion[C]// The 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. 2006: 501-508.
[8]	高滢, 齐红, 刘杰 ,等. 结合似然关系模型和用户等级的协同过滤推荐算法[J]. 计算机研究与发展, 2008,45(9): 1463-1469.
	GAO Y , QI H , LIU J ,et al. A collaborative filtering recommendation algorithm combining probabilistic relational models and user grade[J]. Journal of Computer Research and Development, 2008,45(9): 1463-1469.
[9]	邓爱林, 朱扬勇, 施伯乐 . 基于项目评分预测的协同过滤推荐算法[J]. 软件学报, 2003,14(9): 1621-1628.
	DENG A L , ZHU Y Y , SHI B L . A collaborative filtering recommendation algorithm based on item rating prediction[J]. Journal of Software, 2003,14(9): 1621-1628.
[10]	郑荔平, 胡敏杰, 杨红和 ,等. 基于粗糙集的协同过滤算法研究[J]. 山东大学学报(理学版), 2019,54(2): 45-54.
	ZHENG L P , HU M J , YANG H H ,et al. Research on collaborative filtering algorithm based on rough set[J]. Journal of Shandong University (Natural Science), 2019,54(2): 45-54.
[11]	张锋, 常会友 . 使用BP 神经网络缓解协同过滤推荐算法的稀疏性问题[J]. 计算机研究与发展, 2006,43(4): 667-672.
	ZHANG F , CHANG H Y . Employing BP neural networks to alleviate the sparsity issue in collaborative filtering recommendation algorithms[J]. Journal of Computer Research and Development, 2006,43(4): 667-672.
[12]	孟桓羽, 刘真, 王芳 ,等. 基于图和改进 K 近邻模型的高效协同过滤推荐算法[J]. 计算机研究与发展, 2017(7): 1426-1438.
	MENG H Y , LIU Z , WANG F ,et al. An efficient collaborative filtering algorithm based on graph model and improved KNN[J]. Journal of Computer Research and Development, 2017(7): 1426-1438.
[13]	冷亚军, 梁昌勇, 丁勇 ,等. 协同过滤中一种有效的最近邻选择方法[J]. 模式识别与人工智能, 2013,26(10): 968-974.
	LENG Y J , LIANG C Y , DING Y ,et al. Method of neighborhood formation in collaborative filtering[J]. Pattern Recognition and Artificial Intelligence, 2013,26(10): 968-974.
[14]	于洪, 李俊华 . 一种解决新项目冷启动问题的推荐算法[J]. 软件学报, 2015(6): 1395-1408.
	YU H , LI J H . Algorithm to solve the cold-start problem in new item recommendations[J]. Journal of Software, 2015(6): 1395-1408.
[15]	朱敬华, 明骞 . LBSN 中融合信任与不信任关系的兴趣点推荐[J]. 通信学报, 2018,39(7): 161-169.
	ZHU J H , MING Q . POI recommendation by incorporating trust-distrust relationship in LBSN[J]. Journal on Communications, 2018,39(7): 161-169.
[16]	PAWLAK Z . Rough set[J]. International Journal of Computer and Information Sciences, 1982,11: 341-356.
[17]	阎红灿, 张奉, 刘保相 . 基于粒计算的粗决策规则抽取与约简[J]. 通信学报, 2016,37(S1): 34-39.
	YAN H C , ZHANG F , LIU B X . Rough decision rules extraction and reduction based on granular computing[J]. Journal on Communications, 2016,37(S1): 34-39.

Metrics

Recommended 0

No Suggested Reading articles found!

推荐长度	方法			邻居数量
推荐长度	方法	20	30	40	50	60
	IBCF	0.289	0.289	0.236	0.237	0.222
4	MVCF	0.333	0.313	0.250	0.167	0.167
	RSCF	0.402	0.386	0.305	0.298	0.316
	RECF	0.500	0.375	0.313	0.350	0.341
	IBCF	0.250	0.222	0.216	0.193	0.186
6	MVCF	0.278	0.292	0.167	0.111	0.111
	RSCF	0.325	0.285	0.259	0.289	0.297
	RECF	0.333	0.292	0.277	0.315	0.300
	IBCF	0.213	0.191	0.203	0.188	0.185
8	MVCF	0.250	0.250	0.125	0.083	0.083
	RSCF	0.300	0.289	0.268	0.284	0.275
	RECF	0.365	0.333	0.307	0.295	0.303
	IBCF	0.209	0.160	0.157	0.165	0.154
10	MVCF	0.200	0.200	0.100	0.067	0.067
	RSCF	0.285	0.258	0.265	0.275	0.256
	RECF	0.300	0.267	0.233	0.300	0.280

推荐长度	方法			邻居数量
推荐长度	方法	20	30	40	50	60
	IBCF	0.313	0.333	0.281	0.255	0.236
4	MVCF	0.355	0.325	0.305	0.285	0.255
	RSCF	0.405	0.387	0.328	0.306	0.323
	RECF	0.487	0.395	0.333	0.325	0.375
	IBCF	0.235	0.267	0.233	0.222	0.225
6	MVCF	0.285	0.285	0.265	0.235	0.235
	RSCF	0.355	0.313	0.289	0.297	0.283
	RECF	0.375	0.325	0.295	0.315	0.295
	IBCF	0.215	0.181	0.215	0.193	0.203
8	MVCF	0.235	0.235	0.133	0.197	0.222
	RSCF	0.338	0.296	0.278	0.265	0.233
	RECF	0.365	0.333	0.307	0.295	0.303
	IBCF	0.213	0.178	0.186	0.197	0.195
10	MVCF	0.215	0.215	0.178	0.137	0.113
	RSCF	0.289	0.287	0.258	0.247	0.226
	RECF	0.333	0.300	0.287	0.296	0.273

Collaborative filtering recommendation algorithm based on rough set rule extraction

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 4

References 17

Related Articles 15

Metrics

Recommended 0

[1]	Bin WU,Yun CHEN,Zhongchuan SUN,Yangdong YE. Co-pairwise ranking model for item recommendation [J]. Journal on Communications, 2019, 40(9): 193-206.
[2]	Jinghua ZHU,Qian MING. POI recommendation by incorporating trust-distrust relationship in LBSN [J]. Journal on Communications, 2018, 39(7): 157-165.
[3]	tao WUYi,ming ZHANGXing,mao WANGXing,Han LI. User fuzzy similarity-based collaborative filtering recommendation algorithm [J]. Journal on Communications, 2016, 37(1): 199-207.
[4]	Sheng-zong LIU,Xiao-ping FAN,Zhi-fang LIAO,Jia HU. Hidden Markov model fused with staying time for personalized recommendation [J]. Journal on Communications, 2014, 35(9): 112-121.
[5]	. User similarity-based collaborative filtering recommendation algorithm [J]. Journal on Communications, 2014, 35(2): 3-24.
[6]	Hui-gui RONG,Sheng-xu HUO,Chun-hua HU,Jin-xia MO. User similarity-based collaborative filtering recommendation algorithm [J]. Journal on Communications, 2014, 35(2): 16-24.
[7]	Ling ZHANG,Zhong-ying BAI,Shou-shan LUO,Kang XIE,Guan-ning CUI,Mao-hua SUN. Integrated intrusion detection model based on rough set and artificial immune [J]. Journal on Communications, 2013, 34(9): 166-176.
[8]	. Adaptive user behavior’s evaluation method based on network status [J]. Journal on Communications, 2013, 34(7): 8-80.
[9]	You LU,Jun-zhou LUO,Wei LI,Feng YU,Nu XIA. Adaptive user behavior's evaluation method based on network status [J]. Journal on Communications, 2013, 34(7): 71-80.
[10]	Wei-hua GONG,Liang-huai YANG,Rong JIN,Wei-long DING. Domain of interests clustering algorithm based on users'preferred topics [J]. Journal on Communications, 2011, 32(1): 72-78.
[11]	Xiong-fei LI,Tao SUN,Jian-fang GUO. Rough set model based on the labelled tree [J]. Journal on Communications, 2010, 31(6): 35-43.
[12]	De-qin YAN,Ke-qiu LI,Zhong-xian CHI. Soft discernibility matrix and attribute reduction for information systems [J]. Journal on Communications, 2009, 30(8): 45-50.
[13]	Xiao-hui ZHANG,Bo-gang LIN. Anomaly detection based on feature selection and multi-class support vector machines [J]. Journal on Communications, 2009, 30(10A): 68-73.
[14]	You-chan ZHU,Wei XIONG,Yong-wen JING,Ya-bin GAO. Design and realization of integrated classifier based on Rough Set [J]. Journal on Communications, 2006, 27(11A): 63-67.
[15]	Qi LUO,Ying YU,Cheng-ling ZHAO,Yan CAO. Research on personalized service system in E-supermarket by using adaptive recommendation algorithm [J]. Journal on Communications, 2006, 27(11): 183-186.