Cited By
View all- Liu HDeng SWu LJian MYang BZhang D(2020)Recommendations for Different Tasks Based on the Uniform Multimodal Joint RepresentationApplied Sciences10.3390/app1018617010:18(6170)Online publication date: 4-Sep-2020
Recommending Followees Based on Content Weighted User Interest Homophily
Authors: 
Lifang Wu, Dai Zhang, Xiuzhen Zhang, Yuchen Jing, Haiying Liu, Chang Wen ChenAuthors Info & Claims
Lifang Wu, Dai Zhang, Xiuzhen Zhang, Yuchen Jing, Haiying Liu, Chang Wen ChenAuthors Info & ClaimsPages 146 - 151
Abstract
We study the problem of recommending followees to users on content curation social networks (CCSNs). Different from existing friendship-oriented user recommendation approaches, we exploit user interest homophily to recommend users of similar interests, combining the users' social network as well as their topical interests. We first profile users with social links and topical interests derived from "re-pin paths". We further design a collaborative filtering strategy for user recommendation based on interest homophily. Experiments on a content curation social network show that our recommendation algorithm based on user interest homophily performs better than recommendation based on user popularity.
References
[1]
M. G. Armentano, D. Godoy, and A. Amandi. Topology-based recommendation of users in micro-blogging communities. Journal of Computer Science & Technology, 27(3):624--634, 2012.
[2]
C. Bernardini, T. Silverston, and O. Festor. A pin is worth a thousand words: Characterization of publications in pinterest. In Proceedings of the 2014 International Wireless Communications and Mobile Computing Conference, pages 322--327, 2014.
[3]
D. Billsus and M. J. Pazzani. Learning collaborative information filters. In Proceedings of the Fifteenth International Conference on Machine Learning, pages 46--54, 1998.
[4]
J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, pages 43--52, 2013.
[5]
J. Canny. Collaborative filtering with privacy via factor analysis. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, pages 238--245, 2002.
[6]
S. Chang, V. Kumar, E. Gilbert, and L. Terveen. Specialization, homophily, and gender in a social curation site: findings from pinterest. In Proceedings of the 17th ACM conference on Computer supported cooperative work & social computing, pages 674--686, 2014.
[7]
J. Chen, W. Geyer, C. Dugan, M. Muller, and I. Guy. Make new friends, but keep the old: recommending people on social networking sites. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 201--210, 2009.
[8]
E. Gilbert, S. Bakhshi, S. Chang, and L. Terveen. I need to try this?: a statistical overview of pinterest. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 2427--2436, 2013.
[9]
I. Guy, I. Ronen, and E. Wilcox. Do you know?: recommending people to invite into your social network. In Proceedings of the 14th international conference on Intelligent user interfaces, pages 77--86, 2009.
[10]
C. Hall and M. Zarro. Social curation on the website pinterest.com. Proceedings of the American Society for Information Science & Technology, 49(1):1--9, 2012.
[11]
J. Hannon, M. Bennett, and B. Smyth. Recommending twitter users to follow using content and collaborative filtering approaches. In Proceedings of the fourth ACM conference on Recommender systems, pages 199--206, 2010.
[12]
J. Hannon, K. McCarthy, and B. Smyth. Finding useful users on twitter: Twittomender the followee recommender. In Proceedings of the 33rd European conference on Advances in information retrieval, pages 784--787, 2011.
[13]
T. Hofmann and J. Puzicha. Latent class models for collaborative filtering. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pages 688--693, 1999.
[14]
Y. Jing, L. Wu, X. Zhang, D. Wang, and C. Chen. Recommending users to follow based on user taste homophily for content curation social networks. In 2015 ACM SIGKDD Social Recommender System Workshop, Sydney, Australia, 2015.
[15]
K. Y. Kamath, A.-M. Popescu, and J. Caverlee. Board recommendation in pinterest. In Proceedings of the 21st Conference on User Modeling, Adaptation and Personalization, 2013.
[16]
Y. Kim and K. Shim. Twitobi: A recommendation system for twitter using probabilistic modeling. In Proceedings of the 2011 IEEE 11th International Conference on Data Mining, pages 340--349, 2011.
[17]
Y. Koren. Factorization meets the neighborhood: a multifaceted collaborative filtering model. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 426--434, 2008.
[18]
D. Liu, C. Lu, and K. Mohan. Pinterest analysis and recommendations. Course report. Stanford University, 2014.
[19]
M. McPherson, L. Smith-Lovin, and J. M. Cook. Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1):415--444, 2001.
[20]
R. Ottoni, D. L. Casas, J. P. Pesce, W. Meira Jr., C. Wilson, A. Mislove, and V. Almeida. Of pins and tweets: Investigating how users behave across image- and text-based social networks. In Proceedings of the 8th International AAAI Conference on Weblogs and Social Media, page 344, 2014.
[21]
R. Ottoni, J. P. Pesce, D. L. Casas, G. Franciscani Jr., W. Meira Jr., P. Kumaraguru, and V. Almeida. Ladies first: Analyzing gender roles and behaviors in pinterest. In Proceedings of the 7th International AAAI Conference on Weblogs and Social Media, 2013.
[22]
D. M. Pennock, E. J. Horvitz, S. Lawrence, and C. L. Giles. Collaborative filtering by personality diagnosis: A hybrid memory- and model-based approach. In Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, pages 473--480, 2000.
[23]
P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: an open architecture for collaborative filtering of netnews. In Proceedings of the 1994 ACM conference on Computer supported cooperative work, pages 175--186, 1994.
[24]
B. M. Sarwar, G. Karypis, J. A. Konstan, and J. T. Riedl. Application of dimensionality reduction in recommender system - a case study. In ACM WebKDD Workshop, pages 82--90, 2000.
[25]
J. Stan, P. Maret, and V.-H. Do. Semantic user interaction profiles for better people recommendation. In Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining, pages 434--437, 2011.
[26]
S. Wan, Y. Lan, J. Guo, C. Fan, and X. Cheng. Informational friend recommendation in social media. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval, pages 1045--1048, 2013.
[27]
S. M. Weiss and N. Indurkhya. Lightweight collaborative filtering method for binary-encoded data. In Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery, pages 484--491, 2001.
[28]
S.-H. Yang, B. Long, A. Smola, N. Sadagopan, Z. Zheng, and H. Zha. Like like alike: joint friendship and interest propagation in social networks. In Proceedings of the 20th international conference on World wide web, pages 537--546, 2011.
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Published In
August 2016
360 pages
ISBN:9781450348508
DOI:10.1145/3007669
Copyright © 2016 ACM.
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Published: 19 August 2016
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ICIMCS'16
ICIMCS'16: International Conference on Internet Multimedia Computing and Service
August 19 - 21, 2016
Xi'an, China
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ICIMCS'16 Paper Acceptance Rate 77 of 118 submissions, 65%;
Overall Acceptance Rate 163 of 456 submissions, 36%
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View all- Liu HDeng SWu LJian MYang BZhang D(2020)Recommendations for Different Tasks Based on the Uniform Multimodal Joint RepresentationApplied Sciences10.3390/app1018617010:18(6170)Online publication date: 4-Sep-2020
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