Skip to main content

Advertisement

Log in

Evolutionary computing in recommender systems: a review of recent research

  • Published:
Natural Computing Aims and scope Submit manuscript

Abstract

One of the main current applications of intelligent systems is recommender systems (RS). RS can help users to find relevant items in huge information spaces in a personalized way. Several techniques have been investigated for the development of RS. One of them is evolutionary computational (EC) techniques, which is an emerging trend with various application areas. The increasing interest in using EC for web personalization, information retrieval and RS fostered the publication of survey papers on the subject. However, these surveys have analyzed only a small number of publications, around ten. This study provides a comprehensive review of more than 65 research publications focusing on five aspects we consider relevant for such: the recommendation technique used, the datasets and the evaluation methods adopted in their experimental parts, the baselines employed in the experimental comparison of proposed approaches and the reproducibility of the reported experiments. At the end of this review, we discuss negative and positive aspects of these papers, as well as point out opportunities, challenges and possible future research directions. To the best of our knowledge, this review is the most comprehensive review of various approaches using EC in RS. Thus, we believe this review will be a relevant material for researchers interested in EC and RS.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. Pu et al. (2011) claim that these criteria are very hard or even impossible to measure. Moreover, it was shown by Jannach et al. (2013) that the choice of measurable accuracy criteria to evaluate RS is not easy and that objective evaluation measures and users’ subjective quality measures often disagree.

  2. http://www.netflixprize.com/.

  3. Al-Shamri and Bharadwaj (2008), Bobadilla et al. (2011), Dao et al. (2012), Gao and Li (2008), Ho et al. (2007), Hwang et al. (2010), Kim and Ahn (2004), Kj and Ahn (2008), Zhang and Chang (2006).

  4. Al-Shamri and Bharadwaj (2008), Bobadilla et al. (2011), Dao et al. (2012), Fong et al. (2008b), Kj and Ahn (2008).

  5. https://scholar.google.com/.

  6. www.researchgate.net.

  7. It is assumed only one feedback value for a user-item pair, here.

  8. An offspring is created by adding small (normally distributed) values to each parameter of the parent individual.

  9. The authors described the same approach in more details in Fong et al. (2008a, b).

  10. http://www.mymedialite.net/.

  11. PSO techniques, see Poli (2008), Trelea (2003) or Yang et al. (2013), are intended to simulate social behavior as a stylized representation of the movement of organisms in swarms. PSO can be used on optimization problems that are partially irregular or noisy.

  12. See https://www.fit.fraunhofer.de/en/fb/cscw/projects/mace.html for more details, however, the data seems to be not publicly available.

  13. The same approach is described in Athani et al. (2013, 2014) and Bojewar and Fulekar (2012) while Badhe et al. (2014) discuss a feasibility study of its implementation. However, these papers have no added value compared to Kim et al. (2010).

  14. In IEA, often applied to problems that are difficult to evaluate quantitatively, the fitness values of candidate solutions are based on the evaluation of a user according to her own interests.

  15. See Kansei engineering, e.g. Nagamachi (1995), Sharma et al. (2013), for more details.

  16. Experimental settings are not clearly provided in the paper.

  17. The authors analyzed or mentioned more papers in this review, however, only the different approaches are presented in Tables 2, 34, 5 and 6.

  18. The authors only considered recommendation baselines, here. Even if some papers presenting EC-based clustering compare their results to other clustering techniques (e.g. k-means), they lack comparison to some simple recommendation baselines.

  19. http://mahout.apache.org/.

  20. http://rival.recommenders.net/.

References

  • Abbas A, Zhang L, Khan S (2015) A survey on context-aware recommender systems based on computational intelligence techniques. Computing 97(7):667–690

    Article  MathSciNet  Google Scholar 

  • Abdi H, Williams LJ (2010) Principal component analysis. Wiley Interdiscip Rev Comput Stat 2(4):433–459

    Article  Google Scholar 

  • Adomavicius G, Mobasher B, Ricci F, Tuzhilin A (2011) Context-aware recommender systems. AI Mag 32(3):67–80

    Article  Google Scholar 

  • Agarwal V, Bharadwaj K (2011) Trust-enhanced recommendation of friends in web based social networks using genetic algorithms to learn user preferences. In: Trends in computer science, engineering and information technology, communications in computer and information science, vol 204. Springer, Berlin Heidelberg, pp 476–485

  • Agarwal V, Bharadwaj K (2013) A collaborative filtering framework for friends recommendation in social networks based on interaction intensity and adaptive user similarity. Soc Netw Anal Min 3(3):359–379

    Article  Google Scholar 

  • Ahmadi MR (2009) A new recommender system based on cooperative co-evolution algorithm. Int J Inf Commun Technol 1(1):39–47

    Google Scholar 

  • Al-Shamri MYH, Bharadwaj KK (2008) Fuzzy-genetic approach to recommender systems based on a novel hybrid user model. Expert Syst Appl 35(3):1386–1399

    Article  Google Scholar 

  • Alpaydin E (2010) Introduction to machine learning, 2nd edn. The MIT Press, London

    MATH  Google Scholar 

  • Amatriain X, Pujol JM, Oliver N (2009) I like it... i like it not: evaluating user ratings noise in recommender systems. In: Proceedings of the 17th international conference on user modeling, adaptation, and personalization: formerly UM and AH, Springer-Verlag, pp 247–258

  • Anand D, Bharadwaj KK (2010) Adaptive user similarity measures for recommender systems: a genetic programming approach. IEEE Int Conf Comput Sci Inform Technol 8:121–125

    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(5):5101–5109

    Article  Google Scholar 

  • Athani M, Khan AU, Pathak N (2013) A recommender system based on genetic algorithm for songs on web. Int J Adv Res Comput Sci Softw Eng 3(12):763–766

    Google Scholar 

  • Athani M, Pathak N, Khan AU (2014) Dynamic music recommender system using genetic algorithm. Int J Eng Adv Technol 3(4):230–232

    Google Scholar 

  • Bäck T (1996) Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms. Oxford University Press, Oxford

    MATH  Google Scholar 

  • Back T, Rudolph G, paul Schwefel H (1993) Evolutionary programming and evolution strategies: similarities and differences. In: Annual conference on evolutionary programming, pp 11–22

  • Badhe N, Mishra D, Joshi C, Shukla N (2014) Recommender system for music data using genetic algorithm. Int J Innov Adv Comput Sci 3(2):66–69

    Google Scholar 

  • Balbach S (1995) ClarkNet web server logs. http://ita.ee.lbl.gov/html/contrib/ClarkNet-HTTP.html. Accessed 2 Sept 2015

  • Baltrunas L, Ludwig B, Ricci F (2011) Matrix factorization techniques for context aware recommendation. In: Proceedings of the Fifth ACM conference on recommender systems, ACM, pp 301–304

  • Banati H, Mehta S (2010a) Memetic collaborative filtering based recommender system. In: Vaagdevi international conference on information technology for real world problems, pp 102–107

  • Banati H, Mehta S (2010b) A multi-perspective evaluation of ma and ga for collaborative filtering recommender system. Int J Comput Sci Inform Technol 2(5):103–122

    Article  Google Scholar 

  • Belém F, Martins E, Almeida J, Gonçalves M (2011a) Associatie tag recommendation data. http://vod.dcc.ufmg.br/recc/. Accessed 2 Sept 2015

  • Belém F, Martins E, Pontes T, Almeida J, Gonçalves M (2011b) Associative tag recommendation exploiting multiple textual features. In: International ACM SIGIR conference on research and development in information retrieval, ACM, pp 1033–1042

  • Belém F, Santos R, Almeida J, Gonçalves M (2013) Topic diversity in tag recommendation. In: ACM Conference on recommender systems, ACM, pp 141–148

  • Belém FM, Martins EF, Almeida JM, Gonçalves MA (2014) Personalized and object-centered tag recommendation methods for web 2.0 applications. Inf Process Manag 50(4):524–553

    Article  Google Scholar 

  • Bellogín A, Said A, de Vries AP (2014) The magic barrier of recommender systems no magic, just ratings. In: User modeling, adaptation, and personalization, Lecture notes in computer science, vol 8538, Springer International Publishing, pp 25–36

  • Bhattacharya M (2013) Evolutionary approaches to expensive optimisation. J Adv Res Artif Intell 2(3):53–59

    Google Scholar 

  • Bibsonomy (2015) Bibsonomy data.http://www.kde.cs.uni-kassel.de/bibsonomy/dumps. Accessed 2 Sept 2015

  • 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):1310–1316

    Article  Google Scholar 

  • Bobadilla J, Ortega F, Hernando A, Gutirrez A (2013) Recommender systems survey. Knowl Based Syst 46:109–132

    Article  Google Scholar 

  • Bojewar S, Fulekar J (2012) Application of genetic algorithm for audio search with recommender system. Int J Adv Comput Math Sci 3(2):224–226

    Google Scholar 

  • Boumaza A, Brun A (2012a) From neighbors to global neighbors in collaborative filtering: an evolutionary optimization approach. In: Annual conference on genetic and evolutionary computation, ACM, pp 345–352

  • Boumaza A, Brun A (2012b) Stochastic search for global neighbors selection in collaborative filtering. In: Annual ACM symposium on applied computing, ACM, pp 232–237

  • Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  MATH  Google Scholar 

  • Burke R (1998) Entree Chicago recommendation data. https://kdd.ics.uci.edu/databases/entree/entree.html. Accessed 2 Sept 2015

  • Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User Adap Inter 12(4):331–370

    Article  MATH  Google Scholar 

  • Castro LNd (2006) Fundamentals of natural computing: basic concepts, algorithms, and applications. Chapman & Hall/CRC, Boca Raton

    MATH  Google Scholar 

  • Celma O (2006) Last.fm dataset 1k users. http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/lastfm-1K.html. Accessed 2 Sept 2015

  • Chen X, Ong YS, Lim MH, Tan KC (2011) A multi-facet survey on memetic computation. IEEE Trans Evol Comput 15(5):591–607

    Article  Google Scholar 

  • Cramer NL (1985) A representation for the adaptive generation of simple sequential programs. In: International conference on genetic algorithms. L. Erlbaum Associates Inc., pp 183–187

  • Cremonesi P, Koren Y, Turrin R (2010) Performance of recommender algorithms on top-n recommendation tasks. In: ACM conference on recommender systems. pp 39–46

  • da Silva E, Camilo Junior C, Pascoal L, Rosa T (2014) An evolutionary approach for combining results of recommender systems techniques based on collaborative filtering. In: IEEE congress on evolutionary computation, pp 959–966

  • Dao TH, Jeong SR, Ahn H (2012) A novel recommendation model of location-based advertising: context-aware collaborative filtering using GA approach. Expert Syst Appl 39(3):3731–3739

    Article  Google Scholar 

  • Darvishi-Mirshekarlou F, Akbarpour S, Feizi-Derakhshi M (2013) Reviewing cluster based collaborative filtering approaches. Int J Comput Appl Technol Res 2(6):650–659

    Google Scholar 

  • Das S, Maity S, Qu BY, Suganthan P (2011) Real-parameter evolutionary multimodal optimization a survey of the state-of-the-art. Swarm Evol Comput 1(2):71–88

    Article  Google Scholar 

  • Degemmis M, Lops P, Semeraro G (2007) A content-collaborative recommender that exploits wordnet-based user profiles for neighborhood formation. User Model User Adap Inter 17(3):217–255

    Article  Google Scholar 

  • Demir G, Uyar A, Gündüz-Öğüdücü Ş (2010) Multiobjective evolutionary clustering of web user sessions: a case study in web page recommendation. Soft Comput 14(6):579–597

    Article  Google Scholar 

  • Demir GN, Uyar AS, Ögüdücü SG (2007) Graph-based sequence clustering through multiobjective evolutionary algorithms for web recommender systems. In: Annual conference on genetic and evolutionary computation, ACM, pp 1943–1950

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

    Article  Google Scholar 

  • Dian H, Ying L (2010) E-commerce recommendation method based on genetic algorithm and composite weight matrix. In: International conference on electrical and control engineering. pp 2760–2763

  • Dietterich TG (2000) Ensemble methods in machine learning. In: International workshop on multiple classifier systems. Springer-Verlag, pp 1–15

  • Fawcett T (2006) An introduction to roc analysis. Pattern Recogn Lett 27(8):861–874

    Article  MathSciNet  Google Scholar 

  • Floreano D, Mattiussi C (2008) Bio-inspired artificial intelligence: theories, methods, and technologies. The MIT Press, Cambridge

    Google Scholar 

  • Fogel L, Owens A, Walsh M (1966) Artificial intelligence through simulated evolution. Wiley, New York

    MATH  Google Scholar 

  • Fong S, Ho Y, Hang Y (2008a) On improving ga-based collaborative filtering for online recommender

  • Fong S, Ho Y, Hang Y (2008b) Using genetic algorithm for hybrid modes of collaborative filtering in online recommenders. In: International conference on hybrid intelligent systems. pp 174–179

  • Gao L, Li C (2008) Hybrid personalized recommended model based on genetic algorithm. In: International conference on wireless communications, networking and mobile computing. pp 9215–9218

  • Geng B, Li L, Jiao L, Gong M, Cai Q, Wu Y (2015) Nnia-rs: a multi-objective optimization based recommender system. Phys A 424:383–397

    Article  MathSciNet  Google Scholar 

  • George T, Merugu S (2005) A scalable collaborative filtering framework based on co-clustering. In: IEEE International conference on data mining, IEEE Computer Society, pp 625–628

  • Georgiou O, Tsapatsoulis N (2010) Improving the scalability of recommender systems by clustering using genetic algorithms. In: International conference on artificial neural networks, vol 6352. Lecture Notes in Computer ScienceSpringer, Berlin Heidelberg, pp 442–449

  • Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning, 1st edn. Addison-Wesley Longman Publishing Co., Inc, Boston

    MATH  Google Scholar 

  • Goldberg K (2003) Jester data. http://eigentaste.berkeley.edu/dataset/. Accessed 2 Sept 2015

  • Goldberg K (2009) Donation dashboard data. http://dd.berkeley.edu/dataset/. Accessed 2 Sept 2015

  • Gong M, Jiao L, Du H, Bo L (2008) Multiobjective immune algorithm with nondominated neighbor-based selection. Evol Comput 16(2):225–255

    Article  Google Scholar 

  • Gopalan K, Nathan S, Bhanu T, Channa A, Saraf P (2011) A context aware personalized media recommendation system: an adaptive evolutionary algorithm approach. In: International conference on bio-inspired computing: theories and applications, pp 45–50

  • GroupLens (1998) Movielens 100k data. http://grouplens.org/datasets/movielens/. Accessed 2 Sept 2015

  • GroupLens (2003) Movielens 1m data. http://grouplens.org/datasets/movielens/. Accessed 2 Sept 2015

  • Guimarães A, Costa TF, Lacerda A, Pappa GL, Ziviani N (2013) Guard: a genetic unified approach for recommendation. J Inf Data Manag 4(3):295–310

    Google Scholar 

  • Gündüz Ş, Özsu MT (2003) A web page prediction model based on click-stream tree representation of user behavior. In: ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 535–540

  • GVU (1998) WWW user survey. http://www.cc.gatech.edu/gvu/user_surveys/survey-1998-04/datasets/. Accessed 2 Sept 2015

  • Hao Z (2013) Mixed recommendation algorithm based on commodity gene and genetic algorithm. In: International conference on information engineering and applications, vol 219. Lecture Notes in Electrical EngineeringSpringer, London, pp 849–857

  • Hawkins R (2015) Ranking and scoring – guideline. Tech. rep., ICRA Learning resources

  • Herlocker JL, Konstan JA, Borchers A, Riedl J (1999) An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd annual international ACM SIGIR conference on research and development in information retrieval. ACM, pp 230–237

  • Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):5–53

    Article  Google Scholar 

  • Ho Y, Fong S, Hang Y (2007) A hybrid ga-based collaborative filtering model for online recommenders. In: International conference on e-Business, pp 200–203

  • Hofmann T (2004) Latent semantic models for collaborative filtering. ACM Trans Inf Syst 22(1):89–115

    Article  Google Scholar 

  • Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  • Horváth T (2009) A model of user preference learning for content-based recommender systems. Comput Inform 28(4):453–481

    MathSciNet  Google Scholar 

  • Hu L, Cao J, Xu G, Cao L, Gu Z, Zhu C (2013) Personalized recommendation via cross-domain triadic factorization. In: International conference on World Wide Web, pp 595–606

  • Hwang CS, Su YC, Tseng KC (2010) Using genetic algorithms for personalized recommendation. In: Computational collective intelligence. Technologies and applications, Lecture Notes in Computer Science, vol 6422. Springer, Berlin, pp 104–112

  • Jack SB, Kadie CM, Heckerman D (1998) Microsoft anonymous web data. https://kdd.ics.uci.edu/databases/msweb/msweb.html. Accessed 2 Sept 2015

  • Jannach D, Lerche L, Gedikli F, Bonnin G (2013) What recommenders recommend an analysis of accuracy, popularity, and sales diversity effects. User modeling, adaptation, and personalization, vol 7899. Lecture notes in computer science. Springer, Berlin, pp 25–37

  • Järvelin K, Kekäläinen J (2000) Ir evaluation methods for retrieving highly relevant documents. In: International ACM SIGIR conference on research and development in information retrieval. ACM, pp 41–48

  • Joachims T (2006) Training linear svms in linear time. In: ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 217–226

  • Jones G (1998) Genetic and evolutionary algorithms. In: Schleyer PvR, Allinger NL, Clark T, Gasteiger J, Kollman PA, Schaefer III HF, Schreiner PR (eds) Encyclopedia of computational chemistry. Wiley, Chichester, pp 1127–1136

  • Jones DR, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black-box functions. J Glob Optim 13(4):455–492. doi: 10.1023/A:1008306431147

  • Kant V, Bharadwaj K (2013) A user-oriented content based recommender system based on reclusive methods and interactive genetic algorithm. In: International conference on bio-inspired computing: theories and applications. Springer India, pp 543–554

  • Karatzoglou A, Amatriain X, Baltrunas L, Oliver N (2010) Multiverse recommendation: N-dimensional tensor factorization for context-aware collaborative filtering. In: Proceedings of the fourth ACM conference on recommender systems. ACM, pp 79–86

  • Kelly D, Teevan J (2003) Implicit feedback for inferring user preference: a bibliography. SIGIR Forum 37(2):18–28

    Article  Google Scholar 

  • Khoshneshin M, Street WN (2010) Incremental collaborative filtering via evolutionary co-clustering. In: ACM conference on recommender systems. ACM, pp 325–328

  • Khrouf H, Troncy R (2013) Hybrid event recommendation using linked data and user diversity. In: Proceedings of the 7th ACM conference on recommender systems. ACM, pp 185–192

  • Kim HT, Ahn CW (2012) An interactive evolutionary approach to designing novel recommender systems. Int J Phys Sci 7(15):622–625

    Google Scholar 

  • Kim HT, Kim E, Lee JH, Ahn CW (2010) A recommender system based on genetic algorithm for music data. Int Conf Comput Eng Technol 6:414–417

    Google Scholar 

  • Kim HT, Lee JH, Ahn CW (2011) A recommender system based on interactive evolutionary computation with data grouping. Proc Comput Sci 3:611–616

    Article  Google Scholar 

  • Kim HT, An J, Wook AC (2014) A new evolutionary approach to recommender systems. IEICE Trans Inf Syst E97-D(3):622–625

  • Kim Kj, Ahn H (2004) Using a clustering genetic algorithm to support customer segmentation for personalized recommender systems. In: International conference on AI, simulation, and planning in high autonomy systems. Springer-Verlag, pp 409–415

  • Kim Kj, Ahn H (2008) A recommender system using ga k-means clustering in an online shopping market. Expert Syst Appl 34(2):1200–1209

    Article  Google Scholar 

  • Kolda TG, Bader BW (2009) Tensor decompositions and applications. SIAM Rev 51(3):455–500

    Article  MathSciNet  MATH  Google Scholar 

  • Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42(8):30–37

    Article  Google Scholar 

  • Lathauwer LD, Moor BD, Vandewalle J (2000) A multilinear singular value decomposition. SIAM J Matrix Anal Appl 21(4):1253–1278

    Article  MathSciNet  MATH  Google Scholar 

  • Leskovec J (2006) Amazon product co-purchasing network metadata. https://snap.stanford.edu/data/. Accessed 2 Sept 2015

  • Li Q, Yao M, Yang J, Xu N (2014) Genetic algorithm and graph theory based matrix factorization method for online friend recommendation. The Scientific World Journal 2014

  • Liang Y, Li Q (2011) Incorporating interest preference and social proximity into collaborative filtering for folk recommendation. In: SIGIR 2011 Workshop on Social Web Search and Mining, Analysis under crisis, Beijing, China, 24–28 July 2011

  • Lourenço HR, Martin OC, Stützle T (2003) Iterated local search. In: Handbook of Metaheuristics, volume 57 of International series in operations research and management science. Kluwer Academic Publishers, pp 321–353

  • Lü L, Medo M, Yeung CH, Zhang YC, Zhang ZK, Zhou T (2012) Recommender systems. Phys Rep 519(1):1–49

    Article  Google Scholar 

  • Marung U, Theera-Umpon N, Auephanwiriyakul S (2014) Applying memetic algorithm-based clustering to recommender system with high sparsity problem. J Cent South Univ 21(9):3541–3550

    Article  Google Scholar 

  • Mary P, Baburaj E (2013) Constraint informative rules for genetic algorithm-based web page recommendation system. J Comput Sci 9(11):1589–1601

    Article  Google Scholar 

  • Massa P (2011) Epinions data. http://www.trustlet.org/wiki/Epinions_dataset. Accessed 2 Sept 2015

  • Meena R, Bharadwaj KK (2013) Group recommender system based on rank aggregation—an evolutionary approach. In: Mining intelligence and knowledge exploration, Lecture Notes in Computer Science, vol 8284. Springer International Publishing, pp 663–676

  • Middleton SE, Shadbolt NR, De Roure DC (2004) Ontological user profiling in recommender systems. ACM Trans Inf Syst 22(1):54–88

    Article  Google Scholar 

  • Min SH, Han I (2005) Optimizing collaborative filtering recommender systems. Advances in Web Intelligence, vol 3528. Lecture Notes in Computer ScienceSpringer, Berlin Heidelberg, pp 313–319

  • Nagamachi M (1995) Kansei engineering: a new ergonomic consumer-oriented technology for product development. Int J Ind Ergon 15(1):3–11

    Article  Google Scholar 

  • Nanas N, de Roeck A (2010) A review of evolutionary and immune-inspired information filtering. Nat Comput 9(3):545–573

    Article  MathSciNet  MATH  Google Scholar 

  • Naruchitparames J, Gunes M, Louis S (2011) Friend recommendations in social networks using genetic algorithms and network topology. In: IEEE Congress on Evolutionary Computation, pp 2207–2214

  • Navgaran D, Moradi P, Akhlaghian F (2013) Evolutionary based matrix factorization method for collaborative filtering systems. In: Electrical Engineering (ICEE), 2013 21st Iranian Conference on, pp 1–5

  • Nguyen TT, Yang S, Branke J (2012) Evolutionary dynamic optimization: a survey of the state of the art. Swarm Evol Comput 6:1–24

    Article  Google Scholar 

  • Ning X, Karypis G (2011) Slim: Sparse linear methods for top-n recommender systems. In: International conference on data mining, IEEE Computer Society, pp 497–506

  • P A Khodke PBR (2013) Genetic algorithm based similarity transitivity in collaborative filtering. Int J Eng Res Technol 2(12):2933–2936

    Google Scholar 

  • Panniello U, Tuzhilin A, Gorgoglione M (2014) Comparing context-aware recommender systems in terms of accuracy and diversity. User Model User Adap Inter 24(1–2):35–65

    Article  Google Scholar 

  • Park DH, Kim HK, Choi IY, Kim JK (2012) A literature review and classification of recommender systems research. Expert Syst Appl 39(11):10059–10072

    Article  Google Scholar 

  • Pazzani M, Billsus D (1997) Learning and revising user profiles: the identification ofinteresting web sites. Mach Learn 27(3):313–331

    Article  Google Scholar 

  • Pazzani MJ, Billsus D (2007) The adaptive web. Springer-Verlag, chap Content-based Recommendation Systems, pp 325–341

  • Pei J, Han J, Mortazavi-Asl B, Pinto H, Chen Q, Dayal U, Hsu MC (2001) Prefixspan: mining sequential patterns efficiently by prefix-projected pattern growth. In: International conference on data engineering, pp 215–224

  • Pero Š, Horváth T (2013) Opinion-driven matrix factorization for rating prediction. User modeling, adaptation, and personalization, vol 7899. Lecture Notes in Computer Science. Springer, Berlin Heidelberg, pp 1–13

  • Poli R (2008) Analysis of the publications on the applications of particle swarm optimisation. J Artif Evol Appl 2008: Article ID 685,175

  • Pu P, Chen L, Hu R (2011) A user-centric evaluation framework for recommender systems. In: Proceedings of the Fifth ACM conference on recommender systems. ACM, pp 157–164

  • Queiroz A, Marinho LB (2014) Event recommendation in event-based social networks. In: Late-breaking results, doctoral consortium and workshop proceedings of the 25th ACM hypertext and social media conference, CEUR Workshop Proceedings

  • Rambharose T, Nikov A (2010) Computational intelligence-based personalization of interactive web systems. WSEAS Trans Inf Sci Appl 7(4):484–497

    Google Scholar 

  • Rechenberg I (1973) Evolutionsstrategie: Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. Frommann-Holzboog, Stuttgart

    Google Scholar 

  • Rendle S, Schmidt-Thieme L (2010) Pairwise interaction tensor factorization for personalized tag recommendation. In: International conference on web search and data mining. ACM, pp 81–90

  • Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) Bpr: Bayesian personalized ranking from implicit feedback. In: Proceedings of the twenty-fifth conference on uncertainty in artificial intelligence. AUAI Press, pp 452–461

  • Rendle S, Gantner Z, Freudenthaler C, Schmidt-Thieme L (2011) Fast context-aware recommendations with factorization machines. In: Proceedings of the 34th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 635–644

  • Ribeiro MT, Lacerda A, Veloso A, Ziviani N (2012) Pareto-efficient hybridization for multi-objective recommender systems. In: ACM Conference on recommender systems. ACM, pp 19–26

  • Ricci F, Rokach L, Shapira B, Kantor PB (eds) (2011) Recommender systems handbook. Springer, Heidelberg

    MATH  Google Scholar 

  • Salehi M, Kamalabadi IN, Ghaznavi-Ghoushchi MB (2013a) Attribute-based collaborative filtering using genetic algorithm and weighted c-means algorithm. Int J Bus Inf Syst 13(3):265–283

    Google Scholar 

  • Salehi M, Pourzaferani M, Razavi SA (2013b) Hybrid attribute-based recommender system for learning material using genetic algorithm and a multidimensional information model. Egypt Inf J 14(1):67–78

    Article  Google Scholar 

  • Sarwar B, Karypis G, Konstan J, Riedl J (2002) Incremental singular value decomposition algorithms for highly scalable recommender systems. In: International conference in computers and information technology

  • Schröder G, Thiele M, Lehner W (2011) Setting goals and choosing metrics for recommender system evaluations. In: Workshop of the fifth ACM conference on recommender systems

  • Shani G, Gunawardana A (2009) Evaluating recommender systems. Tech. Rep. MSR-TR-2009-159

  • Sharma V, Karla P, Kumar A (2013) Customer perception assessment technique kansei engineering: a review. Int J Sci Res 2(7):237–240

    Google Scholar 

  • Shrivastava A, Rajawat S (2014) An implementation of hybrid genetic algorithm for clustering based data for web recommendation system. Int J Comput Sci Eng 2(4):6–11

    Google Scholar 

  • SIGKDD (2000) KDD Cup 2000 data. http://www.sigkdd.org/kddcup/index.php. Accessed 2 Sept 2015

  • SIGKDD (2012) KDD Cup 2012 track 1 data. http://www.kddcup2012.org/c/kddcup2012-track1/data. Accessed 2 Sept 2015

  • Sneha YS, Mahadevan G (2011) A study on clustering techniques in recommender systems. In: International Conference on Computational Techniques and Artificial Intelligence, pp 97–100

  • Takagi H (2001) Interactive evolutionary computation: fusion of the capabilities of ec optimization and human evaluation. Proc IEEE 89(9):1275–1296

    Article  Google Scholar 

  • Tanaka M, Hiroyasu T, Miki M, Sasaki Y, Yoshimi M, Yokouchi H (2010) Automatic generation method to derive for the design variable spaces for interactive genetic algorithms. In: IEEE Congress on Evolutionary Computation, pp 1–8

  • Tanaka M, Miyaji M, Yamamoto U, Hiroyasu T, Miki M (2013) Interactive recommender system to estimate personal user’s kansei model. Int J Comput Sci Eng 5(11):904–913

    Google Scholar 

  • Trelea IC (2003) The particle swarm optimization algorithm: convergence analysis and parameter selection. Inf Process Lett 85(6):317–325

    Article  MathSciNet  MATH  Google Scholar 

  • Tucker L (1966) Some mathematical notes on three-mode factor analysis. Psychometrika 31(3):279–311

    Article  MathSciNet  Google Scholar 

  • Ujjin S, Bentley P (2002) Learning user preferences using evolution. In: Asia-Pacific Conference on Simulated Evolution and Learning, Singapore

  • Vargas S, Castells P (2011) Rank and relevance in novelty and diversity metrics for recommender systems. In: ACM conference on recommender systems. ACM, pp 109–116

  • Vargas-Govea B, Serna JGG, Medellãn RP (2012) Restaurant and consumer data. https://archive.ics.uci.edu/ml/datasets/Restaurant+%26+consumer+data. Accessed 2 Sept 2015

  • Velez-Langs O, De Antonio A (2014) Learning users characteristics in collaborative filtering through genetic algorithms: some new results. In: Advance trends in soft computing, studies in fuzziness and soft computing, vol 312. Springer International Publishing, pp 309–326

  • Venturini V, Carb J, Molina JM (2008) Learning user profile with genetic algorithm in ami applications. Hybrid artificial intelligence systems, vol 5271. Lecture Notes in Computer ScienceSpringer, Berlin Heidelberg, pp 124–131

  • Verma A, Virk HK (2015) A hybrid genre-based recommender system for movies using genetic algorithm and knn approach. Int J Innov Eng Technol 5(4):48–55

    Google Scholar 

  • Wang Y, Wang L, Li Y, He D, Liu TY (2013) A theoretical analysis of NDCG type ranking measures. The 26th annual conference on learning theory, 2013. Princeton University, NJ, USA, pp 25–54

  • Wang S, Gong M, Ma L, Cai Q, Jiao L (2014a) Decomposition based multiobjective evolutionary algorithm for collaborative filtering recommender systems. In: IEEE Congress on evolutionary computation, pp 672–679

  • Wang Z, Yu X, Feng N, Wang Z (2014b) An improved collaborative movie recommendation system using computational intelligence. J Vis Lang Comput 25(6):667–675

    Article  Google Scholar 

  • Weimer M, Karatzoglou A, Le QV, Smola AJ (2007) Cofi rank—maximum margin matrix factorization for collaborative ranking. Advances in neural information processing systems 20. In: Proceedings of the 21th annual conference on neural information processing systems. Vancouver, British Columbia, Canada, pp 1593–1600

  • Xiao J, Luo M, Chen JM, Li JJ (2015) An item based collaborative filtering system combined with genetic algorithms using rating behavior. Lecture Notes in Computer Science, vol 9227, Springer International Publishing, pp 453–460

  • Xu JA, Araki K (2006) A svm-based personal recommendation system for tv programs. In: Multi-media modelling conference proceedings, 2006 12th International, p 4

  • Yang XS, Cui Z, Xiao R, Gandomi AH, Karamanoglu M (2013) Swarm intelligence and bio-inspired computation: theory and applications, 1st edn. Elsevier, Waltham

    Google Scholar 

  • Ye M, Yin P, Lee WC, Lee DL (2011) Exploiting geographical influence for collaborative point-of-interest recommendation. In: Proceedings of the 34th International ACM SIGIR conference on research and development in information retrieval. ACM, pp 325–334

  • Yin H, Cui B, Li J, Yao J, Chen C (2012) Challenging the long tail recommendation. Proc VLDB Endow 5(9):896–907

    Article  Google Scholar 

  • Yoshii K, Goto M, Komatani K, Ogata T, Okuno HG (2008) An efficient hybrid music recommender system using an incrementally trainable probabilistic generative model. IEEE Trans Audio Speech Lang Process 16(2):435–447

    Article  Google Scholar 

  • Yuan Q, Cong G, Ma Z, Sun A, Thalmann NM (2013) Time-aware point-of-interest recommendation. In: Proceedings of the 36th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 363–372

  • Zhang F, Chang HY (2006) A collaborative filtering algorithm employing genetic clustering to ameliorate the scalability issue. In: IEEE International conference on e-Business engineering, pp 331–338

  • Zhang M, Hurley N (2008) Avoiding monotony: improving the diversity of recommendation lists. In: ACM conference on recommender systems. ACM, pp 123–130

  • Zhou T, Ren J, Medo M, Zhang YC (2007) Bipartite network projection and personal recommendation. Phys Rev E Stat Nonlin Soft Matter Phys 76(4):046115

    Article  Google Scholar 

  • Zhou A, Qu BY, Li H, Zhao SZ, Suganthan PN, Zhang Q (2011) Multiobjective evolutionary algorithms: a survey of the state of the art. Swarm Evol Comput 1(1):32–49

    Article  Google Scholar 

  • Zuo Y, Gong M, Zeng J, Ma L, Jiao L (2015) Personalized recommendation based on evolutionary multi-objective optimization. IEEE Comput Intel Mag Res Front 10(1):52–62

    Article  Google Scholar 

Download references

Acknowledgments

This review was supported by the Brazilian research funding agencies CAPES, CNPq and FAPESP and the project VEGA 1/0475/14 granted by the Scientific Grant Agency of the Ministry of Education of Slovak Republic and the Slovak Academy of Sciences.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tomáš Horváth.

Additional information

This work was done under the project entitled “Utilizing Nature Inspired Computation Techniques in Recommender Systems” financed by the program PNPD/CAPES.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Horváth, T., de Carvalho, A.C.P.L.F. Evolutionary computing in recommender systems: a review of recent research. Nat Comput 16, 441–462 (2017). https://doi.org/10.1007/s11047-016-9540-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11047-016-9540-y

Keywords

Navigation