Skip to main content
SpringerLink
Log in

Recommender systems based on collaborative filtering and resource allocation

  • Original Article
  • Published:
Social Network Analysis and Mining Aims and scope Submit manuscript

Abstract

Recommendation systems are important part of electronic commerce, where appropriate items are recommended to potential users. The most common algorithms used for constructing recommender systems in commercial applications are collaborative filtering methods and their variants, which is mainly due to their simple implementation. In these methods, structural features of bipartite network of users and items are used and potential items are recommended to the users based on a similarity measure that shows how similar the behavior of the users is. Indeed, the performance of the memory-based CF algorithms heavily depends on the quality of similarities obtained among users/items. As the obtained similarities are more reliable, better performance for the recommender systems is expected. In this paper, we propose three models to extract reliability of similarities estimated in classic recommenders. We incorporate the obtained reliabilities to improve performance of the recommender systems. In the proposed algorithms for reliability extraction, a number of elements are taken into account including the structure of the user-item bipartite network, the individual profile of the users, i.e., how many items they have rated, and that of the items, i.e., how many users have rated them. Among the proposed methods, the method based on resource allocation provides the highest performance as compared to others. Our numerical results on two benchmark datasets (Movielens and Netflix) shows that employing resource allocation in classical recommenders significantly improves their performance. These results are of great importance since including resource allocation in the systems does not increase their computational complexity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  • Abdessalem T, Cautis B, Souhli A (2009) Trust management in social networks. Télécom ParisTech, LTCI, UMR CNRS 514146

  • Adamic EA LA (2003) Friends and neighbors on the web. Soc Netw 25:211–230

    Article  Google Scholar 

  • Adamopoulos P, Tuzhilin A (2013) Probabilistic Neighborhood Selection in Collaborative Filtering Systems

  • Adomavicius G, Tuzhilin A (2005a) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Audio Electroacoust Knowl Data Eng 17:734–749

    Article  Google Scholar 

  • Adomavicius G, Tuzhilin A (2005b) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. Knowl Data Eng, IEEE Trans 17(6):734–749

    Article  Google Scholar 

  • Ahn HJ (2008) A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Inf Sci 178:37–51

    Article  Google Scholar 

  • Altingovde IS, Subakan ÖN, Ulusoy Ö (2013) Cluster searching strategies for collaborative recommendation systems. Inf Process Manage 49:688–697

    Article  Google Scholar 

  • Anand D, Bharadwaj KK (2011) Utilizing various sparsity measures for enhancing accuracy of collaborative recommender systems based on local and global similarities. Expert Syst Appl 38:5101–5109

    Article  Google Scholar 

  • Badrul Sarwar GK, Joseph Konstan, and John Riedl (2001) ItemBased Collaborative Filtering Recommendation Algorithms. Hong Kong

  • Basu C, Hirsh H, Cohen W (1998) Recommendation as classification: Using social and content-based information in recommendation. National Conference on Artificial Intelligence

  • Bellogín A, Castells P, Cantador I (2013) Improving memory-based collaborative filtering by neighbour selection based on user preference overlap. LE CENTRE DE HAUTES ETUDES INTERNATIONALES D’INFORMATIQUE DOCUMENTAIRE pp 145–148

  • Ben Schafer DF J, Herlocker Jon, Sen Shilad (2007) Collaborative filtenng recommender systems. LNCS 4321:291–324

    Google Scholar 

  • Bobadilla J, Ortega F, Hernando A, Bernal J (2012) A collaborative filtering approach to mitigate the new user cold start problem. Knowl-Based Syst 26:225–238

    Article  Google Scholar 

  • John S. Breese DH, and Carl Kadie (1998). Empirical Analysis of Predictive Algorithms for Collaborative Filtering pp 43–52

  • Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-Adapt Interact 12:331–370

    Article  MATH  Google Scholar 

  • Cattuto C, Loreto V, Pietronero L (2007) Semiotic dynamics and collaborative tagging. Proc Natl Acad Sci USA 104:1461–1464

    Article  Google Scholar 

  • Cover TM, Thomas JA (2012) Elements of information theory, Wiley

  • David Liben-Nowell JK (2007) The link-prediction problem for social networks. Am Soc Info Sci Technol 58:1019–1031

    Article  Google Scholar 

  • Deshpande M, Karypis G (2004) Item-based top-N recommendation algorithms. ACM Trans Inf Syst 22(1):143–177

    Article  Google Scholar 

  • Cacheda F, Carneiro V, Fernandez D, Formoso V (2011) Comparison of collaborative filtering algorithms: limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Trans Web 5:2

  • 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

    Article  Google Scholar 

  • Fischer G (2001) User modeling in human–computer interaction. User Model User-Adap Inter 11:65–86

    Article  MATH  Google Scholar 

  • Fouss F, Pirotte A, Renders J-M, Saerens M (2007) Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. Knowl Data Eng, IEEE Trans 19:355–369

    Article  Google Scholar 

  • Garton L, Haythornthwaite C, Wellman B (1997) Studying online social networks. J Comp Mediat Commun 3:1–32

  • Gediminas Adomavicius AT (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17:734–749

    Article  Google Scholar 

  • Ghazanfar MA, Prügel-Bennett A (2014) Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems. Expert Syst Appl 41:3261–3275

    Article  Google Scholar 

  • Gunawardana A, Meek C (2009) A unified approach to building hybrid recommender systems. ACM pp 117–124

  • Herlocker JL, Konstan JA, Borchers A, Riedl J (1999) An algorithmic framework for performing collaborative filtering. Berkeley, California, pp 230–237

    Google Scholar 

  • Hoel PG (1954) Introduction to mathematical statistics. Introduction to mathematical statistics

  • Hofmann T (2004) Latent semantic models for collaborative filtering. ACM Trans Inf Syst (TOIS) 22:89–115

    Article  Google Scholar 

  • James Bennett CE, Bing Liu, Padhraic Smyth and Domonkos Tikk (2007) KDD Cup and workshop 2007. ACM SIGKDD Explorations Newsletter 9

  • Javari A, Jalili M (2013) Accurate and novel recommendations: An algorithm based on popularity forecasting. ACM Trans Intell Syst Technol (to appear)

  • Javari A, Jalili M (2014) Cluster-based collaborative filtering for sign prediction in social networks with positive and negative links. ACM Trans Intell Syst Technol (TIST) 5:24

    Google Scholar 

  • Jin R, Chai JY,Si L (2004) An automatic weighting scheme for collaborative filtering Sheffield, UK pp 337–344

  • Kleinberg JM (1999) Authoritative sources in a hyperlinked environment. J ACM 46:604–632

    Article  MathSciNet  MATH  Google Scholar 

  • Kohavi R(1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. San Francisco, CA pp 1137–1143

  • Konstan J, Miller B, Maltz D, Herlocker J, Gordon L et al (1997) GroupLens: applying collaborative filtering in usenet news. Commun ACM 40:77–87

    Article  Google Scholar 

  • Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42:30–37

    Article  Google Scholar 

  • Liu Jian-Guo WB-HAGQ (2009) Improved Collaborative Filtering Algorithm via Information Transformation. Int J Mod Phy C 20:285–293

    Article  Google Scholar 

  • Lü L, Zhou T (2011) Link prediction in complex networks: A survey. Phys A 390:1150–1170

    Article  Google Scholar 

  • Manos Papagelis DP (2005) Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents. Eng Appl Artif Intell 18:781–789

    Article  Google Scholar 

  • Maslov S, Zhang Y-C (2001) Extracting hidden information from knowledge networks. Phys Rev Lett 87:248701

    Article  Google Scholar 

  • Ni J, Zhang Y-L, Hu Z-L, Song W-J, Hou L et al (2014) Ceiling effect of online user interests for the movies. Physica A 402:134–140

    Article  Google Scholar 

  • Popescul A, Ungar LH, Pennock DM, Lawrence S (2001) Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. Seventeenth Conference on Uncertainty in Artificial Intelligence

  • Resnick P, Iacovou N, Suchak M, Bergstorm P, Riedl J (1994) GroupLens: an open architecture for collaborative filtering of netnews. ACM Press, Chapel Hill, pp 175–186

    Google Scholar 

  • Resnick P, Varian HR (1997) Recommender systems. Commun ACM 40:56–58

    Article  Google Scholar 

  • Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. 10th international conference on World Wide Web: ACM

  • Shang M-S, Lu L, Zhang Y-C, Zhou T (2010) Empirical analysis of web-based user-object bipartite networks. Europhys Lett 92:48006

    Article  Google Scholar 

  • Symeonidis P, Ntempos D, Manolopoulos Y (2014) Recommender systems for location-based social networks: Springer

  • Tao Zhou LL, Zhang Yi-Cheng (2009) Predicting missing links via local information. Eur Phy 71:623–630

    Article  MATH  Google Scholar 

  • Yang X, Guo Y, Liu Y (2011) Bayesian-inference based recommendation in online social networks. IEEE, Infocom

    Google Scholar 

  • 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

    Article  Google Scholar 

  • Zhou T, Ren J, Medo M, Zhang Y-C (2007) Bipartite network projection and personal recommendation. Phys Rev E 76:046115

    Article  Google Scholar 

  • Zhou T, Kuscsik Z, Liu J-G, Medo M, Wakeling JR et al (2010) Solving the apparent diversity-accuracy dilemma of recommender systems. Proc Natl Acad Sci USA 107:4511–4515

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Amin Javari, Joobin Gharibshah & Mahdi Jalili

  2. School of Electrical and Computer Engineering, RMIT University, Melbourne, Australia

    Mahdi Jalili

Authors
  1. Amin Javari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joobin Gharibshah
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mahdi Jalili
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mahdi Jalili.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Javari, A., Gharibshah, J. & Jalili, M. Recommender systems based on collaborative filtering and resource allocation. Soc. Netw. Anal. Min. 4, 234 (2014). https://doi.org/10.1007/s13278-014-0234-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13278-014-0234-0

Keywords

Search

Navigation