Advertisement

Soft Computing

, Volume 23, Issue 2, pp 627–654 | Cite as

A survey on data mining techniques in recommender systems

  • Maryam Khanian NajafabadiEmail author
  • Azlinah Hj. Mohamed
  • Mohd Naz’ri Mahrin
Methodologies and Application
  • 481 Downloads

Abstract

Recommender systems have been regarded as gaining a more significant role with the emergence of the first research article on collaborative filtering (CF) in the mid-1990s. CF predicts the interests of an active user based on the opinions of users with similar interests. To extract information on the preference of users for a set of items and evaluate the performance of the recommender system’s techniques and algorithms, a critical analysis can be conducted. This study therefore employs a critical analysis on 131 articles in CF area from 36 journals published between the years 2010 and 2016. This analysis seems to be the exclusive survey which supports and motivates the community of researchers and practitioners. It is done by using the applications of users’ activities and intelligence computing and data mining techniques on CF recommendation systems. In addition, it provides a classification of the literature on academic database according to the benchmark recommendation databases, two users’ feedbacks (explicit and implicit feedbacks) which reflect their activities and categories of intelligence computing and data mining techniques. Eventually, this study provides a road map to guide future direction on recommender systems research and facilitates the accumulated and derived knowledge on the application of intelligence computing and data mining techniques in CF recommendation systems.

Keywords

Collaborative recommendation systems Public datasets Survey User feedback Intelligence computing Data mining techniques 

Notes

Acknowledgements

The authors would like to thank the Research Management Centre of—Universiti Teknologi MARA (UiTM) and the Malaysian Ministry of Education for their support and cooperation including researches and other individuals who are either directly or indirectly involved in this study.

Compliance with ethical standards

Conflicts of interest

Maryam Khanian Najafabadi, Azlinah Hj. Mohamed and Mohd Naz’ri Mahrin declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

References

  1. Adomavicius G, Zhang J (2012) Impact of data characteristics on recommender systems performance. ACM Trans Manag Inf Syst 3(1):3Google Scholar
  2. Adomavicius G, Tuzhilin A (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(2005):734–749Google Scholar
  3. Ahn HJ, Kang H, Lee J (2010) Selecting a small number of products for effective user profiling in collaborative filtering. Expert Syst Appl 37(4):3055–3062Google Scholar
  4. Anand D, Mampilli BS (2014) Folksonomy-based fuzzy user profiling for improved recommendations. Expert Syst Appl 41(5):2424–2436Google Scholar
  5. 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(5):5101–5109Google Scholar
  6. Bakshi S, Jagadev AK, Dehuri S, Wang GN (2014) Enhancing scalability and accuracy of recommendation systems using unsupervised learning and particle swarm optimization. Appl Soft Comput 15:21–29Google Scholar
  7. Bauer J, Nanopoulos A (2014) Recommender systems based on quantitative implicit customer feedback. Decis Support Syst 68:77–88Google Scholar
  8. Bellogín A, Castells P, Cantador I (2014) Neighbor selection and weighting in user-based collaborative filtering: a performance prediction approach. ACM Trans Web 8(2):12Google Scholar
  9. Berkovsky S, Kuflik T, Ricci F (2012) The impact of data obfuscation on the accuracy of collaborative filtering. Expert Syst Appl 39(5):5033–5042Google Scholar
  10. Bedi P, Sharma R (2012) Trust based recommender system using ant colony for trust computation. Expert Syst Appl 39(1):1183–1190Google Scholar
  11. Bilge A, Polat H (2013) A comparison of clustering-based privacy-preserving collaborative filtering schemes. Appl Soft Comput 13(5):2478–2489Google Scholar
  12. Birtolo C, Ronca D (2013) Advances in clustering collaborative filtering by means of fuzzy C-means and trust. Expert Syst Appl 40(17):6997–7009Google Scholar
  13. Boratto L, Carta S, Fenu G (2015) Discovery and representation of the preferences of automatically detected groups: exploiting the link between group modeling and clustering. Future Gener Comput Syst 64:165–174Google Scholar
  14. Bobadilla J, Ortega F, Hernando A, Glez-de-Rivera G (2013a) A similarity metric designed to speed up, using hardware, the recommender systems k-nearest neighbors algorithm. Knowl Based Syst 51:27–34Google Scholar
  15. Bobadilla J, Ortega F, Hernando A, Gutiérrez A (2013b) Recommender systems survey. Knowl Based Syst 46(2013):109–132Google Scholar
  16. Bobadilla J, Hernando A, Ortega F, Gutiérrez A (2012a) Collaborative filtering based on significances. Inf Sci 185(1):1–17Google Scholar
  17. Bobadilla J, Ortega F, Hernando A, Alcalá J (2011) Improving collaborative filtering recommender system results and performance using genetic algorithms. Knowl Based Syst 24(8):1310–1316Google Scholar
  18. Bobadilla J, Ortega F, Hernando A, Bernal J (2012b) Generalization of recommender systems: collaborative filtering extended to groups of users and restricted to groups of items. Expert Syst Appl 39(1):172–186Google Scholar
  19. Bobadilla J, Serradilla F, Bernal J (2010) A new collaborative filtering metric that improves the behavior of recommender systems. Knowl Based Syst 23(6):520–528Google Scholar
  20. Braida F, Mello CE, Pasinato MB, Zimbrão G (2015) Transforming collaborative filtering into supervised learning. Expert Syst Appl 42(10):4733–4742Google Scholar
  21. Briguez CE, Budan MC, Deagustini CA, Maguitman AG, Capobianco M, Simari GR (2014) Argument-based mixed recommenders and their application to movie suggestion. Expert Syst Appl 41(14):6467–6482Google Scholar
  22. Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User Adapt Interact 12(4):331–370zbMATHGoogle Scholar
  23. Casino F, Domingo-Ferrer J, Patsakis C, Puig D, Solanas A (2015) A k-anonymous approach to privacy preserving collaborative filtering. J Comput Syst Sci 81(6):1000–1011Google Scholar
  24. Cai Y, Leung HF, Li Q, Min H, Tang J, Li J (2014a) Typicality-based collaborative filtering recommendation. IEEE Trans Knowl Data Eng 26(3):766–779Google Scholar
  25. Cai Y, Lau RY, Liao SS, Li C, Leung HF, Ma LC (2014b) Object typicality for effective web of things recommendations. Decis Support Syst 63:52–63Google Scholar
  26. Cacheda F, Carneiro V, Fernández 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(1):2Google Scholar
  27. Chen MH, Teng CH, Chang PC (2015) Applying artificial immune systems to collaborative filtering for movie recommendation. Adv Eng Inform 29(4):830–839Google Scholar
  28. Cheng LC, Wang HA (2014) A fuzzy recommender system based on the integration of subjective preferences and objective information. Appl Soft Comput 18:290–301Google Scholar
  29. Chen YC, Lin YS, Shen YC, Lin SD (2013a) A modified random walk framework for handling negative ratings and generating explanations. ACM Trans Intell Syst Technol 4(1):12Google Scholar
  30. Chen L, Zeng W, Yuan Q (2013b) A unified framework for recommending items, groups and friends in social media environment via mutual resource fusion. Expert Syst Appl 40(8):2889–2903Google Scholar
  31. Choi K, Suh Y (2013) A new similarity function for selecting neighbors for each target item in collaborative filtering. Knowl Based Syst 37:146–153Google Scholar
  32. Colace F, De Santo M, Greco L, Moscato V, Picariello A (2015) A collaborative user-centered framework for recommending items in online social networks. Comput Hum Behav 51:694–704Google Scholar
  33. Da Costa AF, Manzato MG (2016) Exploiting multimodal interactions in recommender systems with ensemble algorithms. Inf Syst 56:120–132Google Scholar
  34. De Campos LM, Fernández-Luna JM, Huete JF, Rueda-Morales MA (2010) Combining content-based and collaborative recommendations: a hybrid approach based on Bayesian networks. Int J Approx Reason 51(7):785–799Google Scholar
  35. Devi MK, Venkatesh P (2013) Smoothing approach to alleviate the meager rating problem in collaborative recommender systems. Future Gener Comput Syst 29(1):262–270Google Scholar
  36. Elahi M, Ricci F, Rubens N (2013) Active learning strategies for rating elicitation in collaborative filtering: a system-wide perspective. ACM Trans Intell Syst Technol 5(1):13Google Scholar
  37. Eckhardt A (2012) Similarity of users’(content-based) preference models for collaborative filtering in few ratings scenario. Expert Syst Appl 39(14):11511–11516Google Scholar
  38. Feng H, Tian J, Wang HJ, Li M (2015) Personalized recommendations based on time-weighted overlapping community detection. Inf Manag 52(7):789–800Google Scholar
  39. Formoso V, Fernández D, Cacheda F, Carneiro V (2013) Using profile expansion techniques to alleviate the new user problem. Inf Process Manag 49(3):659–672Google Scholar
  40. Gan M, Jiang R (2013) Improving accuracy and diversity of personalized recommendation through power law adjustments of user similarities. Decis Support Syst 55(3):811–821Google Scholar
  41. Geng B, Li L, Jiao L, Gong M, Cai Q, Wu Y (2015) NNIA-RS: a multi-objective optimization based recommender system. Physica A 424:383–397MathSciNetzbMATHGoogle Scholar
  42. Gogna A, Majumdar A (2015a) Matrix completion incorporating auxiliary information for recommender system design. Expert Syst Appl 42(14):5789–5799Google Scholar
  43. Ghazarian S, Nematbakhsh MA (2015) Enhancing memory-based collaborative filtering for group recommender systems. Expert Syst Appl 42(7):3801–3812Google Scholar
  44. Gharibshah J, Jalili M (2014) Connectedness of users-items networks and recommender systems. Appl Math Comput 243:578–584MathSciNetzbMATHGoogle Scholar
  45. Ghazanfar MA, Prügel-Bennett A (2014) Leveraging clustering approaches to solve the gray-sheep users problem in recommender systems. Expert Syst Appl 41(7):3261–3275Google Scholar
  46. Ghazanfar MA, Prügel-Bennett A, Szedmak S (2012) Kernel-mapping recommender system algorithms. Inf Sci 208:81–104Google Scholar
  47. Gogna A, Majumdar A (2015b) A comprehensive recommender system model: improving accuracy for both warm and cold start users. IEEE Access 3:2803–2813Google Scholar
  48. Hawalah A, Fasli M (2014) Utilizing contextual ontological user profiles for personalized recommendations. Expert Syst Appl 41(10):4777–4797Google Scholar
  49. Hernando A, Moya R, Ortega F, Bobadilla J (2014) Hierarchical graph maps for visualization of collaborative recommender systems. J Inf Sci 40(1):97–106Google Scholar
  50. Hernando A, Bobadilla J, Ortega F, Tejedor J (2013) Incorporating reliability measurements into the predictions of a recommender system. Inf Sci 218:1–16MathSciNetGoogle Scholar
  51. Horsburgh B, Craw S, Massie S (2015) Learning pseudo-tags to augment sparse tagging in hybrid music recommender systems. Artif Intell 219:25–39Google Scholar
  52. Hoseini E, Hashemi S, Hamzeh A (2012) SPCF: a stepwise partitioning for collaborative filtering to alleviate sparsity problems. J Inf Sci 38(6):578–592Google Scholar
  53. Hostler RE, Yoon VY, Guimaraes T (2012) Recommendation agent impact on consumer online shopping: the movie magic case study. Expert Syst Appl 39(3):2989–2999Google Scholar
  54. Hsiao KJ, Kulesza A, Hero AO (2014) Social collaborative retrieval. IEEE J Sel Top Signal Process 8(4):680–689Google Scholar
  55. Hwang WS, Lee HJ, Kim SW, Won Y, Lee MS (2016) Efficient recommendation methods using category experts for a large dataset. Inf Fusion 28:75–82Google Scholar
  56. Huang S, Ma J, Cheng P, Wang S (2015) A hybrid multigroup coclustering recommendation framework based on information fusion. ACM Trans Intell Syst Technol 6(2):27Google Scholar
  57. Huang CL, Yeh PH, Lin CW, Wu DC (2014) Utilizing user tag-based interests in recommender systems for social resource sharing websites. Knowl Based Syst 56:86–96Google Scholar
  58. Hu L, Song G, Xie Z, Zhao K (2014) Personalized recommendation algorithm based on preference features. Tsinghua Sci Technol 19(3):293–299Google Scholar
  59. Javari A, Jalili M (2015) Accurate and novel recommendations: an algorithm based on popularity forecasting. ACM Trans Intell Syst Technol 5(4):56Google Scholar
  60. Kaššák O, Kompan M, Bieliková M (2015) Personalized hybrid recommendation for group of users: top-N multimedia recommender. Inf Process ManagGoogle Scholar
  61. Kagita VR, Pujari AK, Padmanabhan V (2015) Virtual user approach for group recommender systems using precedence relations. Inf Sci 294:15–30zbMATHGoogle Scholar
  62. Kaleli C (2014) An entropy-based neighbor selection approach for collaborative filtering. Knowl Based Syst 56:273–280Google Scholar
  63. Kolomvatsos K, Anagnostopoulos C, Hadjiefthymiades S (2014) An efficient recommendation system based on the optimal stopping theory. Expert Syst Appl 41(15):6796–6806Google Scholar
  64. Krestel R, Fankhauser P (2012) Personalized topic-based tag recommendation. Neurocomputing 76(1):61–70Google Scholar
  65. Kim HN, El Saddik A (2015) A stochastic approach to group recommendations in social media systems. Inf Syst 50:76–93Google Scholar
  66. Kim H, Kim HJ (2014) A framework for tag-aware recommender systems. Expert Syst Appl 41(8):4000–4009Google Scholar
  67. Kim HN, Ha I, Lee KS, Jo GS, El-Saddik A (2011a) Collaborative user modeling for enhanced content filtering in recommender systems. Decis Support Syst 51(4):772–781Google Scholar
  68. Kim HN, El-Saddik A, Jo GS (2011b) Collaborative error-reflected models for cold-start recommender systems. Decis Support Syst 51(3):519–531Google Scholar
  69. Koren Y (2010) Factor in the neighbors: Scalable and accurate collaborative filtering. ACM Trans Knowl Discov Data 4(1):1Google Scholar
  70. Langseth H, Nielsen TD (2015) Scalable learning of probabilistic latent models for collaborative filtering. Decis Support Syst 74:1–11Google Scholar
  71. Last.fm dataset, the official song tags and song similarity collection for the million song dataset, http://labrosa.ee.columbia.edu/millionsong/lastfm, (June 2014)
  72. Langseth H, Nielsen TD (2012) A latent model for collaborative filtering. Int J Approx Reason 53(4):447–466MathSciNetGoogle Scholar
  73. Liu J, Sui C, Deng D, Wang J, Feng B, Liu W, Wu C (2016) Representing conditional preference by boosted regression trees for recommendation. Inf Sci 327:1–20Google Scholar
  74. Liu W, Wu C, Feng B, Liu J (2015) Conditional preference in recommender systems. Expert Syst Appl 42(2):774–788Google Scholar
  75. Liu J, Wu C, Xiong Y, Liu W (2014a) List-wise probabilistic matrix factorization for recommendation. Inf Sci 278:434–447Google Scholar
  76. Liu H, Hu Z, Mian A, Tian H, Zhu X (2014b) A new user similarity model to improve the accuracy of collaborative filtering. Knowl Based Syst 56:156–166Google Scholar
  77. Liu J, Wu C, Liu W (2013) Bayesian probabilistic matrix factorization with social relations and item contents for recommendation. Decis Support Syst 55(3):838–850Google Scholar
  78. Liu Z, Qu W, Li H, Xie C (2010) A hybrid collaborative filtering recommendation mechanism for P2P networks. Future Gener Comput Syst 26(8):1409–1417Google Scholar
  79. Lika B, Kolomvatsos K, Hadjiefthymiades S (2014) Facing the cold start problem in recommender systems. Expert Syst Appl 41(4):2065–2073Google Scholar
  80. Li X, Chen H (2013) Recommendation as link prediction in bipartite graphs: a graph kernel-based machine learning approach. Decis Support Syst 54(2):880–890Google Scholar
  81. Lv G, Hu C, Chen S (2015) Research on recommender system based on ontology and genetic algorithm. NeurocomputingGoogle Scholar
  82. Lu J, Wu D, Mao M, Wang W, Zhang G (2015) Recommender system application developments: a survey. Decis Support Syst 74:12–32Google Scholar
  83. Luo X, Zhou M, Xia Y, Zhu Q (2014) An efficient non-negative matrix-factorization-based approach to collaborative filtering for recommender systems. IEEE Trans Industr Inf 10(2):1273–1284Google Scholar
  84. Luo X, Xia Y, Zhu Q (2012) Incremental collaborative filtering recommender based on regularized matrix factorization. Knowl Based Syst 27:271–280Google Scholar
  85. Ma H, Zhou TC, Lyu MR, King I (2011) Improving recommender systems by incorporating social contextual information. ACM Trans Inf Syst 29(2):9Google Scholar
  86. Mehta S, Banati H (2014) Context aware filtering using social behavior of frogs. Swarm Evol Comput 17:25–36Google Scholar
  87. Moreno MN, Segrera S, López VF, Muñoz MD, Sánchez ÁL (2016) Web mining based framework for solving usual problems in recommender systems. A case study for movies’ recommendation. Neurocomputing 176:72–80Google Scholar
  88. Moradi P, Ahmadian S, Akhlaghian F (2015) An effective trust-based recommendation method using a novel graph clustering algorithm. Physica A 436:462–481Google Scholar
  89. Movahedian H, Khayyambashi MR (2014) Folksonomy-based user interest and disinterest profiling for improved recommendations: an ontological approach. J Inf Sci 40(5):594–610Google Scholar
  90. Najafabadi MK, Mahrin MNR (2016) A systematic literature review on the state of research and practice of collaborative filtering technique and implicit feedback. Artif Intell Rev 45(2):167–201Google Scholar
  91. Najafabadi MK, Mahrin MNR, Chuprat S, Sarkan HM (2017) Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data. Comput Hum Behav 67:113–128Google Scholar
  92. Nakatsuji M, Toda H, Sawada H, Zheng JG, Hendler JA (2016) Semantic sensitive tensor factorization. Artif Intell 230:224–245MathSciNetzbMATHGoogle Scholar
  93. Nakatsuji M, Fujiwara Y (2014) Linked taxonomies to capture users’ subjective assessments of items to facilitate accurate collaborative filtering. Artif Intell 207:52–68MathSciNetGoogle Scholar
  94. Nikolakopoulos AN, Kouneli MA, Garofalakis JD (2015) Hierarchical itemspace rank: exploiting hierarchy to alleviate sparsity in ranking-based recommendation. Neurocomputing 163:126–136Google Scholar
  95. Pan W, Liu Z, Ming Z, Zhong H, Wang X, Xu C (2015a) Compressed knowledge transfer via factorization machine for heterogeneous collaborative recommendation. Knowl Based Syst 85:234–244Google Scholar
  96. Pan W, Zhong H, Xu C, Ming Z (2015b) Adaptive bayesian personalized ranking for heterogeneous implicit feedbacks. Knowl Based Syst 73:173–180Google Scholar
  97. Pan W, Yang Q (2013) Transfer learning in heterogeneous collaborative filtering domains. Artif Intell 197:39–55MathSciNetzbMATHGoogle Scholar
  98. Park DH, Kim HK, Choi IY, Kim JK (2012) A literature review and classification of recommender systems research. Expert Syst Appl 39(11):10059–10072Google Scholar
  99. Peng F, Lu J, Wang Y, Yi-Da Xu R, Ma C, Yang J (2016) N-dimensional Markov random field prior for cold-start recommendation. Neurocomputing 191:187–199Google Scholar
  100. Patra BK, Launonen R, Ollikainen V, Nandi S (2015) A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data. Knowl Based Syst 82:163–177Google Scholar
  101. Pirasteh P, Hwang D, Jung JJ (2015) Exploiting matrix factorization to asymmetric user similarities in recommendation systems. Knowl Based Syst 83:51–57Google Scholar
  102. Polatidis N, Georgiadis CK (2016) A multi-level collaborative filtering method that improves recommendations. Expert Syst Appl 48:100–110Google Scholar
  103. Ranjbar M, Moradi P, Azami M, Jalili M (2015) An imputation-based matrix factorization method for improving accuracy of collaborative filtering systems. Eng Appl Artif Intell 46:58–66Google Scholar
  104. Ramezani M, Moradi P, Akhlaghian F (2014) A pattern mining approach to enhance the accuracy of collaborative filtering in sparse data domains. Physica A 408:72–84Google Scholar
  105. Rana C, Jain SK (2014) An evolutionary clustering algorithm based on temporal features for dynamic recommender systems. Swarm Evol Comput 14:21–30Google Scholar
  106. Rafeh R, Bahrehmand A (2012) An adaptive approach to dealing with unstable behaviour of users in collaborative filtering systems. J Inf Sci 38(3):205–221Google Scholar
  107. Ren Y, Li G, Zhang J, Zhou W (2013) Lazy collaborative filtering for data sets with missing values. IEEE Trans Cybern 43(6):1822–1834Google Scholar
  108. Salah A, Rogovschi N, Nadif M (2016) A dynamic collaborative filtering system via a weighted clustering approach. Neurocomputing 175:206–215Google Scholar
  109. Shambour Q, Lu J (2015) An effective recommender system by unifying user and item trust information for B2B applications. J Comput Syst Sci 81(7):1110–1126MathSciNetzbMATHGoogle Scholar
  110. Shambour Q, Lu J (2012) A trust-semantic fusion-based recommendation approach for e-business applications. Decis Support Syst 54(1):768–780Google Scholar
  111. Shang MS, Zhang ZK, Zhou T, Zhang YC (2010) Collaborative filtering with diffusion-based similarity on tripartite graphs. Physica A 389(6):1259–1264Google Scholar
  112. Shinde SK, Kulkarni U (2012) Hybrid personalized recommender system using centering-bunching based clustering algorithm. Expert Syst Appl 39(1):1381–1387Google Scholar
  113. Sun Z, Han L, Huang W, Wang X, Zeng X, Wang M, Yan H (2015) Recommender systems based on social networks. J Syst Softw 99:109–119Google Scholar
  114. Tan S, Bu J, Qin X, Chen C, Cai D (2014) Cross domain recommendation based on multi-type media fusion. Neurocomputing 127:124–134Google Scholar
  115. Toledo RY, Mota YC, Martínez L (2015) Correcting noisy ratings in collaborative recommender systems. Knowl-Based Syst 76:96–108Google Scholar
  116. Tyagi S, Bharadwaj KK (2013) Enhancing collaborative filtering recommendations by utilizing multi-objective particle swarm optimization embedded association rule mining. Swarm Evol Comput 13:1–12Google Scholar
  117. Tsai CF, Hung C (2012) Cluster ensembles in collaborative filtering recommendation. Appl Soft Comput 12(4):1417–1425Google Scholar
  118. Umyarov A, Tuzhilin A (2011) Using external aggregate ratings for improving individual recommendations. ACM Trans Web 5(1):3Google Scholar
  119. Wang Z, Yu X, Feng N, Wang Z (2014a) An improved collaborative movie recommendation system using computational intelligence. J VisLang Comput 25(6):667–675Google Scholar
  120. Wang S, Sun J, Gao BJ, Ma J (2014b) VSRank: a novel framework for ranking-based collaborative filtering. ACM Trans Intell Syst Technol 5(3):51Google Scholar
  121. Wang J, Ke L (2014) Feature subspace transfer for collaborative filtering. Neurocomputing 136:1–6Google Scholar
  122. Wen Y, Liu Y, Zhang ZJ, Xiong F, Cao W (2014) Compare two community-based personalized information recommendation algorithms. Physica A 398:199–209Google Scholar
  123. Wu H, Yue K, Pei Y, Li B, Zhao Y, Dong F (2016) Collaborative topic regression with social trust ensemble for recommendation in social media systems. Knowl Based SystGoogle Scholar
  124. Wu ML, Chang CH, Liu RZ (2014) Integrating content-based filtering with collaborative filtering using co-clustering with augmented matrices. Expert Syst Appl 41(6):2754–2761Google Scholar
  125. Xie F, Chen Z, Shang J, Feng X, Li J (2015) A link prediction approach for item recommendation with complex number. Knowl Based Syst 81:148–158Google Scholar
  126. Xie F, Chen Z, Shang J, Fox GC (2014) Grey forecast model for accurate recommendation in presence of data sparsity and correlation. Knowl Based Syst 69:179–190Google Scholar
  127. Xu Y, Yin J (2015) Collaborative recommendation with user generated content. Eng Appl Artif Intell 45:281–294Google Scholar
  128. Yakut I, Polat H (2012) Estimating NBC-based recommendations on arbitrarily partitioned data with privacy. Knowl Based Syst 36:353–362Google Scholar
  129. Yan S, Zheng X, Chen D, Wang Y (2013) Exploiting two-faceted web of trust for enhanced-quality recommendations. Expert Syst Appl 40(17):7080–7095Google Scholar
  130. Yera R, Castro J, Martínez L (2016) A fuzzy model for managing natural noise in recommender systems. Appl Soft Comput 40:187–198Google Scholar
  131. Yu H, Kim S (2012) SVM tutorial–classification, regression and ranking handbook of natural computing. Springer, Berlin, pp 479–506Google Scholar
  132. Zahra S, Ghazanfar MA, Khalid A, Azam MA, Naeem U, Prugel-Bennett A (2015) Novel centroid selection approaches for KMeans-clustering based recommender systems. Inf Sci 320:156–189MathSciNetGoogle Scholar
  133. Zeng W, Zhu YX, Lü L, Zhou T (2011) Negative ratings play a positive role in information filtering. Physica A 390(23):4486–4493MathSciNetGoogle Scholar
  134. Zhao W, Guan Z, Liu Z (2015) Ranking on heterogeneous manifolds for tag recommendation in social tagging services. Neurocomputing 148:521–534Google Scholar
  135. Zhou X, He J, Huang G, Zhang Y (2015) SVD-based incremental approaches for recommender systems. J Comput Syst Sci 81(4):717–733MathSciNetzbMATHGoogle Scholar
  136. Zhang J, Peng Q, Sun S, Liu C (2014) Collaborative filtering recommendation algorithm based on user preference derived from item domain features. Physica A 396:66–76Google Scholar
  137. Zhang Z, Lin H, Liu K, Wu D, Zhang G, Lu J (2013) A hybrid fuzzy-based personalized recommender system for telecom products/services. Inf Sci 235:117–129Google Scholar
  138. Zhang Z, Zhao K, Zha H (2012) Inducible regularization for low-rank matrix factorizations for collaborative filtering. Neurocomputing 97:52–62Google Scholar
  139. Zhang ZK, Zhou T, Zhang YC (2010) Personalized recommendation via integrated diffusion on user-item-tag tripartite graphs. Physica A 389(1):179–186MathSciNetGoogle Scholar
  140. Zhu T, Ren Y, Zhou W, Rong J, Xiong P (2014) An effective privacy preserving algorithm for neighborhood-based collaborative filtering. Future Gener Comput Syst 36:142–155Google Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Maryam Khanian Najafabadi
    • 1
    Email author
  • Azlinah Hj. Mohamed
    • 1
  • Mohd Naz’ri Mahrin
    • 2
  1. 1.Advanced Analytics Engineering Centre (AAEC)Universiti Teknologi MARA (UiTM)Shah AlamMalaysia
  2. 2.Advanced Informatics School (AIS)Universiti Teknologi Malaysia (UTM)Kuala LumpurMalaysia

Personalised recommendations