This document provides an outline and table of contents for a review of research on recommender systems between 2010-2014. It includes sections on the definition of recommender systems, the types of filtering algorithms used, year-wise distribution of papers published, applications of recommender systems, datasets used, categorization of papers by application and technique, measurement of recommendation quality, and recent trends. Tables list the top journals, cited papers, and distribution of papers by publisher from the collected research on recommender systems.

2. Contents
• What is a recommender system?
• Year wise distribution of research work on recommender system(2010-2014)
• What characterizes a recommender system?
• An year wise distribution of papers published between 2010-2014 in the field of
Recommender System using the Collaborative Filtering
• Why a recommender system?
• Applications of recommender system
• Outline of the review process
• Distribution of Papers collected on the Basis of Publishers
• Publications of Top Journals from the collected set of papers
• Top-10 cited papers from the collected set of papers
• Top-10 cited papers between 2010-2014 & 2013-2014 from the collected set of
papers
• Datasets
• Application & technique based categorization of the collected papers
• How to measure the recommendation quality ?
• Commercial existence of Recommender System
• Recent research trends and future prospects
• Reference list
I & S E |IIT KGP

3. Recommender System
 Recommender Systems collect information on the
preferences of its users for a set of items, say, movies, songs,
books, gadgets. [Bobadilla, Ortega et al.(2013)].
 They suggest items to the users based on the features of the
items or user’s preferences.
 The recommender system can acquire information implicitly
by monitoring user’s behavior [Cho , Lee et. al.(2010)] and
explicitly by collecting user’s rating to any item [Cho , Lee et.
al.(2010)].
I & S E |IIT KGP

4. A few examples:
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- While purchasing a book on Recommender Systems on Amazon.com, some other books are also
recommended

5. I & S E |IIT KGP
A few examples:
- While using facebook, some new groups or/and friends are suggested

6. An year wise distribution of papers published between
2010-2014 in the field of Recommender System
I & S E |IIT KGP
The numbers indicated are the sum of numbers obtained by searching papers using
the keywords “recommender system/application of recommender systems” in the
libraries of IEEE, INFORMS, ELSEVIER.

7. What Characterizes a
Recommender System?
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Types of
filtering
algorithms
Collaborative
filtering
Content-
based
filtering
Demographic
Filtering
Hybrid
filtering
• RS is characterized by the filtering algorithm used in it
[Adomavicius, Tuzhilin et.al.(2005), Candillier, Meye et.al.(2007)],
the different filtering algorithms are cited below:

8. An year wise distribution of papers published
between 2010-2014 in the field of Recommender
System using the Collaborative Filtering
I & S E |IIT KGP
The numbers indicated are the sum of numbers obtained by searching papers using the
keywords “recommender system/application of recommender systems” in the libraries
of IEEE, INFORMS, ELSEVIER.

9. Why a Recommender System?
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NEED OF
RECOMMENDER
SYSTEM
For Service
Provider
Helps in deciding
what kind of
offerings should be
made to the user
For the User
Helps in choosing
among a large
number of articles
or products

10. I & S E |IIT KGP
APPLICATION OF
RECOMMENDER
SYSTEM
Movie and Music
Recommendation
Books Search
E-Commerce
Travel/Tourism
Recommendation
Web Search

11. Outline of the Review Process
I & S E |IIT KGP
Step 1
• We collected a Primary Set of 152 research papers published in the field of
Recommender system in between 2014-15 form reputed journals of the
publishers like IEEE, Science Direct, INFORMS, SPRINGER, ACM, WILEY.
Step 2
• All the references of the papers of Primary Set are merged and the
irrelevant ( the papers not required to understand the growing research
trend in RS or which were published before 2000 )and repetitive
references are omitted to form a Secondary Set of 135 papers. These
papers are also from the above mentioned publishers.
Step 3
• All the papers from both sets are tabulated using a 2-D criteria.
• The first dimension is the Name of the Publisher and then the next is the
name of the Journal.

12. Distribution of Papers collected
on the Basis of Publishers
I & S E |IIT KGP
IEEE - 75(25 Journals)
INFORMS - 21(6 Journals)
ELSEVIER - 126(22
Journals)
SPRINGER - 27(10
Journals)
ACM - 29(8 Journals)
WILEY - 9(4 Journals)

13. Papers published in Top Journals of
the collected set of papers
I & S E |IIT KGP
IEEE
Journal No. of papers published Impact Factor (2013)
Knowledge and Data Engineering 15 1.815
Multimedia 8 1.767
Learning Technologies 7 1.22
Intelligent Systems 7 1.92
Internet Computing 7 2.0
INFORMS
Journal No. of papers published Impact Factor (2013)
Management Science 6 2.524
Informs Journal on Computing 6 1.12
Operations Research 3 1.5
Marketing Science 3 2.208
Information Systems Research 2 2.322

14. Papers published in Top Journals of
the collected set of papers
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ELSEVIER
Journal No. of papers published Impact Factor (2013)
Experts Systems with Applications 48 1.965
Knowledge based Systems 19 3.058
Computers in Human Behavior 5 2.273
Information Sciences 10 3.893
Decision Support Systems 13 2.036
SPRINGER
Journal No. of papers published Impact Factor (2013)
Multimedia Tools Application 3 1.058
Information Retrieval 3 0.625
Soft Computing 2 1.304
Journal of Intelligent Information 1 0.632
Knowledge & Information Systems 1 2.639

15. Papers published in Top Journals of
the collected set of papers
I & S E |IIT KGP
ACM
Journal No. of papers published Impact Factor (2013)
Information Systems 8 1.3
Computing Surveys 6 4.043
Multimedia Computing, Communications
and Applications
5 0.904
Internet Technology 4 0.577
Knowledge Discovery from Data 3 1.147
WILEY
Journal No. of papers published Impact Factor (2013)
Journal of the Association for Information
Science & Technology
4 2.23
International Journal of Communication
Systems
3 1.106
Software – Practice & Experience 1 1.148
International Journal of Intelligent Systems 1 1.411

16. Top-10 cited papers from the
collected set of papers
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Authors Citations
Adomavicius & Tuzhilin (2005) 5532
Herlocker, Konstan et al. (2004) 3484
Greg, Brent et al. (2003) 2952
Josang, Ismail et al. (2007) 2301
Dellarocas (2003) 2083
Deshpande & Karypis ( 2004) 1241
Goldberg, Roeder et al. (2001) 1007
Shim, Warkentin et al. (2002) 977
Adomavicius, Sankaranarayanan et al. (2005) 624
Francois, Alain et al. (2007) 568

17. Top-10 cited papers between 2010-
2014 from the collected set of papers
I & S E |IIT KGP
Authors Citations
Koren(2010) 242
Feng, Li et al. (2010) 210
Bobadilla, Ortega et al.(2013) 158
Liu & Lee(2010) 132
Lee(2012) 128
Cambria, Schuller et al.(2013) 117
Barragáns, Costa et al.(2010) 112
Bandyopadhyay & Sen(2011) 111
Park, Kim et al.(2012) 105
Verbert, Manouselis et al.(2012) 83

18. Top-10 cited papers between 2013-
2014 from the collected set of papers
I & S E |IIT KGP
Authors Citations
Bobadilla, Ortega et al.(2013) 158
Cambria, Schuller et al.(2013) 117
Mostafa (2013) 42
Yang, Guo et al.(2013) 40
Tao, Yong et al.(2014) 24
Shi, Larson et al.(2014) 15
Liu, Chen et al.(2014) 9
Bi, Xu et al.(2014) 8
Kaklauskas, Zavadskas et al.(2013) 8
Charu, Yuchen et al.(2014) 6

19. Datasets
The various datasets used while researching the recommenders systems are cited here:
• Netflix- The dataset provided by Netflix Inc. which is an online video service provider
• MovieLens- The dataset provided by MovieLens. It is website that recommends
movies to its users (http://grouplens.org/datasets/movielens/)
• Sushi- This dataset stores responses of questionnaire survey and the demographic
data of the respondents (http://www.kamishima.net/sushi)
• Wikilens- It is dataset of Wikilens recommender system which facilitated its
community to define item types and categories to be rated
(http://grouplens.org/datasets/wikilens)
• The TAQ dataset- It provides historical data of trades and quotes(TAQ) for all issues
traded on NYSE, NASDAQ and Regional stock exchanges
(http://www.nyxdata.com/Data-Products/Daily-TAQ)
• Jester- The dataset used while online joke recommendation.
(http://goldberg.berkeley.edu/jesterdata)
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20. Datasets
• Delicious Bookmarks- This dataset contains social networking, bookmarking, and
tagging information. (http://grouplens.org/datasets/hetrec-2011/)
• Choose4Greece- This dataset has social voting features . It was launched during
the Greek national elections of 2012 .
(http://www.preferencematcher.com/datasets/)
• Art of the Mix(AotM)- This dataset is used in researches in music recommendation
and contains the playlist retrieved from the Art of the Music website.
(http://labrosa.ee.columbia.edu/projects/musicsim/)
• Last.fm- This is a song recommendation dataset. It is created using the last.fm API.
(http://labrosa.ee.columbia.edu/millionsong/lastfm#getting)
I & S E |IIT KGP

21. Application based categorization
of the collected papers
I & S E |IIT KGP
Area Authors
Music recommendation Turnbull, Barrington et al. (2008), Cornelis, Lesaffre et
al.(2010), Jacobson, Fields et al. (2011), Chen, Jang et al.
(2011), Bonnin & Jannach (2014), Lee & Lee(2014),
Horsburgh, Craw et al.(2015) .
Movie recommendation Ricci(2002),Hosanagar & Fleder(2009),Yu, Liu et al.(2012),
Carrer, Hernández et al.(2012), Choi, Ko et al.(2012), Uysal
& Gunal(2012), Eliashberg, Hui et al.(2014), Briguez,
Budán et al.(2014), Mendoza, Garcia et al.(2015).
Travel/Tourism Shih,Yen et al.(2011), Batet, Moreno et al.(2012), Liu, Xu et
al.(2014), Tan, Liu et al.(2014), Liu, Chen et al.(2014),
Borràs, Moreno et al. (2014).

22. Application based categorization
of the collected papers
I & S E |IIT KGP
Area Authors
Text analysis Fung, Yu et al. (2005), Louloudis, Gatos et
al.(2008), Aghdam, Ghasem et al. (2009),Yin,
Liu(2009), Liu, Loh et al.(2009), Lee (2012), Maks
& Vossen(2012), Mostafa (2013), Zhao, Aggarwal
et al.(2014), Dnyanesh & Singh(2014), Ryu,
Hyung et al.(2014), Hao, Cao et al. (2014),
Hashimi, Hafez et al.(2015).
Sentiment Analysis Duric & Song(2012), Maks & Vossen (2012),
Uysal & Gunal (2012), Desmet & Hoste (2013),
Cambria, Schuller et al. (2013), Mostafa(2013),
Moraes & Valiat(2013), Xuan & Stieglitz(2013),
Hendrikx, Bubendorfer et al.(2015).

23. Technique based categorization of
the collected papers
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Technique Authors
Collaborative Filtering Linden, Smith et al.(2003), Greg, Brent et al.(2003),
Huang & Zang(2004), Hofmann T (2004), Herlocker,
Konstan et.al.(2004), Jin, Si et al.(2006), Bridge &
Ryan (2006), Fouss, Pirotte et al.(2007), Ahn(2008),
Serradilla, Bobadilla et al.(2009), Chen, Shtykh et
al.(2009), Liu & Lee(2010), Barranquero, Labra et
al.(2010), Koren (2010), Zhan, Hsieh et al.(2010),
Anand & Bharadwaj (2011), ), Huang, Zeng et
al.(2011), Bobadilla, Ortega et al. (2012), Cai, Leung
et al.(2014), Hsiao, Kulesza et al.(2014), Javari,
Gharibshah et al.(2014), Yang, Guo et al.(2014),
Rivera, Ruiz et al.(2014), Pereira, Lopes et al.(2014),
Li, Chena et al.(2014).

24. Technique based categorization of
the collected papers
I & S E |IIT KGP
Technique Authors
Collaborative Filtering Peng, Zhang et al.(2014), Zhanga, Yua et al. (2014),
Zhua, Renb et al. (2014), Liu, Hu et al.(2014), Toledo,
Mota et al. (2015), Nilashi, Jannach et al.(2015) ,
Krzywicki, Wobcke et el.(2015), Ghazarian &
Nematbakhsh (2015), Braida, Mello et al. (2015),
Valdez, Lovelle et al. (2015).
Fuzzy approaches Yager(2003), Cao & Li (2007), Norcio & Zenebe(2009),
Castellano, Fanelli et al.(2011), Nilashi, Ibrahim et
al.(2014), Son (2014), Zhang, Dianshuang et al.(2015),
Wang, Zeng et al.(2015), Gupta, Saini et al. (2015),
Thong, Son et al. (2015)A, Son & Thong (2015)B.

25. Technique based categorization of
the collected papers
I & S E |IIT KGP
Technique Authors
Hybrid Filtering Liu, Lai et al.(2009), Barragáns, Costa et al. (2010) ,
Park, Lee et al.(2013), Liu(2014), Son (2014) , Thong &
Son(2015)A ,Horsburgh, Craw et al.(2015).
Content-based
filtering
Norcio & Zenebe(2009), Barragáns, Costa et al. (2010),
Cornelis, Lesaffre et al.(2010), Chen, Jang et al. (2011),
Meng, Chia et al. (2014).
Bayesian
classifications
Yang, Guo et al.(2013), Liu &Wu et al.(2013), Tan, Liu
et al.(2014), Baoxing, Enhong et al.(2014), Wei, Barry
et al.(2014).

26. Survey papers in the set of
collected papers
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Authors
Surveys Josang, Ismail et al.(2007), Carpineto &
Romano (2012), Kim, Park et al. (2012),
Verbert,Manouselis et al.(2012),
Bobadilla, Ortega et al.(2013), Natalia,
Cue´ Llar et al. (2014), Bonnin &
Jannach(2014), Huai, Chen et al.(2014),
Woz´Niak , Grana et al.(2014),
Nassirtoussi, Aghabozorgi et al.(2014),
Champiri, Shahamiri et al. (2015).

27. How to measure the
recommendation quality ?
I & S E |IIT KGP
• A recommender system can be evaluated using quality
measures [Gunawardana & Shani(2009)] and evaluation
metrics [Hernandez , Gaudioso et.al.(2008)] and can be
categorized as:
Quality
Measures
Prediction
evaluations
Evaluations for
recommendation as
sets
Evaluations for
recommendation as
ranked lists

28. I & S E |IIT KGP
Evaluation
Metrics
Prediction Metrics
MAE, RMSE,
Coverage
Set
Recommendation
Metrics
Precision,
Recall, F1
Rank
Recommendation
Metrics
HL, DCGK
Diversity Metrics
Diversity
and Novelty

29. Commercial existence of
Recommender System
I & S E |IIT KGP
• A large number of recommender systems currently available on the Internet.
• Commercial websites devised tailored solution to help their user find items and
increase sale
• The following table describes special features of a few commercial systems:
System Feature
MovieLens •Collaborative filtering
•Builds a profile by asking the user to rate the movie
•Searches for similar profile
•Stochastic and heuristic models to improve profile
matching

30. I & S E |IIT KGP
System Feature
Pandora •Deep item analysis (Music Genome Project theory)
•User preference represented in term of a collection of items
Amazon •Combined approach (personalized, social and item based)
•Recommendation based on matching of: actual items, related
items, items other user purchased, new release, related items to
new release
Google •Customize search result based on location and recent search
activity(“when possible”)
•Customize results based on account history
•Uses pages link structure(social recommendation)
•Recommendation to closest match (The “Did You Mean”
feature)

31. Recent Research Trends & Future
Prospects
I & S E |IIT KGP
 Online reputation and polling systems[Krishnamurthy and
Holies(2014)]
 Social voting advice applications[Katakis, Tsapatsoulis et. al.
(2014)]
 The most valuable collaborators recommendation[Xia, Chen
et. al. (2014)]
 The concept of Internet of Things [Bi, Xu et. al.(2014)]

32. Recent Research Trends & Future
Prospects
I & S E |IIT KGP
 Dynamically promoting experts for recommendation [Lee and
Lee(2014)]
 Tree-based recommendation using fuzzy preferences [Wu,
Zhang et al.(2014)]
 Cloud computing and recommendation[Campo, Pegueroles
et al. (2014)]
 Security and privacy in filtering algorithms[Casino, Ferrer et
al.(2014)]
 Diversity improvement in recommendations[Adomavicius &
Kwon et al.(2014)

33. Recent Research Trends & Future
Prospects
I & S E |IIT KGP
 Evaluation of filtering algorithms using novel methods[ Banda
& Bhardwaj (2014)]
 Detection and correction of natural noise in filtering
techniques [Toledo, Mota et. al.(2015)]
 Fashion recommendation [Weng and Zeng,(2015)]
 Recommendation in medical and health[Thong, Son et.
al.(2015)]
 Recommendation system in bibliometrics and scientometrics
[Lorente, Porcel et. al.(2015)]

34. I & S E |IIT KGP
Reference List
• Guangtao Wang, Qinbao Song, Xueying Zhang, And Kaiyuan Zhang, A Generic Multilabel Learning-Based Classification
Algorithm Recommendation Method, ACM Transactions On Knowledge Discovery From Data, Vol. 9, No. 1, Article 7,
Publication Date: October 2014.
• Natalia D´Iaz Rodr´Iguez, M. P. Cue´ Llar, Johan Lilius, Miguel Delgado Calvo-Floresa, Survey On Ontologies For Human
Behavior Recognition, ACM Computing Surveys, Vol. 46, No. 4, Article 43, Publication Date: March 2014.
• Amin Javari And Mahdi Jalili, Accurate And Novel Recommendations: An Algorithm Based On Popularity Forecasting, ACM
Transactions On Intelligent Systems And Technology, Vol. 5, No. 4, Article 56, Publication Date: December (2014)A.
• Geoffray Bonnin And Dietmar Jannach, Automated Generation Of Music Playlists: Survey And Experiments, ACM
Computing Surveys, Vol. 47, No. 2, Article 26, Publication Date: November 2014
• Peter Christen, Dinusha Vatsalan, Vassilios S. Verykios, Challenges For Privacy Preservation In Data Integration, ACM Journal
Of Data And Information Quality, Vol. 5, Nos. 1–2, Article 4, Publication Date: August 2014.
• Yue Shi, Martha Larson, And Alan Hanjalic, Collaborative Filtering Beyond The User-Item Matrix: A Survey Of The State Of
The Art And Future Challenges, ACM Computing Surveys, Vol. 47, No. 1, Article 3, Publication Date: April 2014.
• Tao Mei, Yong Rui, Shipeng Li, & Qi Tian, Multimedia Search Re-ranking: A Literature Survey, ACM Computing Surveys, Vol.
46, No. 3, Article 38, Publication Date: January 2014
• Chang Tan, Qi Liu, Enhong Chen, Hui Xiong & Xiang Wu, Object-Oriented Travel Package Recommendation, ACM
Transactions On Intelligent Systems And Technology, Vol. 5, No. 3, Article 43, Publication Date: September 2014.
• Panagiotis Adamopoulos And Alexander Tuzhilin, On Unexpectedness In Recommender Systems: Or How To Better Expect
The Unexpected, ACM Transactions On Intelligent Systems And Technology, Vol. 5, No. 4, Article 54, Publication Date:
December 2014.
• Qinghua Huang, Bisheng Chen, Jingdong Wang And Tao Mei, Personalized Video Recommendation Through Graph
Propagation, ACM Transactions On Multimedia Computing, Communications And Applications, Vol. 10, No. 4, Article 32,
Publication Date: June 2014.
• Y. Song, L. Zhang, and C. L. Giles, “Automatic tag recommendation algorithms for social recommender systems,” ACM Trans.
Web, vol. 5, no. 1, 2011.
• S.Lee,W.D.Neve, K. N. Plataniotis, and Y. M. Ro, “Map-based image tag recommendation using a visual folksonomy,” Pattern
Recognit. Lett., vol. 31, no. 9, pp. 976–982, 2010.
• H. M. Yu, W. H. Tsai, and H. M. Wang, “A query-by-singing system for retrieving karaoke music,” IEEE Trans. Multimedia, vol.
10, no. 8, pp. 1626–1637, 2008

35. I & S E |IIT KGP
Reference List
• Soudip Roy Chowdhury, Florian Daniel And Fabio Casati Recommendation And Weaving Of Reusable Mashup Model Patterns
For Assisted Development, ACM Transactions On Internet Technology, Vol. 14, No. 2-3, Article 21, Publication Date: October
2014.
• Al´Ipio M. Jorge, Adam Jatowt, Gae¨ L Dias, Ricardo Campos, Survey Of Temporal Information Retrieval And Related
Applications, ACM Computing Surveys, Vol. 47, No. 2, Article 15, Publication Date: July 2014.
• Baoxing Huai, Enhong Chen, Hengshu Zhu, Hui Xiong, Tengfei Bao, Qi Liu, Jilei Tian, Toward Personalized Context Recognition
For Mobile Users: A Semi-Supervised Bayesian HMM Approach, ACM Transactions On Knowledge Discovery From Data, Vol. 9,
No. 2, Article 10, Publication Date: September 2014.
• Changsheng Xu, Zhengyu Deng, Ming Yan, Jitao Sang, Twitter Is Faster: Personalized Time-Aware Video Recommendation From
Twitter To Youtube, ACM Trans. Multimedia Comput. Commun. Appl., Vol. 11, No. 2, Article 31, Publication Date: December
2014.
• Michalis Katsarakis, Georgios Fortetsanakis, Paulos Charonyktakis, Alexandros Kostopoulos, And Maria Papadopouli,On User-
Centric Tools For Qoe-Based Recommendaton And Real-Time Analysis Of Large-Scale Markets, IEEE Communications Magazine,
September 2014.
• Hong Yu And Mark O. Riedl, Personalized Interactive Narratives Via Sequential Recommendation Of Plot Points, IEEE
Transactions On Computational Intelligence And AI In Games, Vol. 6, No. 2, June 2014.
• Vikram Krishnamurthy, William Hoiles, Online Reputation And Polling Systems: Data Incest, Social Learning, And Revealed
Preferences, IEEE Transactions On Computational Social Systems, Vol. 1, No. 3, September 2014.
• Ioannis Katakis, Nicolas Tsapatsoulis, Fernando Mendez, Vasiliki Triga, And Constantinos Djouvas, Social Voting Advice
Applications -Definitions, Challenges, Datasets And Evaluation, IEEE Transactions On Cybernetics, Vol. 44, No. 7, July 2014.
• Daqiang Zhang, Ching-Hsien Hsu, Min Chen, Quan Chen, Naixue Xiong, And Jaime Lloret,Cold-Start Recommendation Using Bi-
Clustering And Fusion For Large-Scale Social Recommender Systems, Emerging Topics In Computing, Volume 2, No. 2, June
2014
• Feng Xia, Zhen Chen, Wei Wang, Jing Li, And Laurence T. Yang, Mvcwalker: Random Walk-Based Most Valuable Collaborators
Recommendation Exploiting Academic Factors, Emerging Topics In Computing, Volume 2, No. 3, September 2014.
• M. Hamada, “An Integrated Virtual Environment for Active and Collaborative e-Learning in Theory of Computation,” IEEE Trans .
Learning Technologies, vol. 1, no. 2, pp. 117-130, Apr.–June 2008.
• R. Kumar and C.P. Rose, “Architecture for Building Conversational Agents that Support Collaborative Learning,” IEEE Trans.
Learning Technologies, vol. 4, no. 1, pp. 21-34, Jan.–Mar. 2011.

36. I & S E |IIT KGP
Reference List
• Zhuming Bi, Li Da Xu, And Chengen Wang, Internet Of Things For Enterprise Systems Of Modern Manufacturing, IEEE
Transactions On Industrial Informatics, Vol. 10, No. 2, May 2014.
• Qi Liu, Enhong Chen, Hui Xiong, Yong Ge, Zhongmou Li, And Xiang Wu, A Cocktail Approach For Travel Package
Recommendation, IEEE Transactions On Knowledge And Data Engineering, Vol. 26, No. 2, February 2014.
• Magdalini Eirinaki, Suju Abraham, Neoklis Polyzotis, And Naushin Shaikh, Querie: Collaborative Database Exploration, IEEE
Transactions On Knowledge And Data Engineering, Vol. 26, No. 7, July 2014.
• Yi Cai, Ho-Fung Leung, Qing Li, Huaqing Min, Jie Tang, And Juanzi Li, Typicality-Based Collaborative Filtering Recommendation,
IEEE Transactions On Knowledge And Data Engineering, Vol. 26, No. 3, March 2014.
• Jehoshua Eliashberg, Sam K. Hui, And Z. John Zhang, Assessing Box Office Performance Using Movie Scripts: A Kernel-Based
Approach, IEEE Transactions On Knowledge And Data Engineering, Vol. 26, No. 11, November 2014.
• Charu C. Aggarwal, Yuchen Zhao, And Philip S. Yu, On The Use Of Side Information For Mining Text Data, IEEE Transactions On
Knowledge And Data Engineering, Vol. 26, No. 6, June 2014.
• Han-Chieh Chao, Chin-Feng Lai, Shih-Yeh Chen, And Yueh-Min Huang, A M-Learning Content Recommendation Service By
Exploiting Mobile Social Interaction, IEEE Transactions On Learning Technologies, Vol. 7, No. 3, July-September 2014.
• Xiaokang Zhou, Jian Chen, Member, Bo Wu, And Qun Jin, Discovery Of Action Patterns And User Correlations In Task-Oriented
Processes For Goal-Driven Learning Recommendation IEEE Transactions On Learning Technologies, Vol. 7, No. 3, July-
September 2014.
• Zhibo Wang, Jilong Liao, Qing Cao, Hairong Qi, Zhi Wang, Friendbook: A Semantic-Based Friend Recommendation System For
Social Networks, IEEE Transactions On Mobile Computing, Vol. 14, No. 3, March 2015.
• Xin Chen, Mihaela Vorvoreanu, And Krishna Madhavan, Mining Social Media Data For Understanding Students’ Learning
Experiences, IEEE Transactions On Learning Technologies, Vol. 7, No. 3, July-September 2014.
• K. Farrahi and D. Gatica-Perez, “Probabilistic mining of sociogeographic routines from mobile phone data,” IEEE J. Select. Topics
Signal Process., vol. 4, no. 4, pp. 746–755, Aug. 2010.
• J. Bobadilla, F. Ortega, A. Hernando, A. Gutierrez, Recommender systems survey, Knowledge-Based Systems 46 (2013) 109–
132.
• K. Verbert, N. Manouselis, X. Ochoa, M. Wolpers, H. Drachsler, I. Bosnic, and E. Duval, “Context-aware recommender systems
for learning: A survey and future challenges,” IEEE Trans. Learn. Techn., vol. 5, no. 4, pp. 318–335, Oct.-Dec. 2012.
• J. Luo, F. Dong, J. Cao, and A. Song, “A context-aware personalized resource recommendation for pervasive learning,” Cluster
Comput., vol. 13, pp. 213–239, Jun. 2010.

37. I & S E |IIT KGP
Reference List
• Jing Liu, Zechao Li, Jinhui Tang, Yu Jiang, And Hanqing Lu, Personalized Geo-Specific Tag Recommendation For Photos On Social
Websites, IEEE Transactions On Multimedia, Vol. 16, No. 3, April 2014.
• Marie Katsurai, Takahiro Ogawa , And Miki Haseyama, A Cross-Modal Approach For Extracting Semantic Relationships Between
Concepts Using Tagged Images, IEEE Transactions On Multimedia, Vol. 16, No. 4, June 2014.
• Ning-Han Liu, Effective Results Ranking For Mobile Query By Singing/Humming Using A Hybrid Recommendation Mechanism,
IEEE Transactions On Multimedia, Vol. 16, No. 5, August 2014.
• Kibeom Lee And Kyogu Lee, Using Dynamically Promoted Experts For Music Recommendation, IEEE Transactions On
Multimedia, Vol. 16, No. 5, August 2014.
• Shunmei Meng, Wanchun Dou, Xuyun Zhang, And Jinjun Chen, Kasr:A keyword-Aware service Recommendation Method On
Map Reduce For Big Data Applications, IEEE Transactions On Parallel And Distributed Systems, Vol. 25, No. 12, December 2014.
• Xi Chen, Zibin Zheng, Qi Yu, And Michael R. Lyu, Web Service Recommendation Via Exploiting Location And Qos Information,
IEEE Transactions On Parallel And Distributed Systems, Vol. 25, No. 7, July 2014.
• Weili Han, Zheran Fang, Laurence Tianruo Yang, Gang Pan, Member, And Zhaohui Wu, Collaborative Policy Administration, IEEE
Transactions On Parallel And Distributed Systems, Vol. 25, No. 2, February 2014.
• Osman Khalid, Muhammad Usman Shahid Khan, Samee U. Khan, And Albert Y. Zomaya, Omnisuggest: A Ubiquitous Cloud-
Based Context-Aware Recommendation System For Mobile Social Networks, IEEE Transactions On Services Computing, Vol. 7,
No. 3, July-September 2014.
• Cem Tekin, Simpson Zhang, And Mihaela Van Der Schaar , Distributed Online Learning In Social Recommender Systems, IEEE
Journal Of Selected Topics In Signal Processing, Vol. 8, No. 4, August 2014.
• Ko-Jen Hsiao, Alex Kulesza, And Alfred O. Hero, Social Collaborative Retrieval, IEEE Journal Of Selected Topics In Signal
Processing, Vol. 8, No. 4, August 2014.
• Hong, J.-Y., Suh, E.-H., & Kim, S.-J. (2009) Context-aware systems: A literature review and classification. Expert Systems with
Applications, 36, 8509–8522.
• G. Louloudis, B. Gatos, I. Pratikakis, and C. Halatsis, “Text line detection in handwritten documents,” Patt. Recognit., vol. 41, no.
12, pp. 3758–3772, Dec. 2008.
• G. Adomavicius and A. Tuzhilin, “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and
Possible Extensions,” IEEE Trans. Knowledge and Data Eng., vol. 17, no. 6, pp. 734-749, June 2005.
• J. Vassileva, “Toward social learning environments,” IEEE Trans. Learn. Techn., vol. 1, no. 4, pp. 199–214, Oct.-Dec. 2008.

38. I & S E |IIT KGP
Reference List
• Bo Wu, Xiaokang Zhou, Qun Jin, , Fuhua Lin, And Henry Leung, Analyzing Social Roles Based On A Hierarchical Model And Data
Mining For Collective Decision-Making Support, IEEE Systems Journal, 2015.
• Dnyanesh G. Rajpathak, And Satnam Singh, An Ontology-Based Text Mining Method To Develop D-Matrix From Unstructured
Text, IEEE Transactions On Systems, Man, And Cybernetics: Systems, Vol. 44, No. 7, July 2014.
• Jewoong Ryu, Hyung, And Nam Ik Cho, Independent Text-Line Extraction Algorithm For Handwritten Documents, IEEE Signal
Processing Letters, Vol. 21, No. 9, September 2014.
• Dianshuang Wu, Guangquan Zhang, And Jie Lu, A Fuzzy Preference Tree-Based Recommender System For Personalized
Business-To-Business E-Services, IEEE Transactions On Fuzzy Systems, Vol. 23, No. 1, February 2015.
• L. C. Wang, X. Y. Zeng, , L. Koehl, And Y. Chen, Intelligent Fashion Recommender System: Fuzzy Logic In Personalized Garment
Design, IEEE Transactions On Human-Machine Systems, Vol. 45, No. 1, February 2015.
• Thijs Veugen, Robbert De Haan, Ronald Cramer, And Frank Muller, A Framework For Secure Computations With Two Non-
Colluding Servers And Multiple Clients, Applied To Recommendations, IEEE Transactions On Information Forensics And Security,
Vol. 10, No. 3, March 2015.
• Anna Gatzioura And Miquel Sànchez-Marrè, A Case-Based Recommendation Approach For Market Basket Data, IEEE Intelligent
Systems, 2015.
• Soumya Ray, Sung S. Kim, James G. Morris, The Central Role Of Engagement In Online Communities, Information Systems
Research ©2014 Informs.
• Kartik Hosanagar, Daniel Fleder, Dokyun Lee, Andreas Buja , Will The Global Village Fracture Into Tribes? Recommender
Systems And Their Effects On Consumer Fragmentation, Management Science, Vol. 60, No. 4, April 2014, Pp. 805–823.
• Wei Xie, Barry L. Nelson, Russell R. Barton, A Bayesian Framework For Quantifying Uncertainty In Stochastic Simulation,
Operations Research, , Vol. 62, No. 6, November–December 2014, Pp. 1439–1452
• Abhijeet Ghoshal, Sumit Sarkar, Association Rules For Recommendations With Multiple Items, Informs Journal On Computing,
Vol. 26, No. 3, Summer 2014, Pp. 433–448.
• Kang Zha, Xi Wang, Mo Yu, Bo Gao, User Recommendations In Reciprocal And Bipartite Social Networks—An Online Dating
Case Study, Social Intelligence And Technology, March/April 2014.
• J. Herlocker, J. Konstan, L. Terveen, and J. Riedl, “Evaluating Collaborative Filtering Recommender Systems,” ACM Trans.
Information Systems, vol. 22, no. 1, pp. 5-53, 2004.
• Q. Liu, E. Chen, H. Xiong, C. Ding, and J. Chen, “Enhancing Collaborative Filtering by User Interests Expansion via Personalized
Ranking,” IEEE Trans. Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 42, no. 1, pp. 218-233, Feb. 2012.

39. I & S E |IIT KGP
Reference List
• Hai Jiang, Xin Qi, He Sun, Choice-Based Recommender Systems: A Unified Approach To Achieving Relevancy And Diversity,
Operations Research, Vol. 62, No. 5, September–October 2014, Pp. 973–993.
• Shankar Prawesh, Balaji Padmanabhan , The “Most Popular News” Recommender: Count Amplification And Manipulation
Resistance, Information Systems Research, Vol. 25, No. 3, September 2014, Pp. 569–589.
• Ashish Goel, Pankaj Gupta, John Sirois, Dong Wang, Aneesh Sharma, Siva Gurumurthy, The Who-To-Follow System At Twitter:
Strategy, Algorithms, And Revenue Impact, Interfaces, Vol. 45, No. 1, January–February 2015, Pp. 98–107.
• Zan Huang, Daniel Dajun Zeng, Why Does Collaborative Filtering Work? Transaction-Based Recommendation Model Validation
And Selection By Analyzing Bipartite Random Graphs, Informs Journal On Computing, Vol. 23, No. 1, Winter 2011, Pp. 138–
152.
• Oded Netzer, Ronen Feldman, Jacob Goldenberg, Moshe Fresko, Mine Your Own Business: Market-Structure Surveillance
Through Text Mining, Marketing Science, Vol. 31, No. 3, May–June 2012, pp. 521–543.
• Anindya Ghose, Panagiotis G. Ipeirotis, Beibei Li, Designing Ranking Systems for Hotels on Travel Search Engines by Mining
User-Generated and Crowd sourced Content, Marketing Science, Vol. 31, No. 3, May–June 2012, pp. 493–520.
• Hema Yoganarasimhan, Cloak or Flaunt? The Fashion Dilemma, Vol. 31, No. 1, January–February 2012, pp. 74–95.
• Aibek Makazhanov, Davood Rafiei, Muhammad Waqar, Predicting Political Preference Of Twitter Users, Soc. Netw. Anal. Min.
(2014).
• Amin Javari , Joobin Gharibshah , Mahdi Jalili, Recommender Systems Based On Collaborative Filtering And Resource
Allocation, Soc. Netw. Anal. Min. (2014).
• Rui Zeng , Quan Z. Sheng , Lina Yao, A Simulation Method For Social Networks, Soc. Netw. Anal. Min. (2015).
• Jalel Akaichi, A Medical Social Network For Physicians’ Annotations Posting An Summarization, Soc. Netw. Anal. Min. (2014).
• Nam P. Nguyen , Md Abdul Alim, Thang N. Dinh, My T. Thai, A Method To Detec Communities With Stability In Social Networks,
Soc. Netw. Anal. Min. (2014).
• X. Yu, Y. Liu, J.X. Huang, and A. An, “Mining Online Reviews for Predicting Sales Performance: A Case Study in the Movie
Domain,” IEEE Trans. Knowledge and Data Eng., vol. 24, no. 4, pp. 720-734, Apr. 2012.
• D. Bandyopadhyay and J. Sen, “Internet of things: Applications and challenges in technology and standardization,” Wireless
Pers. Commun., vol. 58, pp. 49–59, 2011.
• E. Welbourne, L. Battle, G. Cole, K. Gould, K. Rector, S. Raymer et al., “Building the Internet of things using RFID: The RFID
ecosystem experience,” IEEE Internet Comput., vol. 13, no. 3, pp. 48–55, May/Jun. 2009.

40. I & S E |IIT KGP
Reference List
• Malhar Anjaria, Ram Mohana Reddy Guddeti, A Novel Sentiment Analysis Of Social Networks Using Supervised Learning, Soc.
Netw. Anal. Min. (2014).
• Ahmed Abdeen Hamed, Xindong Wu, Alan Rubin, A Twitter Recruitment Intelligent System: Association Rule Mining For
Smoking Cessation, Soc. Netw. Anal. Min. (2014).
• Yifan Hao, Huiping Cao, Chuan Hu, Kabi Bhattarai, Satyajayant Misra, K-Anonymity For Social Networks Containing Rich
Structural And Textual Information, Soc. Netw. Anal. Min. (2014).
• Erick Stattner, Martine Collard, Matching Of Communities And Frequent Conceptual Links :A Comparative Analysis Of Two
Network Clustering Approaches, , Soc. Netw. Anal. Min. (2014).
• Tianqing Zhu, Gang Li, Lei Pan, Yongli Ren, Wanlei Zhou, Privacy Preserving Collaborative Filtering For KNN Attack Resisting, Soc.
Netw. Anal. Min. (2014).
• Md. Nafiz Hamid, Md. Abu Naser, Md. Kamrul Hasan, Hasan Mahmud, A Cohesion-Based Friend-Recommendation System, ,
Soc. Netw. Anal. Min. (2014).
• Pete Burnap, Matthew L. Williams, Luke Sloan, Omer Rana, William Housley, Adam Edwards, Vincent Knight, Rob Procter, Alex
Voss, Tweeting The Terror: Modelling The Social Media Reaction To The Woolwich Terrorist Attack, Soc. Netw. Anal. Min.
(2014).
• Svetlana Kim & Yongik Yoon, Recommendation System For Sharing Economy Based On Multidimensional Trust Model, Springer,
Multimed Tools Appl 2014.
• Toon De Pessemier, Simon Dooms, Luc Martens, Comparison Of Group Recommendation Algorithms, Springer, Multimed Tools
Appl 2014.
• Bo Wu, Xiaokang Zhou & Qun Jin, Participatory Information Search And Recommendation Based On Social Roles And Networks,
Springer, Multimed Tools Appl 2014.
• Mehrbakhsh Nilashi, Othman Bin Ibrahim,Norafida Ithnin, Rozana Zakaria, A Multi-Criteria Recommendation System Using
Dimensionality Reduction And Neuro-Fuzzy Techniques Springer, Soft Comput 2014.
• Jui-Hung Chang, Chin-Feng Lai, Ming-Shi Wang, A Fair Scheduler Using Cloud Computing For Digital TV Program
Recommendation System, Telecommun Syst, 2014.
• Giuliana Carullo, Aniello Castiglione, Alfredo De Santis, Francesco Palmieri, A Triadic Closure And Homophily-Based
Recommendation System For Online Social Networks, Springer, World Wide Web, 2015.
• H. Tong, C. Faloutsos, and J.-Y. Pan, ``Random walk with restart: Fast solutions and applications,'' Knowl. Inf. Syst., vol. 14, no. 3,
pp. 327_346, 2008.

41. I & S E |IIT KGP
Reference List
• Chin-Hui Lai ,Applying Knowledge Flow Mining To Group Recommendation Methods For Task-Based Groups, Journal Of The
Association For Information Science And Technology, 66(3):545–563, 2015.
• Surendra Sarnikar, Zhu Zhang, J. Leon Zhao, Query-Performance Prediction For Effective Query Routing In Domain-Specific
Repositories, Journal Of The Association For Information Science And Technology, 65(8):1597–1614, 2014.
• Michal Sela, Talia Lavie, Ohad Inbar, Ilit Oppenheim, Joachim Meyer ,Personalizing News Content: An Experimental Study,
Journal Of The Association For Information Science And Technology, 66(1):1–12, 2015.
• Esther Meng-Yoke Tan, Dion Hoe-Lian Goh, ,A Study Of Social Interaction During Mobile Information Seeking, Journal Of The
Association For Information Science And Technology, 2015.
• Ricardo Terra, Marco Tulio Valente, Krzysztof Czarnecki And Roberto S. Bigonha, A Recommendation System For Repairing
Violations Detected By Static Architecture Conformance Checking, Softw. Pract. Exper. 2015.
• Zhikui Chen, Ruochuan Ling, Chung-Ming Huang And Xu Zhu, A Scheme Of Access Service Recommendation For The Social
Internet Of Things, International Journal Of Communication Systems(2015).
• Joobin Gharibshah , Mahdi Jalili, Connectedness Of Users–Items Networks And Recommender, Applied Mathematics And
Computation 243 (2014) 578–584.
• Li-Chen Cheng, Hua-An Wang , A Fuzzy Recommender System Based On The Integration Of Subjective Preferences And
Objective Information, Applied Soft Computing 18 (2014) 290–301
• Xiwang Yang , Yang Guo , Yong Liu , Harald Steck, A Survey Of Collaborative Filtering Based Social Recommender Systems,
Computer Communications 41 (2014) 1–10.
• Felix Mata-Rivera , Miguel Torres-Ruiz , Giovanni Guzmán , Marco Moreno-Ibarra, Rolando Quintero ,A Collaborative Learning
Approach For Geographic Information Retrieval Based On Social Networks, Computers In Human Behavior (2014).
• Ernesto D’avanzo , Giovanni Pilato, Mining Social Network Users Opinions’ To Aid Buyers’ Shopping Decisions, Computers In
Human Behavior (2014).
• C. C. Aggarwal, S. C. Gates, and P. S. Yu, “On using partial supervision for text categorization,” IEEE Trans. Knowl. Data Eng., vol.
16, no. 2, pp. 245–255, Feb. 2004.
• M. Alduan, F. Alvarez, J. Menendez, and O. Baez, “Recommender System for Sport Videos Based on User Audiovisual
Consumption,” IEEE Trans. Multimedia, vol. 14, no. 6, pp. 1546-1557, Dec. 2012.
• S. Perugini, M. A. Gonçalves, and E. A. Fox, ``Recommender systems research: A connection-centric survey,'' J. Intell. Inf. Syst.,
vol. 23, no. 2, pp. 107_143, 2004.

42. I & S E |IIT KGP
Reference List
• Rafael Pereira, He´ Lio Lopes , Karin Breitman, Vicente Mundimb, Wandenberg Peixoto, Cloud Based Real-Time Collaborative
Filtering For Item–Item Recommendations Computers In Industry 65 (2014) 279–290.
• Tomer Geva, Jacob Zahavi, Empirical Evaluation Of An Automated Intraday Stock Recommendation System Incorporating Both
Market Data And Textual News, Decision Support Systems 57 (2014) 212–223.
• Upkar Varshney, Mobile Health: Four Emerging Themes Of Research Decision Support Systems 66 (2014) 20–35.
• Josef Bauer, Alexandros Nanopoulos, Recommender Systems Based On Quantitative Implicit Customer Feedback, Decision
Support Systems 68 (2014) 77–88.
• Zohreh Dehghani Champiri , Seyed Reza Shahamiri , Siti Salwah Binti Salim, A Systematic Review Of Scholar Context-Aware
Recommender Systems, Expert Systems With Applications 42 (2015) 1743–1758
• Le Hoang Son, HU-FCF: A Hybrid User-Based Fuzzy Collaborative Filtering Method Recommender Systems,Expert Systems With
Applications 41 (2014) 6861–6870.
• Trong Hai Duong , Ngoc Thanh Nguyen , Hai Bang Truong , Van Huan Nguyen, A Collaborative Algorithm For Semantic Video
Annotation Using A Consensus-Based Social Network Analysis, Expert Systems With Applications 42 (2015) 246–258.
• Hyunwoo Kim, Hyoung-Joo Kim, A Framework For Tag-Aware Recommender Systems, Expert Systems With Applications 41
(2014) 4000–4009.
• Yogesh Gupta, Ashish Saini , A.K. Saxena, A New Fuzzy Logic Based Ranking Function For Efficient Information Retrieval System,
Expert Systems With Applications 42 (2015) 1223–1234.
• Bahram Amini, Roliana Ibrahim, Mohd Shahizan Othman, Mohammad Ali Nematbakhsh, A Reference Ontology For Profiling
Scholar’s Background Knowledge In Recommender Systems, Expert Systems With Applications 42 (2015) 913–928.
• Cristian E. Briguez, Maximiliano C.D. Budán, Cristhian A.D. Deagustini , Ana G. Maguitman,
• Marcela Capobianco , Guillermo R. Simari, Argument-Based Mixed Recommenders And Their Application To Movie Suggestion,
Expert Systems With Applications 41 (2014) 6467–6482.
• Bahram Amini, Roliana Ibrahim, Mohd Shahizan Othman, Ali Selamat, Capturing Scholar’s Knowledge From Heterogeneous
Resources For Profiling In Recommender Systems,Expert Systems With Applications 41 (2014) 7945–7957.
• H. J. Ahn, ``A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem,'' Inf. Sci., vol.
178, no. 1, pp. 37_51, 2008.
• J. Herlocker, J. A. Konstan, and J. Riedl, ``An empirical analysis of design choices in neighborhood-based collaborative filtering
algorithms,'' Inf. Retr., vol. 5, no. 4, pp. 287_310, 2002.

43. I & S E |IIT KGP
Reference List
• Kibeom Lee, Kyogu Lee, Escaping Your Comfort Zone: A Graph-Based Recommender System For Finding Novel
Recommendations Among Relevant Items, Expert Systems With Applications (2014).
• Ramón Hermoso , Roberto Centeno, Maria Fasli, From Blurry Numbers To Clear Preferences: A Mechanism To Extract
Reputation In Social Networks, Expert Systems With Applications 41 (2014) 2269–2285
• Nguyen Tho Thong, Le Hoang Son, HIFCF: An Effective Hybrid Model Between Picture Fuzzy Clustering And Intuitionistic Fuzzy
Recommender Systems For Medical Diagnosis, Expert Systems With Applications 42 (2015)A 3682–3701.
• Meng-Lun Wu, Chia-Hui Chang, Rui-Zhe Liu, Integrating Content-Based Filtering With Collaborative Filtering Using Co-Clustering
With Augmented Matrices, Expert Systems With Applications 41 (2014) 2754–2761.
• Joan Borràs , Antonio Moreno, Aida Valls, Intelligent Tourism Recommender Systems: A Survey, Expert Systems With
Applications 41 (2014) 7370–7389.
• Mustansar Ali Ghazanfar , Adam Prügel-Bennett, Leveraging Clustering Approaches To Solve The Gray-Sheep Users Problem In
Recommender Systems, Expert Systems With Applications 41 (2014) 3261–3275.
• Luis Omar Colombo-Mendoza , Rafael Valencia-Garcia , Alejandro Rodriguez-Gonzalez, Giner Alor-Hernandez, Jose Javier
Samper-Zapater, Recommetz: A Context-Aware Knowledge-Based Mobile Recommender System For Movie Show times Expert
Systems With Applications 42 (2015) 1202–1222.
• Dongsheng Li , Chao Chena, Qin Lv , Li Shanga, Yingying Zhaoa, Tun Luc, Ning Gu, An Algorithm For Efficient Privacy-Preserving
Item-Based Collaborative Filtering ,Future Generation Computer Systems, 2014.
• Tianqing Zhua, Yongli Renb, Wanlei Zhoub, Jia Rongb, Ping Xiong, An Effective Privacy Preserving Algorithm For Neighborhood-
Based Collaborative Filtering, Future Generation Computer Systems 36 (2014) 142–155.
• Shuhong Chena, Guojun Wanga , Weijia Jia, Cluster-Group Based Trusted Computing For Mobile Social Networks Using Implicit
Social Behavioral Graph, Future Generation Computer Systems (2014).
• Michał Woz´Niak , Manuel Grana , Emilio Corchado, A Survey Of Multiple Classifier Systems As Hybrid Systems, Information
Fusion 16 (2014) 3–17.
• O. Cornelis, M. Lesaffre, D. Moelants, and M. Leman, “Access to ethnic music: Advances and perspectives in content-based
music information retrieval,” Signal Process., vol. 90, no. 4, pp. 1008–1031, Apr. 2010.
• D. Li, Q. Lv, X. Xie, L. Shang, H. Xia, T. Lu, and N. Gu, “Interest based real-time content recommendation in online social
communities,” Knowl.-Based Syst., vol. 28, pp. 1–12, Apr. 2012.
• G. Linden, B. Smith, and J. York, ``Amazon.com recommendations: Item to- item collaborative filtering,'' IEEE Internet Comput.,
vol. 7, no. 1, pp. 76_80, Jan./Feb. 2003.

44. I & S E |IIT KGP
Reference List
• Yung-Ming Li , Lien-Fa Lin, Yu-Hui Lin, A Recommender Mechanism For Social Knowledge Navigation In An Online Encyclopedia,
Information Processing And Management 50 (2014) 634–652.
• Jianshan Sun , Gang Wanga, Xusen Cheng , Yelin Fu, Mining Affective Text To Improve Social Media Item Recommendation,
Information Processing And Management (2014).
• Juan Vera-Del-Campo, Josep Pegueroles, Juan Hernández-Serrano, Miguel Soriano, DocCloud: A Document Recommender
System On Cloud Computing With Plausible Deniability, Information Sciences 258 (2014) 387–402.
• Punam Bedi, Pooja Vashisth, Empowering Recommender Systems Using Trust And Argumentation, Information Sciences 279
(2014) 569–586.
• Sergio Cleger, J.M. Fernández-Luna, Juan F. Huete, Learning From Explanations In Recommender Systems, Information Sciences
287 (2014) 90–108.
• Chen Lin , Runquan Xie , Xinjun Guan , Lei Li , Tao Li, Personalized News Recommendation Via Implicit Social Experts,
Information Sciences 254 (2014) 1–18.
• Venkateswara Rao Kagita, Arun K. Pujari, Vineet Padmanabhan, Virtual User Approach For Group Recommender Systems Using
Precedence Relations, Information Sciences 294 (2015) 15–30.
• Fatih Gedikli , Dietmar Jannach , Mouzhi Ge, How Should I Explain? A Comparison Of Different Explanation Types For
Recommender Systems, Int. J. Human-Computer Studies 72 (2014) 367–382.
• Ferry Hendrikx, Kris Bubendorfer , Ryan Chard, Reputation Systems: A Survey And Taxonomy, J. Parallel Distrib. Comput. 75
(2015) 184–197.
• Fran Casinoa, Josep Domingo-Ferrerb, Constantinos Patsakisc, Domènec Puigb, Agusti Solanas, A K-Anonymous Approach To
Privacy Preserving Collaborative Filtering, Journal Of Computer And System Sciences, 2014.
• Xun Zhou, Jing He, Guangyan Huang, Yanchun Zhang, SVD-Based Incremental Approaches For Recommender Systems, Journal
Of Computer And System Sciences 81 (2015) 717–733.
• Nouman Azam, Jing Tao Yao, Game-Theoretic Rough Sets For Recommender Systems, Knowledge-Based Systems 72 (2014) 96–
107.
• Le Hoang Son , Nguyen Tho Thong, Intuitionistic Fuzzy Recommender Systems: An Effective Tool For Medical Diagnosis,
Knowledge-Based Systems 74 (2015)B 133–150.
• Z.-S. Chen, J.-S. R. Jang, and C.-H. Lee, “A kernel framework for content-based artist recommendation system in music,” IEEE
Trans. Multimedia, vol. 13, no. 6, pp. 1371–1380, Dec. 2011.

45. I & S E |IIT KGP
Reference List
• Haifeng Liu, Zheng Hu, Ahmad Mian, Hui Tian, Xuzhen Zhu, A New User Similarity Model To Improve The Accuracy Of
Collaborative Filtering, Knowledge-Based Systems 56 (2014) 156–166.
• Wei-Po Lee, Che Kaoli, Jhih-Yuan Huang, A Smart TV System With Body-Gesture Control, Tag-Based Rating And Context-Aware
Recommendation, Knowledge-Based Systems 56 (2014) 167–178.
• Raciel Yera Toledo, Yailé Caballero Mota, Luis Martínez, Correcting Noisy Ratings In Collaborative Recommender Systems,
Knowledge-Based Systems 76 (2015) 96–108.
• Lara Quijano-Sánchez, Belén Díaz-Agudo, Juan A. Recio-García, Development Of A Group Recommender Application In A Social
Network, Knowledge-Based Systems 71 (2014) 72–85.
• Dongsheng Li, Qin Lv , Li Shang , Ning Gu, Item-Based Top-N Recommendation Resilient To Aggregated Information Revelation,
Knowledge-Based Systems 67 (2014) 290–304.
• Guibing Guo, Jie Zhang, Neil Yorke-Smith, Leveraging Multiviews Of Trust And Similarity To Enhance Clustering-Based
Recommender Systems, Knowledge-Based Systems 74 (2015) 14–27.
• Cheng-Lung Huang , Po-Han Yeh, Cheng-Wei Lin, Den-Cing Wu, Utilizing User Tag-Based Interests In Recommender Systems For
Social Resource Sharing Websites, Knowledge-Based Systems 56 (2014) 86–96.
• Jing Zhang, Qinke Peng, Shiquan Sun, Che Liu Collaborative Filtering Recommendation Algorithm Based On User Preference
Derived From Item Domain Features, Physica A 396 (2014) 66–76.
• Zi-Ke Zhanga, Lu Yua, Kuan Fang, Zhi-Qiang You, Chuang Liu, Hao Liu, Xiao-Yong Yane, Website-Oriented Recommendation
Based On Heat Spreading And Tag-Aware Collaborative Filtering, Physica A 399 (2014) 82–88.
• Chhavi Rana , Sanjay Kumar Jain, An Evolutionary Clustering Algorithm Based On Temporal Features For Dynamic
Recommender Systems, Swarm And Evolutionary Computation 14 (2014 )21–30.
• Jaewon Choi, Hong Joo Lee, Farhana Sajjad , Habin Lee, The Influence Of National Culture On The Attitude Towards Mobile
Recommender Systems, Technological Forecasting & Social Change 86 (2014) 65–79.
• Ruzhi Xua, Shuaiqiang Wanga, Xuwei Zheng, Yinong Chen, Distributed Collaborative Filtering With Singular Ratings For Large
Scale Recommendation, The Journal Of Systems And Software 95 (2014) 231–241.
• M. Gaeta, F. Orciuoli, S. Paolozzi, and S. Salerno, “Ontology extraction for knowledge reuse: The e-learning perspective,” IEEE
Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 41, no. 4, pp. 798–809, Jul. 2011.
• G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and
possible extensions,” IEEE Trans. Knowl. Data Eng., vol. 17, no. 6, pp. 734–749, Jun. 2005.

46. I & S E |IIT KGP
Reference List
• Zhoubao Suna, Lixin Hana, Wenliang Huanga, Xueting Wanga, Xiaoqin Zenga, Min Wanga, Hong Yan, Recommender Systems
Based On Social Networks, The Journal Of Systems And Software 99 (2015) 109–119.
• Arman Khadjeh Nassirtoussi, Saeed Aghabozorgi , Teh Ying Waha, David Chek Ling Ngo, Text Mining For Market Prediction: A
Systematic Review, Expert Systems With Applications 41 (2014) 7653–7670.
• Hussein Hashimi, Alaaeldin Hafez, Hassan Mathkour, Selection Criteria For Text Mining Approaches, Computers In Human
Behavior (2015).
• Bingrui Geng, Lingling Li, Licheng Jiao, Maoguo Gong, Qing Cai, Yue Wu, NNIA-Rs: A Multi-Objective Optimization Based
Recommender System, Physica A 424 (2015) 383–397.
• A.Krzywicki , W.Wobcke, Y.S.Kim, X.Cai, M.Bain, A.Mahidadia, P.Compton, Collaborative Filtering For People-To-People
Recommendation In Online Dating: Data Analysis And User Trial, Int. J. Human-Computer studies 76 (2015) 50–66.
• Mehrbakhsh Nilashi , Dietmar Jannach, Othman Bin Ibrahim, Norafida Ithnin, Clustering- And Regression-Based Multi-Criteria
Collaborative Filtering With Incremental Updates, Information Sciences 293 (2015) 235–250.
• Venkateswara Rao Kagita, Arun K. Pujari, Vineet Padmanabhan, Virtual User Approach For Group Recommender Systems Using
Precedence Relations, Information Sciences 294 (2015) 15–30.
• Belén Barragáns-Martínez, Enrique Costa-Montenegro, Jonathan Juncal-Martínez, Developing A Recommender System In A
Consumer Electronic Device, Expert Systems With Applications 42 (2015) 4216–4228.
• Sarik Ghazarian, Mohammad Ali Nematbakhsh, Enhancing Memory-Based Collaborative Filtering For Group Recommender
Systems, Expert Systems With Applications 42 (2015) 3801–3812.
• Youngki Park , Sungchan Park , Woosung Jung, Sang-Goo Lee, Reversed Cf: A Fast Collaborative Filtering Algorithm Using A K-
Nearest Neighbor Graph, Expert Systems With Applications 42 (2015) 4022–4028.
• Filipe Braida, Carlos E. Mello , Marden B. Pasinato, Geraldo Zimbrão, Transforming Collaborative Filtering Into Supervised
Learning, Expert Systems With Applications (2015).
• G. Adomavicius and Y. Kwon, “New Recommendation Techniques for Multicriteria Rating Systems,” IEEE Intelligent Systems, vol.
22, no. 3, pp. 48-55, May/June 2007.
• B. Shao, D. Wang, T. Li, and M. Ogihara, “Music recommendation based on acoustic features and user access patterns,” Trans.
Audio, Speech, Lang. Process., vol. 17, no. 8, pp. 1602–1611, Nov. 2009.
• C. Dellarocas, “The digitization of word of mouth: Promise and challenges of online feedback mechanisms,” Manage. Sci., vol.
49, no. 10, pp. 1407–1424, 2003.

47. I & S E |IIT KGP
Reference List
• Brendan Neville, Maria Fasli, Jeremy Pitt, Utilising Social Recommendation For Decision-Making In Distributed Multi-Agent
Systems, Expert Systems With Applications 42 (2015) 2884–2906.
• Hui Fang, Guibing Guo, Jie Zhang, Multi-Faceted Trust And Distrust Prediction For Recommender Systems, Decision Support
Systems 71 (2015) 37–47.
• Edward Rolando Núñez-Valdez, Juan Manuel Cueva Lovelle , Guillermo Infante Hernández, Aquilino Juan Fuente , José Emilio
Labra-Gayo, Creating Recommendations On Electronic Books: A Collaborative Learning Implicit Approach, Computers In Human
Behavior (2015).
• Ben Horsburgh, Susan Craw, Stewart Massie, Learning Pseudo-Tags To Augment Sparse Tagging In Hybrid Music Recommender
Systems Artificial Intelligence 219 (2015) 25–39.
• Jiang, Qi, and Sun: Choice-Based Recommender Systems Operations Research 62(5), pp. 973–993, ©2014 INFORMS 993.
• Adomavicius G, Tuzhilin A (2007) Validation sequence optimization: A theoretical approach.INFORMS J. Comput.19 (2): 185–
200.
• Ghoshal and Sarkar: Association Rules for Recommendations with Multiple Items 448 INFORMS Journal on Computing 26(3),
pp. 433–448, ©2014.
• Fleder D, Hosanagar K (2009) Blockbuster culture’s next rise or fall: The impact of recommender systems on sales diversity.
Management Sci. 55(5):697–712.
• Herlocker JL, Konstan JA, Terveen LG, Riedl J (2004) Evaluating collaborative filtering recommender systems. ACM Trans.
Inform. Systems 22(1):5–53.
• Adomavicius and Kwon: Maximizing Aggregate Recommendation Diversity INFORMS Journal on Computing 26(2), pp. 351–369,
©2014 INFORMS 369.
• Adomavicius G, Kwon Y (2012) Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans.
Knowledge Data Engg. 24(5):896–911.
• Lee JS, Zhu D (2012) Shilling attack detection—A new approach for a trustworthy recommender system. INFORMS J. Comput.
24:117–131
• Lerman K (2007) Social information processing in news aggregation. IEEE Internet Comput. 11:16–28.
• Van Roy B, Yan X (2010) Manipulation robustness of collaborative filtering. Management Sci. 56:1911.
• Josang, R. Ismail, and C. Boyd, “A survey of trust and reputation systems for online service provision,” Decis. Support Syst., vol.
43, no. 2, pp. 618–644, 2007.

48. I & S E |IIT KGP
Reference List
• Deshpande, M., G. Karypis. 2004. Item-based top-N recommendation algorithms. ACM Trans. Inform. Systems 22(1) 143–177.
• Huang, Z., D. Zeng, H. Chen. 2007. Analyzing consumer-product graphs: Empirical findings and applications in recommender
systems. Management Sci. 53(7) 1146–1164.
• Jin, R., L. Si, C. Zhai. 2006. A study of mixture models for collaborative filtering. Inform. Retrieval 9(3) 357–382.
• Konstan, J. A. 2004. Introduction to recommender systems: Algorithms and evaluation. ACM Trans. Inform. Systems 22(1) 1–4.
• Felfernig, A., G. Friedrich, L. Schmidt-Thieme. 2007. Guest editors’ introduction: Recommender systems. IEEE Intelligent Systems
22(3) 18–21.
• P. du Boucher-Ryan, D. Bridge, Collaborative recommending using formal con-cept analysis, Knowledge-Based Systems 19 (5)
(2006) 309–315.
• Y. Cao, Y. Li, An intelligent fuzzy-based recommendation system for consumer electronic products, Expert Systems with
Applications 33 (1) (2007)
• G. Castellano, A.M. Fanelli, M.A. Torsello, Newer: a system for neuro-fuzzy web recommendation, Applied Soft Computing 11 (1)
(2011) 793–806
• Y.L. Chen, L.C. Cheng, C.N. Chung, A group recommendation system with-consideration of interactions among group members,
Expert Systems with Applications 34 (3) (2008) 2082–2090.
• L. Greg, S. Brent, Y. Jeremy, Amazon.com recommendations: item-to-item collaborative filtering, IEEE Internet Computing 7 (1)
(2003) 76–80
• D.R. Liu, C.H. Lai, W.J. Lee, A hybrid of sequential rules and collaborative filtering for product recommendation, Information
Sciences 179 (2) (2009) 3505–3519
• R.R. Yager, Fuzzy logic methods in recommender systems, Fuzzy Sets and Systems 136 (2) (2003) 133–149.
• J.M. Yang, K.F. Li, Recommendation based on rational inferences in collaborative filtering, Knowledge-Based Systems 22 (1)
(2009) 105–114.
• Francesco Ricci and Quang Nhat Nguyen, Acquiring and Revising Preferences in a Critique-Based Mobile Recommender System,
IEEE Intelligent Systems, 1541-1672/07, 2007.
• Bamshad Mobasher, Robin Burke, Runa Bhaumik, and Chad Williams. 2007. Toward trustworthy recommender systems: An
analysis of attack models and algorithm robustness. ACM Transactions on Internet Technology 7, 4, (October 2007).
• V. Krishnamurthy and H. V. Poor, “A tutorial on interactive sensing in social networks,” IEEE Trans. Comput. Social Syst., vol. 1, no.
1, pp. 3– 21, Mar. 2014.

49. I & S E |IIT KGP
Reference List
• Zenebe, A.F. Norcio, Representation, similarity measures and aggregation methods using fuzzy sets for content-based
recommender systems, Fuzzy Sets and Systems 160 (1) (2009) 76–94.
• Z. Zhang, O. Nasraoui, Mining search engine query logs for social filtering-based query recommendation, Applied Soft
Computing 8 (4) (2008) 1326–1334.
• X. Yang, Y. Guo Y. Liu, Bayesian-inference based recommendation in online social networks, in: IEEE Transactions on Parallel and
Distributed Systems (TPDS), 2013, pp. 642–651.
• F. Liu, H.J. Lee, Use of social network information to enhance collaborative filtering performance, Expert System Applications 37
(7) (2010) 4772–4778.
• Barranquero Tolosa, J., Labra Gayo, J. E., MartínezPrieto, A. B., Méndez Núñez, S., & Ordóñez de Pablos, P. Interactive web
environment for collaborative and extensible diagram based learning. Computers in Human Behavior, 6(2), 210–217 (2010).
• Carpineto, C., Romano, G. (2012). A survey of automatic query expansion in information retrieval. ACM Computing Surveys
(CSUR), 44(1).
• Y. Koren, Factor in the neighbors: scalable and accurate collaborative filtering, ACM Transactions on Knowledge Discovery from
Data (TKDD) 4 (2010).
• G.P.C. Fung, J.X. Yu, H. Luz, The predicting power of textual information on financial markets, IEEE Intelligent Informatics Bulletin
5 (2005) 1–10.
• D.L. Abraham, I. Junglas, B. Ives, Mobile technology at the frontlines of patient care: understanding fit and human drives in
utilization decisions and performance, Decision Support Systems 46 (3) (February 2009) 634–647.
• J.P. Shim, M.Warkentin, J. Courtney, D.J. Power, R. Sharda, C. Carlsson, Past, present, and future of decision support technology,
Decision Support Systems 33 (2) (2002).
• Adomavicius, G., Sankaranarayanan, R., Sen, S., & Tuzhilin, A. (2005).Incorporating contextual information in recommender
systems using a multidimensional approach. ACM Transactions on Information Systems (TOIS), 23, 103–145.
• Herlocker, J. L., & Konstan, J. A. (2001).Content-independent task-focused recommendation. IEEE Internet Computing, 5, 40–47.
• Silvana Aciar, Debbie Zhang, Simeon Simoff, and John Debenham. 2007. Informed recommender : Basing recommendations on
consumer product reviews. IEEE Intelligent Systems 22, 3 (May 2007), 39–47.
• Neil Hurley and Mi Zhang. 2011. Novelty and diversity in top-N recommendation—analysis and evaluation. ACM Transactions on
Internet Technology 10, 4 (March 2011).
•

50. I & S E |IIT KGP
Reference List
• Carrer-Neto, W., Hernández-Alcaraz, M. L., Valencia-García, R., &García-Sánchez, F. (2012). Social knowledge-based
recommender system. Application to the movies domain. Expert Systems with Applications, 39(12), 10990–11000.
• Christidis, K., & Mentzas, G. (2013). A topic-based recommender system for electronic market place platforms. Expert Systems
with Applications, 40(11), 4370–4379.
• Fang, B., Liao, S., Xu, K., Cheng, H., Zhu, C., & Chen, H. (2012). A novel mobile recommender system for indoor shopping. Expert
Systems with Applications, 39(15), 11992–12000.
• Kaklauskas, A. et al. (2013). Recommender system to analyze student’s academic performance. Expert Systems with
Applications, 40(15), 6150–6165.
• Park, D. H., Kim, H. K., Choi, I. Y., & Kim, J. K. (2012). A literature review and classification of recommender systems research.
Expert Systems with Applications, 39(11), 10059–10072.
• Porcel, C., López-Herrera, A. G., & Herrera-Viedma, E. (2009). A recommender system for research resources based on fuzzy
linguistic modelling. Expert Systems with Applications, 36(3), 5173–5183.
• Shih, D. H., Yen, D. C., Lin, H. C., & Shih, M. H. (2011). An implementation and evaluation of recommender systems for traveling
abroad. Expert Systems with Applications, 38(12), 15344–15355
• Zheng, N., & Li, Q. (2011). A recommender system based on tag and time information for social tagging systems. Expert Systems
with Applications, 38(4), 4575–4587.
• Symeonidis, P., Nanopoulos, A., & Manolopoulos, Y. (2010). A unified framework for providing recommendations in social
tagging systems based on ternary semantic analysis. IEEE Transactions on Knowledge and Data Engineering, 22, 179–192.
• Lee, C.-H. (2012). Mining spatio-temporal information on microblogging streams using a density-based online clustering
method. Expert Systems with Applications, 39(10), 9623–9641
• Albanese, M., d’Acierno, A., Moscato, V., Persia, F., &Picariello, A. (2013). A multimedia recommender system. ACM Transactions
on Internet Technology (TOIT), 13(1), 3
• Batet, M., Moreno, A., Sanchez, D., Isern, D., &Valls, A. (2012).Turist@: Agent based personalised recommendation of tourist
activities. Expert Systems with Applications, 39(8), 7319–7329.
• F. Yin and C.-L. Liu, “Handwritten Chinese text line segmentation by clustering with distance metric learning,” Patt. Recognit., vol.
42, no. 12, pp. 3146–3157, Dec. 2009.
• Liu, D.-R., & Lai, C.-H. (2011). Mining group-based knowledge flows for sharing task knowledge. Decision Support Systems, 50(2),
370–386.

51. I & S E |IIT KGP
Reference List
• Liu, L., Xu, J., Liao, S. S., & Chen, H. (2014). A real-time personalized route recommendation system for self-drive tourists based
on vehicle to vehicle communication. Expert Systems with Applications, 41(7), 3409–3417.
• Yang, W.-S., Cheng, H.-C., &Dia, J.-B.(2008). A location-aware recommender systemfor mobile shopping environments. Expert
Systems with Applications, 34(1),437–445.
• Lee, S., Park, S., Kahng, M., & Lee, S.-G. (2013). Pathrank: Ranking nodes on a heterogeneous graph for flexible hybrid
recommender systems. Expert Systems with Applications.
• Al-Shamri, M. Y. H. (2014). Power coefficient as a similarity measure for memory- based collaborative recommender systems.
Expert Systems with Applications, 41, 5680–5688.
• Anand, D., & Bharadwaj, K. K. (2011). Utilizing various sparsity measures for enhancing accuracy of collaborative recommender
systems based on local and global similarities. Expert Systems with Applications, 38, 5101–5109.
• Choi, S.-M., Ko, S.-K., & Han, Y.-S. (2012). A movie recommendation algorithm based on genre correlations. Expert Systems with
Applications, 0957-4174, 39(9), 8079–8085.
• Liu, J., Wu, C., & Liu, W. (2013). Bayesian probabilistic matrix factorization with social relations and item contents for
recommendation. Decision Support Systems, 0167-9236, 55(3), 838–850.
• Liu, J., Xiong, Y., Wu, C., Yao, Z., & Liu, W. (2014). Learning conditional preference networks from inconsistent examples. IEEE
Transactions on Knowledge and Data Engineering.
• Liang, T., Yang, Y., Chen, D., & Ku, Y. (2008). A semantic-expansion approach to personalized knowledge recommendation.
Elsevier, Decision Support Systems, 45(3), 401–412.
• Budzik, J., Hammond, K. J., & Birnbaum, L. (2001).Information access in context. Knowledge-Based Systems, 14(1), 37–53.
• Pu, P.,Chen,L.,2007.Trust-inspiring explanation interfaces for recommender systems. Knowl.-Based Syst.20(August(6)),542–556.
• L. Liu, M. Munro, Systematic analysis of centralized online reputation systems, Decis. Support Syst. 52 (2) (2012) 438–449.
• W.L. Hung, M.S. Yang, On similarity measures between intuitionistic fuzzy sets, Int. J. Intell. Syst. 23 (3) (2008) 364–383.
• Ning H, Sha H. Technology classification, industry, and education for future Internet of Things. International Journal of
Communication Systems 2012; 25(9): 1230–1241
• Nitti M, Girau R, Atzori L. Trustworthiness management in the Social Internet of Things. IEEE Transactions on Knowledge and
Data Engineering 2013; 26(5): 1253–1266.
• Javari A, Jalili M (2014)B Cluster-based collaborative filtering for sign prediction in social networks with positive and negative
links. ACM Trans Intell Syst Technol (TIST) 5:24.

52. I & S E |IIT KGP
Reference List
• F. Feng, C. Li, B. Davvaz, M.I. Ali, Soft sets combined with fuzzy sets and rough sets: a tentative approach, Soft Comput. 14 (9)
(2010) 899–911
• E. Brynjolfsson, Y.J. Hu, M.D. Smith, Consumer surplus in the digital economy: estimating the value of increased product variety
at online booksellers, Manage. Sci. 49 (11) (2003) 1580–1596.
• J. Bobadilla, F. Ortega, A. Hernando, A collaborative filtering similarity measure based on singularities, Inform. Process. Manage.
48 (2012) 204–217.
• K. Goldberg ,T. Roeder, G. Gupta, C. Perkins, Eigen taste: a constant time collaborative filtering algorithm, Inform. Retrieval 4
(2001) 133–151.
• F. Francois, P. Alain, R. Jean-Michel, S. Marco, Random-walk computation of similarities between nodes of a graph with
application to collaborative recommendation, IEEE Trans. Knowl. Data Eng. 19 (2007) 355–369.
• J. Bobadilla, F. Serradilla, A. Hernando, Movielens, Collaborative filtering adapted to recommender systems of e-learning,
Knowl.-Based Syst. 22 (2009) 261–265.
• F. Ricci, Travel recommender systems, IEEE Intell. Syst. 17 (2002) 55–57.
• H. Yang, C. Wang, Recommender system for software project planning: one application of revised CBR algorithm, Expert Syst.
Appl. 36 (2009) 8938–8945.
• H. Fujita, J. Hakura, M. Kurematsu, Intelligent human interface based on mental cloning-based software, Knowl.-Based Syst. 22
(3) (2009) 216–234.
• D. Turnbull, L. Barrington, D. Torres, G. Lanckriet, Semantic annotation and retrieval of music and sound effects, IEEE Trans.
Audio Speech Lang. Process. 16(2) (2008) 467–476.
• Claypool, M., Brown, D., Le, P., & Waseda, M. (2001).Inferring user interest. IEEE Internet Computing, 5(6), 32–39.
• Lee, H. J., & Park, S. J. (2007). MONERS: A news recommender for the mobile web. Expert Systems with Applications, 32(1),
143–150.
• K. Verbert, N. Manouselis, X. Ochoa, M. Wolpers, H. Drachsler, I. Bosnic, and E. Duval. Context-aware recommender systems for
learning: a survey and future challenges. IEEE Transactions on Learning Technologies, 5(4), 2012.
• Burguillo, J. C., & Peleteiro, A. (2010). Exploiting social tagging in a web 2.0 recommender system. IEEE Internet Computing, 14,
23–30.
• Xia F, Yang LT, Wang L, Vinel A. Internet of Things. International Journal of Communication Systems 2012; 25(9): 1101–1102 .
• Zan Huang HC, Zeng Daniel (2004) Applying associative retrieval techniques to alleviate the sparsity problem in collaborative
filtering. ACM Trans Inf Syst 22:116–142.

53. I & S E |IIT KGP
Reference List
• Barragáns-Martínez, A.B., Costa-Montenegro, E.,, Burguillo, J.C., et al., 2010. A hybrid content-based and item-based
collaborative filtering approach to recommend TV programs enhanced with singular value decomposition. Inf. Sci. 180 (22),
4290–4311.
• Aghdam, M. H., Ghasem-Aghaee, N., & Basiri, M. E. (2009). Text feature selection using ant colony optimization. Expert Systems
with Applications, 36, 6843–6853.
• Cambria, E., Schuller, B., Yunqing, X., & Havasi, C. (2013). New avenues in opinion mining and sentiment analysis. IEEE Intelligent
Systems, 28, 15–21.
• Desmet, B., & Hoste, V. (2013). Emotion detection in suicide notes. Expert Systems with Applications, 40, 6351–6358.
• Duric, A., & Song, F. (2012). Feature selection for sentiment analysis based on content and syntax models. Decision Support
Systems, 53, 704–711.
• Liu, Y., Loh, H. T., & Sun, A. (2009). Imbalanced text classification: A term weighting approach. Expert Systems with Applications,
36, 690–701.
• Maks, I., & Vossen, P. (2012). A lexicon model for deep sentiment analysis and opinion mining applications. Decision Support
Systems, 53, 680–688.
• Mostafa, M. M. (2013). More than words: Social networks’ text mining for consumer brand sentiments. Expert Systems with
Applications, 40, 4241–4251.
• Uysal, A. K., & Gunal, S. (2012). A novel probabilistic feature selection method for text classification. Knowledge-Based Systems,
36, 226–235.
• Fields B, Jacobson K, Rhodes C, d’Inverno M, Sandler M et al (2011) Analysis and exploitation of musician social networks for
recommendation and discovery. IEEE Trans Multimed 13:674–786
• Fouss F, Pirotte A, Renders J-M, Saerens M (2007) Analysis and exploitation of musician social networks for recommendation
and discovery. IEEE Trans Multimed 13:674–786
• Hofmann T (2004) Latent semantic models for collaborative filtering. ACM Trans Inf Syst (TOIS) 22:89–115.
• Zhan J, Hsieh C-L, Wang I-C, Sheng Hsu T, Liau C-J, Wang D-W (2010) Privacy-preserving collaborative recommender systems part
C: applications and reviews. IEEE Trans Syst Man Cybern 40(4):472–476.
• Moraes R, Valiati JF (2013) Document-level sentiment classification: an empirical comparison between SVM and ANN. Elseveir
Trans Expert Syst Appl 40:621–633.
• Brandes U, Delling D, Gaertler M, Gorke R, Hoefer M, Nikoloski Z, Wagner D (2008) On modularity clustering. IEEE Trans Knowl
Data Eng 20(2):172–188.