Enhancing recommender systems by incorporating social information

Li-wei HUANG; Gui-sheng CHEN; Yu-cha LIU; De-yi LI

doi:10.1631/jzus.CIIP1303

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Enhancing recommender systems by incorporating social information

计算机与自动化 | Updated：2022-12-09

- Enhancing recommender systems by incorporating social information
- Enhancing recommender systems by incorporating social information
- 信息与电子工程前沿（英文） 2013年14卷第9期页码：711-721
- Affiliations：
  
  1.Institute of Command Information System, PLA University of Science and Technology, Nanjing 210007, China
  2.Institute of Electronic System Engineering, Beijing 100039, China
- Author bio：
  
  [ "Li-wei HUANG, E-mail: huangliwei.1985@gmail.com" ]
  [ "De-yi LI, E-mail: lidy@cae.cn" ]
- Funds：
  
  Project supported by the National Natural Science Foundation of China (Nos. 61035004, 61273213, 61072043, and 61305055) and the National Defense Science Foundation of China (No. 9140A15090112JB93180)
- DOI：10.1631/jzus.CIIP1303
  中图分类号： TP301.6
- 收稿：2013-03-15，
  
  修回：2013-;8-;7，
  
  纸质出版：2013-09
- Accepted：
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Enhancing recommender systems by incorporating social information[J]. 信息与电子工程前沿（英文）, 2013,14(9):711-721.

Li-wei HUANG, Gui-sheng CHEN, Yu-cha LIU, et al. Enhancing recommender systems by incorporating social information[J]. Frontiers of Information Technology & Electronic Engineering, 2013, 14(9): 711-721.
Enhancing recommender systems by incorporating social information[J]. 信息与电子工程前沿（英文）, 2013,14(9):711-721. DOI： 10.1631/jzus.CIIP1303.

Li-wei HUANG, Gui-sheng CHEN, Yu-cha LIU, et al. Enhancing recommender systems by incorporating social information[J]. Frontiers of Information Technology & Electronic Engineering, 2013, 14(9): 711-721. DOI： 10.1631/jzus.CIIP1303.

摘要

Abstract

Although recommendation techniques have achieved distinct developments over the decades

the data sparseness problem of the involved user-item matrix still seriously influences the recommendation quality. Most of the existing techniques for recommender systems cannot easily deal with users who have very few ratings. How to combine the increasing amount of different types of social information such as user generated content and social relationships to enhance the prediction precision of the recommender systems remains a huge challenge. In this paper

based on a factor graph model

we formalize the problem in a semi-supervised probabilistic model

which can incorporate different user information

user relationships

and user-item ratings for learning to predict the unknown ratings. We evaluate the method in two different genres of datasets

Douban and Last.fm. Experiments indicate that our method outperforms several state-of-the-art recommendation algorithms. Furthermore

a distributed learning algorithm is developed to scale up the approach to real large datasets.

关键词

Keywords

references

D. Agarwal , , , B. Chen . . Regression-based Latent Factor Models . . Proc. 15th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining , , 2009 . . p.19 - - 28 . . DOI: 10.1145/1557019.1557029 http://doi.org/10.1145/1557019.1557029 . .

M. Balabanovic , , , Y. Shoham . . Content-based, collaborative recommendation . . Commun. ACM , , 1997 . . 40 ( ( 3 ): ): 66 - - 72 . . DOI: 10.1145/245108.245124 http://doi.org/10.1145/245108.245124 . .

J.S. Breese , , , D. Heckerman , , , C.M. Kadie . . Empirical Analysis of Predictive Algorithm for Collaborative Filtering . . Proc. 14th Conf. on Uncertainty in Artificial Intelligence , , 1998 . . p.43 - - 52 . . . .

M. Deshpande , , , G. Karypis . . Item-based top- N recommendation algorithms . . ACM Trans. Inf. Syst. , , 2004 . . 22 ( ( 1 ): ): 143 - - 177 . . DOI: 10.1145/963770.963776 http://doi.org/10.1145/963770.963776 . .

M. Jahrer , , , A. Tuscher , , , R. Legenstein . . Combining Predictions for Accurate Recommender Systems . . Proc. 16th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining , , 2010 . . p.693 - - 702 . . DOI: 10.1145/1835804.1835893 http://doi.org/10.1145/1835804.1835893 . .

I. Konstas , , , V. Stathopoulos , , , J.M. Jose . . On Social Networks and Collaborative Recommendation . . Proc. 32nd Int. ACM SIGIR Conf. on Research and Development in Information Retrieval , , 2009 . . p.195 - - 202 . . DOI: 10.1145/1571941.1571977 http://doi.org/10.1145/1571941.1571977 . .

Y. Koren . . Factorization Meets the Neighborhood: a Multifaceted Collaborative Filtering Model . . Proc. 14th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining , , 2008 . . p.426 - - 434 . . DOI: 10.1145/1401890.1401944 http://doi.org/10.1145/1401890.1401944 . .

Y. Koren , , , R.M. Bell . . In: Ricci F., Rokach L., Shapira B., et al. (Eds.), Introduction to Recommender Systems Handbook . . Springer US , , 2011 . . p.145 - - 186 . . DOI: 10.1007/978-0-387-85820-3_5 http://doi.org/10.1007/978-0-387-85820-3_5 . .

Y. Koren , , , R.M. Bell , , , C. Volinsky . . Matrix factorization techniques for recommender systems . . Computer , , 2009 . . 42 ( ( 8 ): ): 30 - - 37 . . DOI: 10.1109/MC.2009.263 http://doi.org/10.1109/MC.2009.263 . .

F.R. Kschischang , , , B.J. Frey , , , H. Loeliger . . Factor graphs and the sum-product algorithm . . IEEE Trans. Inf. Theory , , 2001 . . 47 ( ( 2 ): ): 498 - - 519 . . DOI: 10.1109/18.910572 http://doi.org/10.1109/18.910572 . .

M. Kurucz , , , A.A. Benczúr , , , K. Csalogány . . Methods for Large Scale SVD with Missing Values . . Proc. KDD Cup and Workshop , , 2007 . . p.31 - - 38 . . . .

D.D. Lee , , , H.S. Seung . . Learning the parts of objects by non-negative matrix factorization . . Nature , , 1999 . . 401 ( ( 6755 ): ): 788 - - 791 . . DOI: 10.1038/44565 http://doi.org/10.1038/44565 . .

P. Lops , , , M.D. Gemmis , , , G. Semeraro . . In: Ricci F., Rokach L., Shapira B., et al. (Eds.), Introduction to Recommender Systems Handbook . . Springer US , , 2011 . . p.73 - - 105 . . DOI: 10.1007/978-0-387-85820-3_3 http://doi.org/10.1007/978-0-387-85820-3_3 . .

Y. Lu , , , P. Tsaparas , , , A. Ntoulas , , , L. Polanyi . . Exploiting Social Context for Review Quality Prediction . . Proc. 19th Int. Conf. on World Wide Web , , 2010 . . p.691 - - 700 . . DOI: 10.1145/1772690.1772761 http://doi.org/10.1145/1772690.1772761 . .

H. Ma , , , H. Yang , , , M.R. Lyu , , , I. King . . Sorec: Social Recommendation Using Probabilistic Matrix Factorization . . Proc. 17th ACM Conf. on Information and Knowledge Management , , 2008 . . p.931 - - 940 . . DOI: 10.1145/1458082.1458205 http://doi.org/10.1145/1458082.1458205 . .

H. Ma , , , I. King , , , M.R. Lyu . . Learning to recommend with explicit and implicit social relations . . ACM Trans. Intell. Syst. Technol. , , 2011a . . 2 ( ( 3 ): ): Article 29 DOI: 10.1145/1961189.1961201 http://doi.org/10.1145/1961189.1961201 . .

H. Ma , , , D. Zhou , , , C. Liu , , , M.R. Lyu , , , I. King . . Recommender Systems with Social Regularization . . Proc. 4th ACM Int. Conf. on Web Search and Data Mining , , 2011b . . p.287 - - 296 . . DOI: 10.1145/1935826.1935877 http://doi.org/10.1145/1935826.1935877 . .

Q. Mei , , , D. Cai , , , D. Zhang , , , C. Zhai . . Topic Modeling with Network Regularization . . . Proc. 17th Int. Conf. on World Wide Web , , 2008 . . p.101 - - 110 . . DOI: 10.1145/1367497.1367512 http://doi.org/10.1145/1367497.1367512 . .

R. Salakhutdinov , , , A. Mnih . . Probabilistic matrix factorization . . Adv. Neur. Inf. Process. Syst. , , 2008 . . 20 1257 - - 1264 . . . .

B. Sarwar , , , G. Karypis , , , J. Konstan , , , J. Riedl . . Item-Based Collaborative Filtering Recommendation Algorithms . . Proc. 10th Int. Conf. on World Wide Web , , 2001 . . p.285 - - 295 . . DOI: 10.1145/371920.372071 http://doi.org/10.1145/371920.372071 . .

Y. Shen , , , R. Jin . . Learning Personal + Social Latent Factor Model for Social Recommendation . . Proc. 18th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining , , 2012 . . p.1303 - - 1311 . . DOI: 10.1145/2339530.2339732 http://doi.org/10.1145/2339530.2339732 . .

R. Sinha , , , K. Swearingen . . Comparing Recommendations Made by Online Systems and Friends . . Proc. DELOSNSF Workshop on Personalization and Recommender Systems in Digital Libraries , , 2001 . . v.106 . .

E.B. Sudderth , , , A.T. Ihler , , , M. Isard , , , W.T. Freeman , , , A.S. Willsky . . Nonparametric belief propagation . . Commun. ACM , , 2010 . . 53 ( ( 10 ): ): 95 - - 103 . . DOI: 10.1145/1831407.1831431 http://doi.org/10.1145/1831407.1831431 . .

J. Wang , , , Y. Zhang . . Utilizing Marginal Net Utility for Recommendation in E-commerce . . Proc. 34th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval , , 2011 . . p.1003 - - 1012 . . DOI: 10.1145/2009916.2010050 http://doi.org/10.1145/2009916.2010050 . .

X. Xin , , , I. King , , , H. Deng , , , M.R. Lyu . . A Social Recommendation Framework Based on Multi-scale Continuous Conditional Random Fields . . Proc. 18th ACM Conf. on Information and Knowledge Management , , 2009 . . p.1247 - - 1256 . . DOI: 10.1145/1645953.1646111 http://doi.org/10.1145/1645953.1646111 . .

S. Yang , , , B. Long , , , A.J. Smola , , , H. Zha , , , Z. Zheng . . Collaborative Competitive Filtering: Learning Recommender Using Context of User Choice . . Proc. 34th Int. ACM SIGIR Conf. on Research and Development in Information Retrieval , , 2011 . . p.295 - - 304 . . DOI: 10.1145/2009916.2009959 http://doi.org/10.1145/2009916.2009959 . .

X. Yang , , , H. Steck , , , Y. Guo , , , Y. Liu . . On Top- k Recommendation Using Social Networks . . Proc. 6th ACM Conf. on Recommender Systems , , 2012a . . p.67 - - 74 . . DOI: 10.1145/2365952.2365969 http://doi.org/10.1145/2365952.2365969 . .

X. Yang , , , H. Steck , , , Y. Liu . . Circle-Based Recommendation in Online Social Networks . . Proc. 18th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining , , 2012b . . p.1267 - - 1275 . . DOI: 10.1145/2339530.2339728 http://doi.org/10.1145/2339530.2339728 . .

T. Zhou , , , H. Shan , , , A. Banerjee , , , G. Sapiro . . Kernelized Probabilistic Matrix Factorization: Exploiting Graphs and Side Information . . Proc. 12th SIAM Int. Conf. on Data Mining , , 2012 . . p.403 - - 414 . . . .

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