Improved Scalable Recommender System


  • S. Ishtiaq University of Engineering and Technology, Taxila, Pakistan
  • N. Majeed University of Engineering and Technology, Taxila, Pakistan
  • M. Maqsood University of Engineering and Technology, Taxila, Pakistan
  • A. Javed University of Engineering and Technology, Taxila, Pakistan


Recommender systems are known for their ability to recommend items which are new to the user by having some synchronization with user’s personal interest. The importance of recommender systems leads to the creation of new approaches that can produce accurate results. As data became large it results in scalability issues. In this work, we have suggested a scalable technique using different methods that work in a sequential manner. A novel centroid selection for clustering based recommender system is proposed. SVD and user representatives are used to handle scalability issues. Experiments on proposed approach with standard datasets showed great improvement in scalability and slight better accuracy

Author Biographies

S. Ishtiaq, University of Engineering and Technology, Taxila, Pakistan

Department of Software Engineering

N. Majeed, University of Engineering and Technology, Taxila, Pakistan

Department of Software Engineering

M. Maqsood, University of Engineering and Technology, Taxila, Pakistan

Department of Software Engineering

A. Javed, University of Engineering and Technology, Taxila, Pakistan

Department of Software Engineering


Burke and Robin. "Hybrid web recommender systems, "The

adaptive web.Springer Berlin Heidelberg, pp. 377-408, 2007.

K. Lang, “Newsweeder: Learning to filter netnews”, Proc. of the

th Int. Conf. on Machine Learning, vol. 12, pp. 331-339, ICML

Vozalis, Emmanouil and K.G. Margaritis, "Analysis of

recommender systems algorithms", Proc. of the 6th Hellenic

European Conf. on Computer Mathematics and its Applications

(HERCMA-2003), Athens, Greece. pp. 732-745, 2003.

M. Pazzani and D. Billsus of Part, “Content-based

recommendation systems”, The Adaptive Web, P. Brusilovsky,

Berlin: Springer, vol. 4321, pp. 325–341, 2007,.

M. Rehman and T. Ahmad. "Optimized k-Nearest Neighbor Search

with Range Query", The Nucleus, vol. 52, no. 2, pp. 45-49, 2015.

Zou, Haitao, et al. "TrustRank: a cold-start tolerant recommender

system", Enterprise Information Systems, vol. 9.2, pp.117-138,

Resnick, Paul and Hal R. Varian. "Recommender systems",

Communications of the ACM, vol. 40.3, pp. 56-58, 1997.

B. Mobasher, “Recommender systems”, KunstlicheIntelligenz,

Special Issue on Web Mining, vol. 3, pp. 41–43, 2007.

Azar and Yossi et al., "Spectral analysis of data", Proc. of the 33rd

Annual ACM Symposium on Theory of Computing, pp. 619-626,

Goldberg and David et al., Using Collaborative Filtering to Weave

an Information Tapestry", Communications of the ACM,

vol. 35, no. 12, pp. 61-70, 1992.

Sarwar, “Sparsity, scalability, and distribution in recommender

systems”, Ph.D. thesis, University of Minnesota, 2001.

Burke and Robin, "Hybrid recommender systems: Survey and

experiments", User Modeling and User-adapted Interaction,

vol. 12, no. 4, pp. 331-370, 2002.

Spiegel, Stephan, JérômeKunegis and Fang Li, "Hydra: a hybrid

recommender system [cross-linked rating and content

information]", Proc. of the 1st ACM Int. Workshop on Complex

Networks Meet Information &Knowledge Management. ACM,

pp. 75-80, 2009.

M.E. Wall, A. Rechtsteiner and L.M. Rocha, "Singular value

decomposition and principal component analysis", A Practical

Approach to Microarray Data Analysis, Daniel P. Berrar: Springer

US , vol. 2003, pp. 91-109, 2003.

Ahn and Hyung Jun, "A new similarity measure for collaborative

filtering to alleviate the new user cold-starting problem",

Information Sciences, vol. 178, no. 1, pp. 37-51, 2008.

J.L. Herlocker et al., "An algorithmic framework for performing

collaborative filtering", Proc. of the 22nd Annual Int. ACM SIGIR

Conference on Research and Development in Information

Retrieval, ACM, pp. 230-237, 1999.

Ssiliou and Charalampos, et al., "A recommender system

framework combining neural networks &collaborative filtering",

Proc. of the 5th WSEAS Int. Conf. on Instrumentation,

Measurement, Circuits and Systems, World Scientific and

Engineering Academy and Society (WSEAS), pp. 285-290, 2006.

Lee, Meehee, P. Choi and Y. Woo. "A hybrid recommender

system combining collaborative filtering with neural network", Int.

Conf. on Adaptive Hypermedia and Adaptive Web-Based Systems,

pp. 531-534, Springer Berlin Heidelberg, 2002.

Gunawardana, Asela and C. Meek. "A unified approach to building

hybrid recommender systems", Proc. of the 3rd ACM Conf. on

Recommender Systems, pp. 117-124. ACM, 2009.

Jahrer, Michael, A. Töscher, and R. Legenstein, "Combining

predictions for accurate recommender systems", Proc. of the 16th

ACM SIGKDD Int.Conf. on Knowledge Discovery and Data

Mining, pp. 693-702, 2010.

Melville, Prem, R.J. Mooney and R. Nagarajan, "Content-boosted

collaborative filtering for improved recommendations", Aaai/iaai,

pp. 187-192. 2002.

Li, Qing and B.M. Kim, "An approach for combining contentbased

and collaborative filters", Proc. of the 6th Int. Workshop on

Information Retrieval with Asian Languages, vol. 11, pp. 17-24,

Shardan, Upendra and P. Maes, "Social information filtering:

algorithms for automating “word of mouth”, Proc. of the SIGCHI

Conf. on Human Factors in Computing Systems, pp. 210-217,

ACM Press/Addison-Wesley Publishing Co., 1995.

Adomavicius, Gediminas and A. Tuzhilin, "Toward the next

generation of recommender systems: A survey of the state-of-theart

and possible extensions", IEEE transactions on knowledge and

data engineering 17, vol. no. 6, pp. 734-749, 2005.

Linden, Greg, B. Smith and J. York, "

recommendations: Item-to-item collaborative filtering", Internet

Computing, IEEE, vol. 7, no. 1, pp. 76-80, 2003.

Balabanović, Marko and Y. Shoham, "Fab: content-based,

collaborative recommendation", Communications of the ACM,

vol.40, no. 3, pp. 66-72, 1997.

Pazzani and J. Michael, "A framework for collaborative, contentbased

and demographic filtering", Artificial Intelligence Review,

vol. 13, pp. 393-408, 1999.

Garcia, Ruth and Xavier Amatriain. "Weighted content based

methods for recommending connections in online social

networks", Workshop on Recommender Systems and the Social

Web, pp. 68-71, 2010.

Billsus, Daniel, M.J. Pazzani and J. Chen, "A learning agent for

wireless news access", Proc. of the 5th Int. Conf. on Intelligent

user Interfaces, pp. 33-36. ACM, 2000.

Baeza-Yates, Ricardo, and Berthier Ribeiro-Neto. Modern

information retrieval, vol. 463. New York: ACM press, 1999.

Harvard Joachims and Thorsten, "Text categorization with support

vector machines: Learning with many relevant features", European

Conf. on Machine Learning, pp. 137-142, 1998.

J.L. Herlocker, J.A. Konstan, J.T. Riedl and L.G. Terveen,

“Evaluating collaborative filtering recommender systems”,

ACM Transactions on Information Systems, vol. 22, no. 1,

pp. 5–53,2004.

Su, Xiaoyuan and T.M. Khoshgoftaar, "A survey of collaborative

filtering techniques", Advances in Artificial Intelligence, vol.

, pp. 4-23, 2009.

Bobadilla, Jesús, Francisco Serradilla and Jesus Bernal. "A new

collaborative filtering metric that improves the behavior of

recommender systems." Knowledge-Based Systems, vol. 23, no. 6,

pp. 520-528, 2010.

Ortega and Fernando et al., "Improving collaborative filteringbased

recommender systems results using Pareto dominance",

Information Sciences, vol. 239, pp 50-61, 2013.

Luo, Xin, Y. Xia and Q. Zhu, "Incremental collaborative filtering

recommender based on regularized matrix factorization",

Knowledge-Based Systems, vol.27, pp. 271-280, 2012.

Park, Seung-Taek and W. Chu, "Pairwise preference regression for

cold-start recommendation", Proc. of the 3rd ACM Conf. on

Recommender Systems, pp. 21-28. ACM, 2009.

Park, Yoon-Joo and A. Tuzhilin, "The long tail of recommender

systems and how to leverage it", Proc. of the 2008 ACM

Conference on Recommender Systems, pp. 11-18, 2008.

du Boucher-Ryan, Patrick, and D. Bridge", Collaborative

recommending using formal concept analysis", Knowledge-Based

Systems, vol. 19, no. 5, pp. 309-315, 2006.

Sarwar, M. Badrul, G. Karypis, J. Konstan and J. Riedl.

"Recommender systems for large-scale e-commerce: Scalable

neighborhood formation using clustering." In Proceedings of the

fifth international conference on computer and information

technology, vol. 1, pp. 128-134, 2002.

Xue, Gui-Rong, C. Lin, Q. Yang, W. Xi, H. J. Zeng, Y. Yu and

Z. Chen. "Scalable collaborative filtering using cluster-based

smoothing", Proc. of the 28th Annual Int. ACM SIGIR Conf. on

Research and Development in Information Retrieval, pp. 114-121.

A.M. Rashid, S.K. Lam, G. Karypis and J. Riedl, "ClustKNN:

A highly scalable hybrid model-& memory-based CF algorithm,

Proc. of WebKDD, 2006.

P.S Bradley and U. M. Fayyad, "Refining initial points for

K-means clustering", ICML, vol. 98, pp. 91-99, 1998.

Arthur, David and S. Vassilvitskii, "k-means++: The advantages of

careful seeding", Proc. of the 18th Anual ACM-SIAM Symp. on

Discrete Algorithms, pp. 1027-1035, 2007.

Shindler and Michael of Part, "Approximation algorithms for the

metric k-median problem", Efficient Approximation and Online

Algorithms, E. Bampis, Berlin: Springer, vol. 2006, pp. 292-320,

Jamali, Mohsen and M. Ester, "TrustWalker: A random walk

model for combining trust-based and item-based

recommendation", Proc. of the 15th ACM SIGKDD Int. Conf. on

Knowledge Discovery and Data Mining, pp. 397-406. 2009.

Zahra and Sobia et al., "Novel centroid selection approaches for

KMeans-clustering based recommender systems", Information

Sciences, vol. 320, pp. 156-189, 2015.

S. Deerwester, S.T. Dumais, G.W. Furnas, T.K. Landauer and

R. Harshman, Indexing by latent semantic analysis Journal of

the American Society for Information Science, vol. 41, no. 6,

pp. 391–407, 1990.

Billsus, Daniel and M.J. Pazzani, "Learning Collaborative

Information Filters", Icml, vol. 98, pp. 46-54. 1998.

B. Sarwar, G. Karypis, J. Konstan, and J. Riedl, “Application of

dimensionality reduction in recommender systems – A case study”.

Proc. of the ACM WebKDD Workshop, vol. 2, pp. 212-224, 2000.




How to Cite

S. Ishtiaq, N. Majeed, M. Maqsood, and A. Javed, “Improved Scalable Recommender System”, The Nucleus, vol. 53, no. 3, pp. 200–207, Sep. 2016.