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Link prediction approach to recommender systems

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Abstract

The problem of recommender system is very popular with myriad available solutions. Recommender systems recommend items to users and help them in narrowing their search from huge amount of options available to the user. In this work, a novel approach for the recommendation problem is proposed by incorporating techniques from the link prediction problem in social networks. The proposed approach models the typical user-item information as a bipartite network, and predicts future links using link prediction measures, in which link prediction would actually mean recommending an item to a user. The standard recommender system methods suffer from the problems of sparsity and scalability. Since link prediction measures involve computations pertaining to local neighborhoods in the network, this approach would lead to a scalable solution to recommendation. In this work, we present top k links that are predicted by link prediction measures as recommendations to the users. Our work initially applies different existing link prediction measures to the recommendation problem by making suitable adaptations. The prime contribution of this work is to propose a recommendation framework routed from link prediction problem in social networks, that effectively utilizes probabilistic measures of link prediction and embed temporal data accessible on existing links. The proposed approach is evaluated on one movie-rating dataset of MovieLens, two product-rating datasets of Epinions & Amazon and one hotel-rating dataset of TripAdvisor. Results show that the link prediction measures based on temporal probabilistic information prove to be more effective in improving the quality of recommendation. Especially, Temporal cooccurrence probability measure improves the area under ROC curve (AUROC) by 10% for MovieLens, 23% for Epinions, 17% for TripAdvisor, 9% for Amazon over standard item-based collaborative filtering method. Similar improved performance is observed in terms of area under Precision-Recall curve (AUPR) as well as Normalized Rank-Score.

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References

  1. Chui M (2017) Artificial intelligence the next digital frontier? McKinsey Co Glob Inst 47:3–6

    Google Scholar 

  2. Schafer JB, Konstan JA, Riedl J (2001) E-commerce recommendation applications. Data Min Knowl Discov 5:115–153

    Article  MATH  Google Scholar 

  3. Bell RM, Koren Y (2007) Lessons from the netflix prize challenge. Acm Sigkdd Explor Newslett 9:75–79

    Article  Google Scholar 

  4. Liu J, Dolan P, Pedersen ER (2010) Personalized news recommendation based on click behavior, 31–40

  5. Calero Valdez A, Ziefle M, Verbert K, Felfernig A, Holzinger A (2016) Recommender systems for health informatics: state-of-the-art and future perspectives. Mach Learn Health Inf: State-of-the-Art and Future Challenges 391–414

  6. Pazzani MJ, Billsus D. (2007) The adaptive web (eds Brusilovsky, P., Kobsa, A. & Nejdl, W.) Ch. Content-based Recommendation Systems, 325–341 (Springer-Verlag, 2007)

  7. Mooney RJ, Roy L (2000) Content-based book recommending using learning for text categorization, 195–204

  8. Linden G, Smith B, York J. Amazon. com recommendations: Item-to-item collaborative filtering (2003) . IEEE Internet computing 7:76–80

  9. Anand R, Jeffrey David U (2011) Mining of massive datasets

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

    Article  Google Scholar 

  11. Sarwar B, Karypis G, Konstan J, Riedl J (2000) Analysis of recommendation algorithms for e-commerce, 158–167

  12. Shams B, Haratizadeh S (2017) Graph-based collaborative ranking. Expert Syst Appl 67:59–70

    Article  MATH  Google Scholar 

  13. Huang Z, Chung W, Chen H (2004) A graph model for e-commerce recommender systems. J Am Soc Inf Sci Technol 55:259–274

    Article  Google Scholar 

  14. Huang Z, Chung W, Chen H (2004) A graph model for e-commerce recommender systems. J Am Soc Inf Sci Technol 55:259–274

    Article  Google Scholar 

  15. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms, 285–295

  16. Melville P, Mooney RJ, Nagarajan R (2001) Content-boosted collaborative filtering 9:187–192

    Google Scholar 

  17. Adomavicius G, Mobasher B, Ricci F, Tuzhilin A (2011) Context-aware recommender systems. AI Mag 32:67–80

    Google Scholar 

  18. Kefalas P, Symeonidis P, Manolopoulos Y (2015) A graph-based taxonomy of recommendation algorithms and systems in lbsns. IEEE Trans Knowl Data Eng 28:604–622

    Article  Google Scholar 

  19. Ali Z, Qi G, Kefalas P, Abro WA, Ali B (2020) A graph-based taxonomy of citation recommendation models. Artif Intell Rev 1–44

  20. Li X, Chen H (2013) Recommendation as link prediction in bipartite graphs: a graph kernel-based machine learning approach. Decis Support Syst 54:880–890

    Article  Google Scholar 

  21. Zhang L, Zhao M, Zhao D (2020) Bipartite graph link prediction method with homogeneous nodes similarity for music recommendation. Multimedia Tools Appl 79:1–19

    Google Scholar 

  22. Liben-Nowell D, Kleinberg J (2007) The link-prediction problem for social networks. J Am Soc Inf Sci Technol 58:1019–1031

    Article  Google Scholar 

  23. Cremonesi P, Koren Y, Turrin R (2010) Performance of recommender algorithms on top-n recommendation tasks, 39–46

  24. Jaya Lakshmi T, Durga Bhavani S Link prediction in temporal heterogeneous networks, 83–98 (Springer, 2017)

  25. Lakshmi TJ, Bhavani SD. Link prediction measures in various types of information networks: a review, 1160–1167 (IEEE, 2018)

  26. Lichtenwalter RN, Lussier JT, Chawla NV (2010) New perspectives and methods in link prediction, 243–252

  27. Li J, Zhang L, Meng F, Li F (2014) Recommendation algorithm based on link prediction and domain knowledge in retail transactions. Procedia Comput Sci 31:875–881

    Article  Google Scholar 

  28. Davis DA, Lichtenwalter R, Chawla NV (2013) Supervised methods for multi-relational link prediction. Soc Netw Analy Min 3:127–141

    Article  Google Scholar 

  29. Munasinghe L, Ichise R Time aware index for link prediction in social networks., 342–353 (Springer, 2011)

  30. Choudhary P, Mishra N, Sharma S, Patel R (2013) Link score: a novel method for time aware link prediction in social network. ICDMW

  31. Munasinghe L (2013) Time-aware methods for link prediction in social networks. PhD Thesis, The Graduate University for Advanced Studies

  32. Wang C, Satuluri V, Parthasarathy S Local probabilistic models for link prediction, 322–331 (IEEE, 2007)

  33. Druzdzel MJ (2014) Some properties of joint probability distributions, 187 (Elsevier, 2014)

  34. Kashima H, Kato T, Yamanishi Y, Sugiyama M, Tsuda K (2009) Link propagation: a fast semi-supervised learning algorithm for link prediction, 1100–1111 SIAM, 2009

  35. Clauset A, Moore C, Newman ME (2008) Hierarchical structure and the prediction of missing links in networks. Nature 453:98

    Article  Google Scholar 

  36. Jaya Lakshmi T, Durga Bhavani S (2017) Temporal probabilistic measure for link prediction in collaborative networks. Appl Intell 47:83–95

    Article  Google Scholar 

  37. Calders T, Goethals B (2002) Mining all non-derivable frequent itemsets, Vol. 2, 74–85 (Springer, 2002)

  38. Lauritzen SL, Speigelhalter DJ (1988) Local computations with probabilities on graphical structures and their application to expert systems. J Royal Stat Soc Ser B (Methodological) 50:157–224

    MathSciNet  MATH  Google Scholar 

  39. https://grouplens.org/datasets/ (2009)

  40. Epinions product ratings network dataset – KONECT (2017). http://konect.cc/networks/epinions-rating

  41. Tripadvisor network dataset – KONECT (2017). http://konect.cc/networks/wang-tripadvisor

  42. Amazon (wang) network dataset–KONECT (2017). http://konect.cc/networks/wang-amazon

  43. http://files.grouplens.org/datasets/movielens/ml-10m-README.html (2009)

  44. Han J, Kamber M, Pei J (2012) Data mining concepts and techniques third edition. University of Illinois at Urbana-Champaign Micheline Kamber Jian Pei Simon Fraser University

  45. Davis J, Goadrich M (2006) The relationship between precision-recall and roc curves, 233–240

  46. Boyd K, Eng KH, Page CD. Area under the precision-recall curve: Point estimates and confidence intervals, 451–466 (Springer, 2013)

  47. Chawla N (2005) Data mining for imbalanced datasets: An overview 853–867

  48. Lichtenwalter RN, Chawla NV (2011) Lpmade: Link prediction made easy. J Mach Learn Res 12:2489–2492

    MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Data Science Research Lab, SRM University, Guntur, Andhra Pradesh, 522502, India

    T. Jaya Lakshmi

  2. School of Computer and Information Sciences, University of Hyderabad, Hyderabad, Telangana, 500002, India

    S. Durga Bhavani

Authors
  1. T. Jaya Lakshmi
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  2. S. Durga Bhavani
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Correspondence to T. Jaya Lakshmi.

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Lakshmi, T.J., Bhavani, S.D. Link prediction approach to recommender systems. Computing (2023). https://doi.org/10.1007/s00607-023-01227-0

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