پیاده سازی سیستم‌های توصیه گر هتلها با استفاده از اولویت‌های کاربران در توییتر

نوع مقاله: مقاله پژوهشی

نویسندگان

1 مربی دانشگاه علمی و کاربردی

2 مربی، عضو هیئت‌علمی، گروه علمی مهندسی کامپیوتر و فناوری اطلاعات، دانشگاه پیام نور،تهران.

3 استادیار، دانشگاه علوم پزشکی ایران، تهران.

چکیده

استفاده از سیستم‌های توصیه ­گر افزایش فروش در تجارت الکترونیک را اثبات نموده است. هدف سیستم ارائه محصولاتی به کاربر است که متضمن علاقه­مندی و آسایش او از محصولات بوده و همچنین در شرکت‌ها شانس فروش خدمات را ارتقا دهد. یکپارچه­سازی تکنیک‌های مدیریت داده می­تواند مسائل مرتبط با ارائه خدمات منطبق با علایق مشتری را مخاطب قرار داده و کیفیت پیشنهاد‌ها را به طرز چشمگیری بهبود دهد. تحقیقات اخیر بر روی این سیستم ایده استفاده از داده­های شبکه اجتماعی به‌منظور ارتقا سیستم توصیه­گر سنتی و پیش­بینی بهتر را آشکار می­سازد. ما دیدگاه­های سیستم‌های توصیه­­گر مبتنی بر داده­ شبکه اجتماعی توییتر را با استفاده از انواع رابط‌ها، روش‌های تجزیه‌وتحلیل محتوا با تکنیک‌های زبان­شناسی محاسباتی و الگوریتم تاپیک مدلینگ ملت بیان می­کنیم. پس از بررسی عمق اهداف، متدولوژی‌ها این مقاله به علاقه­مندان در توسعه سیستم توصیه­گر سفر و همچنین تسهیل تحقیقات آینده کمک می­نماید.
 

کلیدواژه‌ها


عنوان مقاله [English]

Schematic Design of Hotel Recommendation Systems by user Precedence on Twitter

نویسندگان [English]

  • Venus Mohammadi 1
  • Mohsen Hosseinzadeh 2
  • Mehdi Hosseinzadeh Hosseinzadeh 3
1 Department of Electrical and Computer Engineering, Islamic Azad University, Science and Research Branch, Tehran.
2 PNU, faculty member, Department of Computer Engineering and Information Technology. Payame Noor University, Tehran
3 Iran University of Medical sciences, Tehran .(Corresponding Author: hosseinzadeh.m@lums.ac.ir)
چکیده [English]

v
Recommender systems utilization has proven sales enhancement in most e-commerce platforms. This system objected to provide more options, comfort and flexibility to user which could make him interested, as well as providing better chance for companies to increase sells in their products and services. Flourishing popularity of web site has originated intrigue for recommendation systems. By penetrating in infinite fields, recommendation systems give deceptive suggestion on services compatible with user precedence. Integrating recommender systems by data management techniques to can targeted such issues and quality of suggestions will be improved considerably. Recent research reveals an idea of utilizing social network data to refine weakness points of traditional recommender system and improve prediction accuracy and efficiency. In this paper we represent views of recommender systems based on Twitter social network data by usage of variety interfaces, content analysis Methods, computational linguistics techniques and MALLET topic modeling algorithm. By deep exploration of objects, methodologies and available data sources, this paper will helps interested people to develop travel recommendation systems and facilitates future research by achieved direction.

کلیدواژه‌ها [English]

  • recommender system
  • Social Network
  • Twitter
  • Topic Modeling
 

ﺟﻌﻔﺮی، ﺳﯿﺪ ﻣﺤﻤﺪﺑﺎﻗﺮ. ﮐﺮﯾﻤﯽ، آﺻﻒ. اﺑﺮﻗﻮی زاده، زاﻫﺪه. (۱۳۹۵) "ﻋﻮاﻣﻞ ﺗﺄﺛﯿﺮﮔﺬار ﺑﺮ ﺗﻤﺎﯾﻞ ﺑﻪ اداﻣﻪ اﺳﺘﻔﺎده از وب‌سایت شبکه‌های اﺟﺘﻤﺎعی"، ﻓﺼﻠﻨﺎﻣﻪ ﻣﻄﺎﻟﻌﺎت ﻣﺪﯾﺮﯾﺖ ﻓﻨﺎوری اﻃﻼﻋﺎت، پاییز، ﺳﺎل ﭘﻨﺠﻢ، ﺷﻤﺎره 17.

 شیرخدایی، میثم. حسینی، ابوالحسن. ابراهیم زاده گنجی، سیده زینب.(1395)،"تبیین نقش عوامل مؤثر بر اعتماد الکترونیک در وب‌سایت‌های گردشگری"، فصلنامه مطالعات مدیریت گردشگری، پاییز، دوره 11، شماره 34.

Atkin CK, Rice RE, Valdivia, (2012). AN: Advances in public communication campaigns. The International Encyclopedia of Media Studies, Volume 5. Edited by Scharrer E. London, UK: Wiley-Blackwell Publishing; 526–551

Bao, Y., Fang, H., Zhang, J., (2014). TOPICMF: simultaneously exploiting ratings and reviews for recommendation, Proceedings of the 28th AAAI Conference onArtificial Intelligence (AAAI), pp. 2–8.

Basiri J., Shakery A., Moshiri B., Zi Hayat M, (2010). Alleviating the cold-start problem of recommender systems using a new hybrid approach, 5th International Symposium on Telecommunications, IST 2010 Tehran, Iran; pp. 962–967.

Bessho F, Hara20da T, Kuniyoshi Y,( 2012). Dialog system using real-time crowdsourcing and Twitter large-scale corpus, Proceedings of theProceedings ofthe 13th Annual Meeting of the Special Interest Group on Discourse andDialogue;5-6 July 2012. Seoul, South Korea, Stroudsburg, PA: Association for Computational Linguistics; 227–231.

Blanco-Ferna ´ndez, Y., Lo ´pez-Nores, M., Pazos-Arias, J.J., Garcı´a-Duque, J.,(2011).An improvement for semantics-based recommender systems grounded on attaching temporal information to ontologies and user profiles, Engineering Applications of Artificial Intelligence. 24, 1385–1397

Blei DM, Ng AY, Jordan MI: Latent dirichlet allocation. J Mach Learn Res (2003), 3:993–1022

Bobadilla, J., Ortega, F., Hernando, A., GutiéRrez, A.,(2013). Recommender systems survey, Knowledge-Based Systems. 46, 109–132

Brenner J, Smith, (2013), A:72% of online adults are social networking site users. Pew Research Center’s Internet & American Life Project, 1615 L St., N.W., Suite 700, Washington, D.C. 20036.

Champiri Zohreh Dehghani, Seyed Reza Shahamiri,Siti Salwah Binti Salim,(2014). A systematic review of scholar context-aware recommender systems, Expert System with Application, Volume 42, Issue 3, 1743-1758.

Chen He, Denis Parra, Katrien Verbert,(2016).Interactive recommender systems: a survey of the state of the art and future research challenges and opportunities, Expert Systems With Applications,Volume 56, 9-27.

Chen, C., Zheng, X., Wang, Y., Hong, F., Lin, Z., (2014). Context-aware collaborative topic regression with social matrix factorization for recommender systems, Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI), pp. 9–15.

Colombo-Mendoza Luis Omar, Rafael Valencia-García, Alejandro Rodríguez-González, Giner Alor-Hernández, José Javier Samper-Zapater, RecomMetz, (2015). A context-aware knowledge-based mobile recommender system for movie showtimes, Expert Systems with Applications, Volume 42, Issue 3, 1202-1222.

Das D,(2013).Evolution, rapid growth & future of research on electronic word of mouth (ewom), a scientific review. Available at Social Science Research Network, papers.ssrn.com

Goel, A., Gupta, P., Sirois, J., Wang, D., Sharma, A., Gurumurthy, S.,(2015). The who-to-follow system at twitter: strategy, algorithms, and revenue impact, Interface, 45 (1), 98–107.  

Isabel Cenamor, Tomás de la Rosa, Sergio Núñez, Daniel Borrajo,( 2017). Planning for tourism routes using social networks, Expert Systems with Applications,Volume 69, Pages 1-9.

Li Yung-Ming, Han-Wen Hsiao, Yi-Lin Lee,(2013).Recommending social network applications via social filtering mechanisms, Information Sciences, Volume 239,18-30.

Liu Wenyu, Caihua Wu, Bin Fenga, Juntao Liu,(2014). Conditional Preference in Recommender Systems, Expert Systems with Applications, Volume 42, Issue 2, 774-788.

Liu, X., Aberer, K., (2013). SOCO: A social network aided context-aware recommender system, Proceedings of the 22nd International Conference on World WideWeb (WWW), pp. 781–802.

Lops, P., Gemmis, M., Semeraro, G.,(2011). Content-based recommender systems: state of the art and trends, In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (Eds.), Recommender Systems Handbook. Springer, Boston, MA.

Lu Jie, Dianshuang Wu, Mingsong Mao, Wei Wang, Guangquan Zhang, (2015).Recommender System Application Developments: A Survey, Decision Support Systems, Volume 74,12-32.

Moreno Antonio, Valls Aida, Isern David, Marin Lucas, Borras Joan, (2013). SigTur/E-Destination: Ontology-based personalized recommendation of Tourism and Leisure Activities, Engineering Applications of Artificial Intelligence, Volume 26, Issue 1, 633-651.

Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.,(2011). Recommender systems with social regularization, Proceedings of the 4th ACM International Conference onWeb Search and Data Mining, pp. 287–296.

Mallet. [http://mallet.cs.umass.edu/]

Pawel Ladyzynski, Przemyslaw Grzegorzewski,(2015).Vague Preferences in Recommender Systems, Expert System with Application, Volume 42, Issue 24, 9402-9411.

Queiroz da Silva Edjalma, Celso G. Camilo-Junior, Luiz Mario L. Pascoal, Thierson C. Rosa, (2016). An evolutionary approach for combining results of recommender systems techniques based on collaborative filtering, Expert Systems with Applications,Volume 53, 204-218.

Rao Kagita Venkateswara, Arun K. Pujari, Vineet Padmanabhan,(2015). Virtual user approach for group recommender systems using precedence relations, Information Sciences, Volume 294, 15-30.

Ravi L., Vairavasundaram S.,(2016).A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users, Hindawi Publishing Corporation Computational Intelligence and Neuroscience.

Ricci, F., Nguyen, Q.N., Averianova, O.,(2009). Exploiting a map-based interface in conversational recommender systems for mobile travelers, Sharda N, editor. Tourism Informatics: Visual Travel Recommender Systems, Social Communities, and User Interface Design: IGI Global, Information Science Reference; pp. 73–93.

Romero, D.M., Kleinberg, J.M.(2010). The directed closure process in hybrid socialinformation networks, with an analysis of link formation on Twitter, Proceedings of the Fourth International Conference on Weblogs and Social Media(ICWSM 2010), Washington, DC, USA.

Rowe, M., Stankovic, M., Alani, H.,(2012). Who will follow whom? Exploiting semantics for link prediction in attention-information networks, The Semantic Web—ISWC 2012, Lecture Notes in Computer Science, vol. 7649. Springer-Verlag, Springer, Berlin, Heidelberg.

Su, X., Khoshgoftaar, T.M.,(2009). A survey of collaborative filtering techniques, Advances in Artificial Intelligence, Volume 2009,p 19.

Sun Zhoubao, Lixin Han, Wenliang Huang, Xueting Wang, Xiaoqin Zeng, Min Wang, Hong Yan,(2015).Recommender systems based on social networks, The Journal of Systems and Software, Volume 99,109-119.

Tang, J., Hu, X., Liu, H.,(2013). Social recommendation: a review. Social Network Analysis and Mining, Volume 3, Issue 4, pp 1113–1133.

Toledo Raciel Yera, Yailé Caballero Mota, Luis Martínez,(2015).Correcting noisy ratings in collaborative recommender systems, Knowledge-Based Systems, Volume 76, 96-108.

Tommasel Antonela, Corbellini Alejandro, Godoy Daniela,  Schiaffino Silvia (2016).Personality-aware followee recommendation algorithms: An empirical analysis, Engineering Applications of Artificial Intelligence, Volume 51, 24-36.

Vaccari Sundermanna Camila, Marcos Aur´elio Domingues, Merley da Silva Conradoa,1, Solange Oliveira Rezende, (2016).Privileged Contextual Information for Context-Aware Recommender Systems, Expert Systems with Applications, Volume 57, 139-158.

Veijalainen Jari, Alexander Semenov, Miika Reinikainen,(2015),User Influence and Follower Metrics in a Large Twitter Dataset, Proceedings of the 11th International Conference on Web Information Systems and Technologies (WEBIST-2015), pages 487-497.

Wang, C., Blei, D.M.,(2011), Collaborative topic modeling for recommending scientific articles, Proceedings of the 17th ACM SIGKDD International Conference onKnowledge Discovery and Data Mining (SIGKDD), pp. 448–456.

Wang, H., Chen, B., Li, W.-J.,(2013). Collaborative topic regression with social regularization for tag recommendation, Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), pp. 2719–2725.

Wang, H., Li, W.-J.,(2014). Relational collaborative topic regression for recommender systems, IEEE Transactions on Knowledge and Data Engineering (TKDE), Volume 27, Issue 5,1343 - 1355.

Wei Jian, Jianhua He, Kai Chen, Yi Zhou, Zuoyin Tang, (2016). Collaborative Filtering and Deep Learning Based Recommendation System for Cold Start Items, Expert Systems with Applications, Volume 69, 29-39.

Xu Yueshen, Yin Jianwei,(2015). Collaborative recommendation with user generated content, Engineering Applications of Artificial Intelligence, Volume 45,281-294.

Yang Xiwang, Yang Guo, Yong Liu, Harald Steck,(2014). A survey of collaborative filtering based social recommender systems, Computer Communications, Volume 41,1-10.

Yang, X., Steck, H., Liu, Y.,(2012). Circle-based recommendation in online social networks, Proceedings of the 18th ACM SIGKDD International Conference onKnowledge Discovery and Data Mining (SIGKDD), pp. 1267–1275.

 

ﺟﻌﻔﺮی، ﺳﯿﺪ ﻣﺤﻤﺪﺑﺎﻗﺮ. ﮐﺮﯾﻤﯽ، آﺻﻒ. اﺑﺮﻗﻮی زاده، زاﻫﺪه. (۱۳۹۵) "ﻋﻮاﻣﻞ ﺗﺄﺛﯿﺮﮔﺬار ﺑﺮ ﺗﻤﺎﯾﻞ ﺑﻪ اداﻣﻪ اﺳﺘﻔﺎده از وب‌سایت شبکه‌های اﺟﺘﻤﺎعی"، ﻓﺼﻠﻨﺎﻣﻪ ﻣﻄﺎﻟﻌﺎت ﻣﺪﯾﺮﯾﺖ ﻓﻨﺎوری اﻃﻼﻋﺎت، پاییز، ﺳﺎل ﭘﻨﺠﻢ، ﺷﻤﺎره 17.

 شیرخدایی، میثم. حسینی، ابوالحسن. ابراهیم زاده گنجی، سیده زینب.(1395)،"تبیین نقش عوامل مؤثر بر اعتماد الکترونیک در وب‌سایت‌های گردشگری"، فصلنامه مطالعات مدیریت گردشگری، پاییز، دوره 11، شماره 34.

Atkin CK, Rice RE, Valdivia, (2012). AN: Advances in public communication campaigns. The International Encyclopedia of Media Studies, Volume 5. Edited by Scharrer E. London, UK: Wiley-Blackwell Publishing; 526–551

Bao, Y., Fang, H., Zhang, J., (2014). TOPICMF: simultaneously exploiting ratings and reviews for recommendation, Proceedings of the 28th AAAI Conference onArtificial Intelligence (AAAI), pp. 2–8.

Basiri J., Shakery A., Moshiri B., Zi Hayat M, (2010). Alleviating the cold-start problem of recommender systems using a new hybrid approach, 5th International Symposium on Telecommunications, IST 2010 Tehran, Iran; pp. 962–967.

Bessho F, Hara20da T, Kuniyoshi Y,( 2012). Dialog system using real-time crowdsourcing and Twitter large-scale corpus, Proceedings of theProceedings ofthe 13th Annual Meeting of the Special Interest Group on Discourse andDialogue;5-6 July 2012. Seoul, South Korea, Stroudsburg, PA: Association for Computational Linguistics; 227–231.

Blanco-Ferna ´ndez, Y., Lo ´pez-Nores, M., Pazos-Arias, J.J., Garcı´a-Duque, J.,(2011).An improvement for semantics-based recommender systems grounded on attaching temporal information to ontologies and user profiles, Engineering Applications of Artificial Intelligence. 24, 1385–1397

Blei DM, Ng AY, Jordan MI: Latent dirichlet allocation. J Mach Learn Res (2003), 3:993–1022

Bobadilla, J., Ortega, F., Hernando, A., GutiéRrez, A.,(2013). Recommender systems survey, Knowledge-Based Systems. 46, 109–132

Brenner J, Smith, (2013), A:72% of online adults are social networking site users. Pew Research Center’s Internet & American Life Project, 1615 L St., N.W., Suite 700, Washington, D.C. 20036.

Champiri Zohreh Dehghani, Seyed Reza Shahamiri,Siti Salwah Binti Salim,(2014). A systematic review of scholar context-aware recommender systems, Expert System with Application, Volume 42, Issue 3, 1743-1758.

Chen He, Denis Parra, Katrien Verbert,(2016).Interactive recommender systems: a survey of the state of the art and future research challenges and opportunities, Expert Systems With Applications,Volume 56, 9-27.

Chen, C., Zheng, X., Wang, Y., Hong, F., Lin, Z., (2014). Context-aware collaborative topic regression with social matrix factorization for recommender systems, Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI), pp. 9–15.

Colombo-Mendoza Luis Omar, Rafael Valencia-García, Alejandro Rodríguez-González, Giner Alor-Hernández, José Javier Samper-Zapater, RecomMetz, (2015). A context-aware knowledge-based mobile recommender system for movie showtimes, Expert Systems with Applications, Volume 42, Issue 3, 1202-1222.

Das D,(2013).Evolution, rapid growth & future of research on electronic word of mouth (ewom), a scientific review. Available at Social Science Research Network, papers.ssrn.com

Goel, A., Gupta, P., Sirois, J., Wang, D., Sharma, A., Gurumurthy, S.,(2015). The who-to-follow system at twitter: strategy, algorithms, and revenue impact, Interface, 45 (1), 98–107.  

Isabel Cenamor, Tomás de la Rosa, Sergio Núñez, Daniel Borrajo,( 2017). Planning for tourism routes using social networks, Expert Systems with Applications,Volume 69, Pages 1-9.

Li Yung-Ming, Han-Wen Hsiao, Yi-Lin Lee,(2013).Recommending social network applications via social filtering mechanisms, Information Sciences, Volume 239,18-30.

Liu Wenyu, Caihua Wu, Bin Fenga, Juntao Liu,(2014). Conditional Preference in Recommender Systems, Expert Systems with Applications, Volume 42, Issue 2, 774-788.

Liu, X., Aberer, K., (2013). SOCO: A social network aided context-aware recommender system, Proceedings of the 22nd International Conference on World WideWeb (WWW), pp. 781–802.

Lops, P., Gemmis, M., Semeraro, G.,(2011). Content-based recommender systems: state of the art and trends, In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (Eds.), Recommender Systems Handbook. Springer, Boston, MA.

Lu Jie, Dianshuang Wu, Mingsong Mao, Wei Wang, Guangquan Zhang, (2015).Recommender System Application Developments: A Survey, Decision Support Systems, Volume 74,12-32.

Moreno Antonio, Valls Aida, Isern David, Marin Lucas, Borras Joan, (2013). SigTur/E-Destination: Ontology-based personalized recommendation of Tourism and Leisure Activities, Engineering Applications of Artificial Intelligence, Volume 26, Issue 1, 633-651.

Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.,(2011). Recommender systems with social regularization, Proceedings of the 4th ACM International Conference onWeb Search and Data Mining, pp. 287–296.

Mallet. [http://mallet.cs.umass.edu/]

Pawel Ladyzynski, Przemyslaw Grzegorzewski,(2015).Vague Preferences in Recommender Systems, Expert System with Application, Volume 42, Issue 24, 9402-9411.

Queiroz da Silva Edjalma, Celso G. Camilo-Junior, Luiz Mario L. Pascoal, Thierson C. Rosa, (2016). An evolutionary approach for combining results of recommender systems techniques based on collaborative filtering, Expert Systems with Applications,Volume 53, 204-218.

Rao Kagita Venkateswara, Arun K. Pujari, Vineet Padmanabhan,(2015). Virtual user approach for group recommender systems using precedence relations, Information Sciences, Volume 294, 15-30.

Ravi L., Vairavasundaram S.,(2016).A Collaborative Location Based Travel Recommendation System through Enhanced Rating Prediction for the Group of Users, Hindawi Publishing Corporation Computational Intelligence and Neuroscience.

Ricci, F., Nguyen, Q.N., Averianova, O.,(2009). Exploiting a map-based interface in conversational recommender systems for mobile travelers, Sharda N, editor. Tourism Informatics: Visual Travel Recommender Systems, Social Communities, and User Interface Design: IGI Global, Information Science Reference; pp. 73–93.

Romero, D.M., Kleinberg, J.M.(2010). The directed closure process in hybrid socialinformation networks, with an analysis of link formation on Twitter, Proceedings of the Fourth International Conference on Weblogs and Social Media(ICWSM 2010), Washington, DC, USA.

Rowe, M., Stankovic, M., Alani, H.,(2012). Who will follow whom? Exploiting semantics for link prediction in attention-information networks, The Semantic Web—ISWC 2012, Lecture Notes in Computer Science, vol. 7649. Springer-Verlag, Springer, Berlin, Heidelberg.

Su, X., Khoshgoftaar, T.M.,(2009). A survey of collaborative filtering techniques, Advances in Artificial Intelligence, Volume 2009,p 19.

Sun Zhoubao, Lixin Han, Wenliang Huang, Xueting Wang, Xiaoqin Zeng, Min Wang, Hong Yan,(2015).Recommender systems based on social networks, The Journal of Systems and Software, Volume 99,109-119.

Tang, J., Hu, X., Liu, H.,(2013). Social recommendation: a review. Social Network Analysis and Mining, Volume 3, Issue 4, pp 1113–1133.

Toledo Raciel Yera, Yailé Caballero Mota, Luis Martínez,(2015).Correcting noisy ratings in collaborative recommender systems, Knowledge-Based Systems, Volume 76, 96-108.

Tommasel Antonela, Corbellini Alejandro, Godoy Daniela,  Schiaffino Silvia (2016).Personality-aware followee recommendation algorithms: An empirical analysis, Engineering Applications of Artificial Intelligence, Volume 51, 24-36.

Vaccari Sundermanna Camila, Marcos Aur´elio Domingues, Merley da Silva Conradoa,1, Solange Oliveira Rezende, (2016).Privileged Contextual Information for Context-Aware Recommender Systems, Expert Systems with Applications, Volume 57, 139-158.

Veijalainen Jari, Alexander Semenov, Miika Reinikainen,(2015),User Influence and Follower Metrics in a Large Twitter Dataset, Proceedings of the 11th International Conference on Web Information Systems and Technologies (WEBIST-2015), pages 487-497.

Wang, C., Blei, D.M.,(2011), Collaborative topic modeling for recommending scientific articles, Proceedings of the 17th ACM SIGKDD International Conference onKnowledge Discovery and Data Mining (SIGKDD), pp. 448–456.

Wang, H., Chen, B., Li, W.-J.,(2013). Collaborative topic regression with social regularization for tag recommendation, Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), pp. 2719–2725.

Wang, H., Li, W.-J.,(2014). Relational collaborative topic regression for recommender systems, IEEE Transactions on Knowledge and Data Engineering (TKDE), Volume 27, Issue 5,1343 - 1355.

Wei Jian, Jianhua He, Kai Chen, Yi Zhou, Zuoyin Tang, (2016). Collaborative Filtering and Deep Learning Based Recommendation System for Cold Start Items, Expert Systems with Applications, Volume 69, 29-39.

Xu Yueshen, Yin Jianwei,(2015). Collaborative recommendation with user generated content, Engineering Applications of Artificial Intelligence, Volume 45,281-294.

Yang Xiwang, Yang Guo, Yong Liu, Harald Steck,(2014). A survey of collaborative filtering based social recommender systems, Computer Communications, Volume 41,1-10.

Yang, X., Steck, H., Liu, Y.,(2012). Circle-based recommendation in online social networks, Proceedings of the 18th ACM SIGKDD International Conference onKnowledge Discovery and Data Mining (SIGKDD), pp. 1267–1275.