Recommender Systems - A Review and Recent Research Trends

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:
I & S E |IIT KGP
- 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?
I & S E |IIT KGP
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?
I & S E |IIT KGP
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
I & S E |IIT KGP
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
I & S E |IIT KGP
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)
I & S E |IIT KGP

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
I & S E |IIT KGP
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
I & S E |IIT KGP
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
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