Cited By
View all- Behera GNain NSoni R(2024)Integrating user-side information into matrix factorization to address data sparsity of collaborative filteringMultimedia Systems10.1007/s00530-024-01261-830:2Online publication date: 18-Feb-2024
The State-of-the-Art and Challenges on Recommendation System’s: Principle, Techniques and Evaluation Strategy
Abstract
In this digital era, users and service providers are facing various decisions that prompt data over-burden. The choices should be separated and focused on or altered so that the actual data is passed with significant subtleties to the service provider or to the intended user. A recommender framework or engine handles the information overload problem by customizing and filtering the large volume of data and generating the customer’s appropriate information dynamically with personalized content. This comprehensive study focuses on several recommender systems (RecSys) methodologies and discusses the problems or issues associated with different principles and techniques. In addition to the various principles and techniques, this study elaborates on several similarity measures, including conventional and non-conventional measures, with their merits and demerits also points out both ranking and non-ranking performance metrics. Further, we have studied different articles, including journals and conferences. Based on the studies, we outline current research challenges as future directions. We have briefly discussed various datasets utilized in the recommender domain for evaluating and validating the recommendation task.
References
[1]
The EachMovie dataset HP/Compaq research. https://grouplens.org/datasets/eachmovie/. Accessed 30 July 2021
[2]
Acilar AM and Arslan A A collaborative filtering method based on artificial immune network Expert Syst Appl 2009 36 4 8324-8332
[3]
Aditya P, Budi I, Munajat Q. A comparative analysis of memory-based and model-based collaborative filtering on the implementation of recommender system for e-commerce in Indonesia: a case study Pt X. In: 2016 International conference on advanced computer science and information systems (ICACSIS). IEEE; 2016. pp. 303–308.
[4]
Adomavicius G and Tuzhilin A Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions IEEE Trans Knowl Data Eng 2005 6 734-749
[5]
Adomavicius G and Zhang J Impact of data characteristics on recommender systems performance ACM Trans Manag Inf Syst (TMIS) 2012 3 1 3
[6]
Ahmed M, Imtiaz MT, Khan R. Movie recommendation system using clustering and pattern recognition network. In: 2018 IEEE 8th annual computing and communication workshop and conference (CCWC). IEEE; 2018. pp. 143–147.
[7]
Ahn HJ A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem Inf Sci 2008 178 1 37-51
[8]
Al Alshaikh M, Uchyigit G, Evans R. A research paper recommender system using a dynamic normalized tree of concepts model for user modelling. In: 2017 11th International conference on research challenges in information science (RCIS). IEEE; 2017. pp. 200–210.
[9]
Al-Shamri MYH and Bharadwaj KK Fuzzy-genetic approach to recommender systems based on a novel hybrid user model Expert Syst Appl. 2008 35 3 1386-1399
[10]
Alam MH, Ryu WJ, and Lee S Joint multi-grain topic sentiment: modeling semantic aspects for online reviews Inf Sci 2016 339 206-223
[11]
Aljunid MF and Dh M An efficient deep learning approach for collaborative filtering recommender system Procedia Comput Sci 2020 171 829-836
[12]
Anandhan A, Shuib L, Ismail MA, and Mujtaba G Social media recommender systems: review and open research issues IEEE Access 2018 6 15608-15628
[13]
Barragáns-Martínez AB, Costa-Montenegro E, Burguillo JC, Rey-López M, Mikic-Fonte FA, and Peleteiro A A hybrid content-based and item-based collaborative filtering approach to recommend tv programs enhanced with singular value decomposition Inf Sci 2010 180 22 4290-4311
[14]
Basu C, Hirsh H, Cohen W. et al.: Recommendation as classification: using social and content-based information in recommendation. In: AAAI/IAAI. 1998. pp. 714–720.
[15]
Basudkar B, Bagayatkar S, Chopade M, Darekar S, Student B. Restaurant recommendation system using customer’s data analysis. 2018. Corpus ID 21250386.
[16]
Behera G, Nain N. A comparative study of big mart sales prediction. In: International conference on computer vision and image processing. Springer; 2019. pp. 421–432.
[17]
Behera G, Nain N. Grid search optimization (GSO) based future sales prediction for big mart. In: 2019 15th International conference on signal-image technology & internet-based systems (SITIS). IEEE; 2019. pp. 172–178.
[18]
Behera G, Nain N. Collaborative recommender system (CRS) using optimized SGD-ALS. In: Advances in computing and data sciences: 5th international conference, ICACDS 2021, Nashik, India, April 23–24, 2021. Revised selected papers, part I 5. Springer; 2021. pp. 627–637.
[19]
Behera G and Nain N DeepNNMF: deep nonlinear non-negative matrix factorization to address sparsity problem of collaborative recommender system Int J Inf Technol 2022 14 7 3637-3645
[20]
Behera G and Nain N GSO-CRS: grid search optimization for collaborative recommendation system Sādhanā 2022 47 3 158
[21]
Behera G and Nain N Handling data sparsity via item metadata embedding into deep collaborative recommender system J King Saud Univ Comput Inf Sci 2022 34 10 9953-9963
[22]
Behera G, Nain N. Trade-off between memory and model-based collaborative filtering recommender system. In: Proceedings of the international conference on paradigms of communication, computing and data sciences: PCCDS 2021. Springer; 2022. pp. 137–146.
[23]
Behera G and Nain N Collaborative filtering with temporal features for movie recommendation system Procedia Comput Sci 2023 218 1366-1373
[24]
Berry MJ and Linoff GS Data mining techniques: for marketing, sales, and customer relationship management 2004 Hoboken Wiley
[25]
Bertin-Mahieux T, Ellis DP, Whitman B, Lamere P. The million song dataset. In: Proceedings of the 12th international conference on music information retrieval (ISMIR 2011). 2011.
[26]
Beutel A, Murray K, Faloutsos C, Smola AJ. Cobafi: collaborative Bayesian filtering. In: Proceedings of the 23rd international conference on world wide web. 2014. pp. 97–108.
[27]
Billsus D, Pazzani MJ. A hybrid user model for news story classification. In: UM99 user modeling. Springer; 1999. pp. 99–108.
[28]
Bishop CM Pattern recognition and machine learning 2006 New York Springer
[29]
Bobadilla J, Ortega F, Hernando A, and Gutiérrez A Recommender systems survey Knowl Based Syst 2013 46 109-132
[30]
Bobadilla J, Serradilla F, and Bernal J A new collaborative filtering metric that improves the behavior of recommender systems Knowl Based Syst 2010 23 6 520-528
[31]
Bobadilla J, Serradilla F, Hernando A, et al. Collaborative filtering adapted to recommender systems of e-learning Knowl Based Syst 2009 22 4 261-265
[32]
Breese JS, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the fourteenth conference on uncertainty in artificial intelligence. Morgan Kaufmann Publishers Inc.; 1998. pp. 43–52.
[33]
Buder J and Schwind C Learning with personalized recommender systems: a psychological view Comput Hum Behav 2012 28 1 207-216
[34]
Buettner R Predicting user behavior in electronic markets based on personality-mining in large online social networks: a personality-based product recommender framework Electron Market 2017 27 247-265
[35]
Burke R Knowledge-based recommender systems Encycl Lib Inf Syst 2000 69 Supplement 32 175-186
[36]
Burke R Hybrid recommender systems: survey and experiments User Model User Adapt Interact 2002 12 4 331-370
[37]
Burke RD, Hammond KJ, and Yound B The FindMe approach to assisted browsing IEEE Expert 1997 12 4 32-40
[38]
Candès EJ and Recht B Exact matrix completion via convex optimization Found Comput Math 2009 9 6 717
[39]
Candillier L, Meyer F, Boullé M. Comparing state-of-the-art collaborative filtering systems. In: International workshop on machine learning and data mining in pattern recognition. Springer; 2007. pp. 548–562.
[40]
Carrer-Neto W, Hernández-Alcaraz ML, Valencia-García R, and García-Sánchez F Social knowledge-based recommender system. Application to the movies domain Expert Syst Appl 2012 39 12 10990-11000
[41]
Caruana R, Niculescu-Mizil A. An empirical comparison of supervised learning algorithms. In: Proceedings of the 23rd international conference on machine learning. ACM; 2006. pp. 161–168.
[42]
Castro-Schez JJ, Miguel R, Vallejo D, and López-López LM A highly adaptive recommender system based on fuzzy logic for B2C e-commerce portals Expert Syst Appl 2011 38 3 2441-2454
[43]
Celma Ò and Serra X Foafing the music: bridging the semantic gap in music recommendation Web Semant Sci Serv Agents World Wide Web 2008 6 4 250-256
[44]
Chamoso P, Rivas A, Rodríguez S, and Bajo J Relationship recommender system in a business and employment-oriented social network Inf Sci 2018 433 204-220
[45]
Chang N, Irvan M, and Terano T A TV program recommender framework Procedia Comput Sci 2013 22 561-570
[46]
Chekima K, On CK, Alfred R, and Anthony P Document recommender agent based on hybrid approach Int J Mach Learn Comput 2014 4 2 151-156
[47]
Chen LS, Hsu FH, Chen MC, and Hsu YC Developing recommender systems with the consideration of product profitability for sellers Inf Sci 2008 178 4 1032-1048
[48]
Chu WT and Tsai YL A hybrid recommendation system considering visual information for predicting favorite restaurants World Wide Web 2017 20 1313-1331
[49]
Claypool M, Gokhale A, Miranda T, Murnikov P, Netes D, Sartin M. Combing content-based and collaborative filters in an online newspaper. 1999.
[50]
Condliff MK, Lewis DD, Madigan D, Posse C. Bayesian mixed-effects models for recommender systems. In: ACM SIGIR, vol. 99. Citeseer; 1999. pp. 23–30.
[51]
Costa-Montenegro E, Barragáns-Martínez AB, and Rey-López M Which app? A recommender system of applications in markets: implementation of the service for monitoring users’s interaction Expert Syst Appl 2012 39 10 9367-9375
[52]
Cremonesi P, Modica P, Pagano R, Rabosio E, Tanca L. Personalized and context-aware tv program recommendations based on implicit feedback. In: E-commerce and web technologies: 16th international conference on electronic commerce and web technologies, EC-web 2015, Valencia, Spain, September 2015. Revised selected papers 16. Springer; 2015. pp. 57–68.
[53]
Crespo RG, Martínez OS, Lovelle JMC, García-Bustelo BCP, Gayo JEL, and De Pablos PO Recommendation system based on user interaction data applied to intelligent electronic books Comput Hum Behav 2011 27 4 1445-1449
[54]
Cunningham P, Bergmann R, Schmitt S, Traphöner R, Breen S, and Smyth B WEBSELL: intelligent sales assistants for the world wide web KI 2001 15 1 28-32
[55]
Deldjoo Y, Elahi M, Cremonesi P, Garzotto F, Piazzolla P, and Quadrana M Content-based video recommendation system based on stylistic visual features J Data Semant 2016 5 99-113
[56]
Drosou M and Pitoura E Search result diversification SIGMOD Rec 2010 39 1 41-47
[57]
Duda RO, Hart PE, and Stork DG Pattern classification 2012 Hoboken Wiley
[58]
Fessahaye F, Perez L, Zhan T, Zhang R, Fossier C, Markarian R, Chiu C, Zhan J, Gewali L, Oh P. T-RECSYS: a novel music recommendation system using deep learning. In: 2019 IEEE international conference on consumer electronics (ICCE). IEEE; 2019. pp. 1–6.
[59]
Ghazanfar MA, Prugel-Bennett A. A scalable, accurate hybrid recommender system. In: 2010 Third international conference on knowledge discovery and data mining. IEEE; 2010. pp. 94–98.
[60]
Göksedef M and Gündüz-Öğüdücü Ş Combination of web page recommender systems Expert Syst Appl 2010 37 4 2911-2922
[61]
Goldberg K, Roeder T, Gupta D, and Perkins C Eigentaste: a constant time collaborative filtering algorithm Inf Retr. 2001 4 2 133-151
[62]
Gomez-Uribe CA and Hunt N The Netflix recommender system: algorithms, business value, and innovation ACM Trans Manag Inf Syst (TMIS) 2016 6 4 13
[63]
Gong S A flexible electronic commerce recommendation system Phys Procedia 2012 24 806-811
[64]
Guo G, Zhang J, Yorke-Smith N. A novel Bayesian similarity measure for recommender systems. In: Proceedings of the 23rd international joint conference on artificial intelligence (IJCAI). 2013. pp. 2619–2625.
[65]
Harper FM and Konstan JA The MovieLens datasets: history and context ACM Trans Interact Intell Syst (TIIS) 2015 5 4 1-19
[66]
He X, Liao L, Zhang H, Nie L, Hu X, Chua TS. Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide web. 2017. pp. 173–182.
[67]
Herlocker JL, Konstan JA, Borchers A, Riedl J. An algorithmic framework for performing collaborative filtering. In: ACM SIGIR forum, vol. 51. New York, NY, USA: ACM; 2017. pp. 227–234.
[68]
Herlocker JL, Konstan JA, Terveen LG, and Riedl JT Evaluating collaborative filtering recommender systems ACM Trans Inf Syst (TOIS) 2004 22 1 5-53
[69]
Hosseini-Pozveh M, Nematbakhsh M, Movahhedinia N. A multidimensional approach for context-aware recommendation in mobile commerce. 2009. arXiv preprint arXiv:0908.0982.
[70]
Hu Y, Koren Y, Volinsky C. Collaborative filtering for implicit feedback datasets. In: 2008 Eighth IEEE international conference on data mining. IEE. 2008. pp. 263–272.
[71]
Huang Z, Zeng D, and Chen H A comparison of collaborative-filtering recommendation algorithms for e-commerce IEEE Intell Syst 2007 22 5 68-78
[72]
Hurley N and Zhang M Novelty and diversity in top-N recommendation-analysis and evaluation ACM Trans Internet Technol (TOIT) 2011 10 4 1-30
[73]
Isinkaye F, Folajimi Y, and Ojokoh B Recommendation systems: principles, methods and evaluation Egypt Inform J 2015 16 3 261-273
[74]
Jalali M, Mustapha N, Sulaiman MN, and Mamat A WebPUM: a web-based recommendation system to predict user future movements Expert Syst Appl 2010 37 9 6201-6212
[75]
Jannach D, Zanker M, Felfernig A, and Friedrich G Recommender systems: an introduction 2010 Cambridge Cambridge University Press
[76]
Jennings A and Higuchi H A personal news service based on a user model neural network IEICE Trans Inf Syst 1992 75 2 198-209
[77]
Kaššák O, Kompan M, and Bieliková M Personalized hybrid recommendation for group of users: top-N multimedia recommender Inf Process Manag 2016 52 3 459-477
[78]
Katarya R Movie recommender system with metaheuristic artificial bee Neural Comput Appl 2018 30 6 1983-1990
[79]
Katarya R and Verma OP Efficient music recommender system using context graph and particle swarm Multimedia Tools Appl 2018 77 2673-2687
[80]
Khusro S, Ali Z, Ullah I. Recommender systems: issues, challenges, and research opportunities. In: Information science and applications (ICISA) 2016. Springer; 2016., pp. 1179–1189.
[81]
Konstan JA and Riedl J Recommender systems: from algorithms to user experience User Model User Adapt Interact 2012 22 1–2 101-123
[82]
Konstas I, Stathopoulos V, Jose JM. On social networks and collaborative recommendation. In: Proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval. ACM; 2009. pp. 195–202.
[83]
Koren Y, Bell R, and Volinsky C Matrix factorization techniques for recommender systems Computer 2009 8 30-37
[84]
Krishnappa DK, Zink M, Griwodz C, Halvorsen P (2015) Cache-centric video recommendation: an approach to improve the efficiency of YouTube caches. ACM Trans Multimedia Comput Commun Appl (TOMM) 11(4), 1–20
[85]
Kumar V, Pujari AK, Sahu SK, Kagita VR, and Padmanabhan V Collaborative filtering using multiple binary maximum margin matrix factorizations Inf Sci 2017 380 1-11
[86]
Kużelewska, U. Advantages of information granulation in clustering algorithms. In: International conference on agents and artificial intelligence. Springer; 2011. pp. 131–145.
[87]
Lam S, Frankowski D, Riedl J. Do you trust your recommendations? An exploration of security and privacy issues in recommender systems. In: Emerging trends in information and communication security. 2006. pp. 14–29.
[88]
Lang K. NewsWeeder: learning to filter netnews. In: Machine learning proceedings, 1995. Elsevier; 1995. pp. 331–339.
[89]
Lee DH, Brusilovsky P. Social networks and interest similarity: the case of Citeulike. In: Proceedings of the 21st ACM conference on hypertext and hypermedia. ACM; 2010. pp. 151–156.
[90]
Lee SK, Cho YH, and Kim SH Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations Inf Sci 2010 180 11 2142-2155
[91]
Li J, Xu W, Wan W, and Sun J Movie recommendation based on bridging movie feature and user interest J Comput Sci 2018 26 128-134
[92]
Li Y, Wang H, Liu H, Chen B. A study on content-based video recommendation. In: 2017 IEEE International Conference on Image Processing (ICIP), pp. 4581–4585. IEEE 2017;
[93]
Lian J, Zhou X, Zhang F, Chen Z, Xie X, Sun G. XDEEPFM: combining explicit and implicit feature interactions for recommender systems. In: Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining. 2018. pp. 1754–1763.
[94]
Lieberman H et al. Letizia: an agent that assists web browsing IJCAI 1995 1 1995 924-929
[95]
Lu J, Wu D, Mao M, Wang W, and Zhang G Recommender system application developments: a survey Decis Support Syst 2015 74 12-32
[96]
Luo X, Xia Y, and Zhu Q Incremental collaborative filtering recommender based on regularized matrix factorization Knowl Based Syst 2012 27 271-280
[97]
Luo X, Xia Y, and Zhu Q Applying the learning rate adaptation to the matrix factorization based collaborative filtering Knowl Based Syst 2013 37 154-164
[98]
Markov Z and Larose DT Data mining the web: uncovering patterns in web content, structure, and usage 2007 Hoboken Wiley
[99]
McNally K, O’Mahony MP, Coyle M, Briggs P, and Smyth B A case study of collaboration and reputation in social web search ACM Trans Intell Syst Technol (TIST) 2011 3 1 1-29
[100]
Melville P, Mooney RJ, and Nagarajan R Content-boosted collaborative filtering for improved recommendations AAAI/IAAI 2002 23 187-192
[101]
Mican D, Tomai N. Association-rules-based recommender system for personalization in adaptive web-based applications. In: International conference on web engineering. Springer; 2010. pp. 85–90.
[102]
Min SH and Han I Detection of the customer time-variant pattern for improving recommender systems Expert Syst Appl 2005 28 2 189-199
[103]
Mishra R, Kumar P, and Bhasker B A web recommendation system considering sequential information Decis Support Syst 2015 75 1-10
[104]
Mobasher B, Jin X, Zhou Y. Semantically enhanced collaborative filtering on the web. In: European Web Mining Forum. Springer; 2003. pp. 57–76.
[105]
Mooney RJ, Roy L. Content-based book recommending using learning for text categorization. In: Proceedings of the fifth ACM conference on Digital libraries. ACM; 2000. pp. 195–204.
[106]
Mustaqeem A, Anwar SM, and Majid M A modular cluster based collaborative recommender system for cardiac patients Artif Intell Med 2020 102
[107]
Nanopoulos A, Rafailidis D, Symeonidis P, and Manolopoulos Y MusicBox: personalized music recommendation based on cubic analysis of social tags IEEE Trans Audio Speech Lang Process 2009 18 2 407-412
[108]
Nehring K and Puppe C A theory of diversity Econometrica 2002 70 3 1155-1198
[109]
Nguyen L. A new approach for collaborative filtering based on Bayesian network inference. In: 2015 7th International joint conference on knowledge discovery, knowledge engineering and knowledge management (IC3K), vol. 1. IEEE; 2015. pp. 475–480.
[110]
Nilashi M, bin Ibrahim O, Ithnin N, and Sarmin NH A multi-criteria collaborative filtering recommender system for the tourism domain using expectation maximization (EM) and PCA-ANFIS Electron Commer Res Appl 2015 14 6 542-62
[111]
Núñez-Valdéz ER, Lovelle JMC, Martínez OS, García-Díaz V, De Pablos PO, and Marín CEM Implicit feedback techniques on recommender systems applied to electronic books Comput Hum Behav 2012 28 4 1186-1193
[112]
Oard DW, Kim J, et al. Implicit feedback for recommender systems. In: Proceedings of the AAAI workshop on recommender systems, vol. 83. Wollongong; 1998.
[113]
Pan C, Li W. Research paper recommendation with topic analysis. In: 2010 International conference on computer design and applications, vol. 4. IEEE; 2010. pp. V4–264.
[114]
Papagelis M and Plexousakis D Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents Eng Appl Artif Intell 2005 18 7 781-789
[115]
Pareek J, Jhaveri M, Kapasi A, Trivedi M. SNetRS: social networking in recommendation system. In: Advances in computing and information technology. Springer; 2013. pp. 195–206.
[116]
Park DH, Kim HK, Choi IY, and Kim JK A literature review and classification of recommender systems research Expert Syst Appl 2012 39 11 10059-10072
[117]
Park MH, Hong JH, Cho SB. Location-based recommendation system using Bayesian user’s preference model in mobile devices. In: International conference on ubiquitous intelligence and computing. Springer; 2007. pp. 1130–1139.
[118]
Pathak B, Garfinkel R, Gopal RD, Venkatesan R, and Yin F Empirical analysis of the impact of recommender systems on sales J Manag Inf Syst 2010 27 2 159-188
[119]
Patra BK, Launonen R, Ollikainen V, and Nandi S A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data Knowl Based Syst 2015 82 163-177
[120]
Pazzani MJ A framework for collaborative, content-based and demographic filtering Artif Intell Rev 1999 13 5–6 393-408
[121]
Porcel C and Herrera-Viedma E Dealing with incomplete information in a fuzzy linguistic recommender system to disseminate information in university digital libraries Knowl Based Syst 2010 23 1 32-39
[122]
Porcel C, Moreno JM, and Herrera-Viedma E A multi-disciplinar recommender system to advice research resources in university digital libraries Expert Syst Appl 2009 36 10 12520-12528
[123]
Porcel C, Tejeda-Lorente A, Martínez M, and Herrera-Viedma E A hybrid recommender system for the selective dissemination of research resources in a technology transfer office Inf Sci 2012 184 1 1-19
[124]
Pu P, Chen L, Hu R. A user-centric evaluation framework for recommender systems. In: Proceedings of the fifth ACM conference on Recommender systems. ACM; 2011. pp. 157–164.
[125]
Pujahari A and Sisodia DS Pair-wise preference relation based probabilistic matrix factorization for collaborative filtering in recommender system Knowl Based Syst 2020 196
[126]
Rajarajeswari S, Naik S, Srikant S, Sai Prakash M, Uday P. Movie recommendation system. In: Emerging Research in Computing, information, communication and applications: ERCICA 2018, vol. 1. Springer; 2019. pp. 329–340.
[127]
Rashid AM, Albert I, Cosley D, Lam SK, McNee SM, Konstan JA, Riedl J. Getting to know you: learning new user preferences in recommender systems. In: Proceedings of the 7th international conference on intelligent user interfaces. ACM; 2002. pp. 127–134.
[128]
Ricci F, Rokach L, and Shapira B Recommender systems: introduction and challenges Recommender systems handbook 2015 New York Springer 1-34
[129]
Salter J and Antonopoulos N CinemaScreen recommender agent: combining collaborative and content-based filtering IEEE Intell Syst 2006 21 1 35-41
[130]
Sarwar BM, Karypis G, Konstan JA, Riedl J, et al. Item-based collaborative filtering recommendation algorithms WWW 2001 1 285-295
[131]
Sarwar BM, Konstan JA, Borchers A, Herlocker J, Miller B, Riedl J. Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system. In: The GroupLens research collaborative filtering system. Proceedings of the ACM conference on computer supported cooperative work (CSCW). 1998.
[132]
Schafer JB, Frankowski D, Herlocker J, and Sen S Collaborative filtering recommender systems The adaptive web 2007 Heidelberg Springer 291-324
[133]
Schwab I, Kobsa A, Koychev I. Learning user interests through positive examples using content analysis and collaborative filtering. Internal Memo, GMD, St. Augustin, Germany. 2001.
[134]
Serrano-Guerrero J, Herrera-Viedma E, Olivas JA, Cerezo A, and Romero FP A google wave-based fuzzy recommender system to disseminate information in university digital libraries 2.0 Inf Sci 2011 181 9 1503-1516
[135]
Shambour Q and Lu J A trust-semantic fusion-based recommendation approach for e-business applications Decis Support Syst 2012 54 1 768-780
[136]
Shani G and Gunawardana A Evaluating recommendation systems Recommender systems handbook 2011 Boston Springer 257-297
[137]
Sivapalan S, Sadeghian A, Rahnama H, Madni AM. Recommender systems in e-commerce. In: 2014 world automation congress (WAC). IEEE; 2014. pp. 179–184.
[138]
Smyth B and Cotter P A personalised TV listings service for the digital TV age Knowl Based Syst 2000 13 2–3 53-59
[139]
Stern DH, Herbrich R, Graepel T. Matchbox: large scale online Bayesian recommendations. In: Proceedings of the 18th international conference on World wide web. ACM; 2009. pp. 111–120.
[140]
Su X and Khoshgoftaar TM A survey of collaborative filtering techniques Adv Artif Intell 2009 2009 421425
[141]
Sun X, Kong F, Ye S. A comparison of several algorithms for collaborative filtering in startup stage. In: Proceedings. 2005 IEEE networking, sensing and control. IEEE; 2005. pp. 25–28.
[142]
Takács G, Pilászy I, Németh B, Tikk D. Investigation of various matrix factorization methods for large recommender systems. In: 2008 IEEE international conference on data mining workshops. IEEE; 2008. pp. 553–562.
[143]
Tan S, Bu J, Chen C, Xu B, Wang C, and He X Using rich social media information for music recommendation via hypergraph model ACM Trans Multimedia Comput Commun Appl (TOMM) 2011 7 1 1-22
[144]
Tewari AS, Kumar A, Barman AG. Book recommendation system based on combine features of content based filtering, collaborative filtering and association rule mining. In: 2014 IEEE international advance computing conference (IACC). IEEE; 2014. pp. 500–503.
[145]
Valcarce D, Landin A, Parapar J, and Barreiro Á Collaborative filtering embeddings for memory-based recommender systems Eng Appl Artif Intell 2019 85 347-356
[146]
Van Meteren R, Van Someren M. Using content-based filtering for recommendation. In: Proceedings of the machine learning in the new information age: MLnet/ECML2000 workshop, vol. 30. 2000. pp. 47–56.
[147]
Vargas S, Castells P. Rank and relevance in novelty and diversity metrics for recommender systems. In: Proceedings of the fifth ACM conference on recommender systems. 2011. pp. 109–116.
[148]
Vozalis MG and Margaritis KG Using SVD and demographic data for the enhancement of generalized collaborative filtering Inf Sci 2007 177 15 3017-3037
[149]
Wang H, Zhang F, Wang J, Zhao M, Li W, Xie X, Guo M. RippleNet: propagating user preferences on the knowledge graph for recommender systems. In: Proceedings of the 27th ACM international conference on information and knowledge management. 2018. pp. 417–426.
[150]
Wasfi AMA. Collecting user access patterns for building user profiles and collaborative filtering. In: IUI, vol. 99. Citeseer; 1999. pp. 57–64.
[151]
Wei S, Zheng X, Chen D, and Chen C A hybrid approach for movie recommendation via tags and ratings Electron Commer Res Appl 2016 18 83-94
[152]
Winoto P and Tang TY The role of user mood in movie recommendations Expert Syst Appl 2010 37 8 6086-6092
[153]
Yu Z, Zhou X, Hao Y, and Gu J TV program recommendation for multiple viewers based on user profile merging User Model User Aadapt Interact 2006 16 1 63-82
[154]
Zaíane OR. Building a recommender agent for e-learning systems. In: International conference on computers in education, 2002. Proceedings. IEEE; 2002. pp. 55–59.
[155]
Zeng X, Wu B, Shi J, Liu C, Guo Q. Parallelization of latent group model for group recommendation algorithm. In: 2016 IEEE first international conference on data science in cyberspace (DSC). IEEE; 2016. pp. 80–89.
[156]
Zhang S, Wang W, Ford J, Makedon F, Pearlman J. Using singular value decomposition approximation for collaborative filtering. In: Seventh IEEE international conference on e-commerce technology (CEC’05). IEEE; 2005. pp. 257–264.
[157]
Zhang T and Iyengar VS Recommender systems using linear classifiers J Mach Learn Res 2002 2 Feb 313-334
[158]
Zhao ZD, Shang MS. User-based collaborative-filtering recommendation algorithms on Hadoop. In: 2010 Third international conference on knowledge discovery and data mining. IEEE; 2010. pp. 478–481.
[159]
Zhu X, Ye H, Gong S. A personalized recommendation system combining case-based reasoning and user-based collaborative filtering. In: 2009 Chinese control and decision conference. IEEE; 2009. pp. 4026–4028.
[160]
Ziegler CN, Lausen G, Schmidt-Thieme L. Taxonomy-driven computation of product recommendations. In: Proceedings of the thirteenth ACM international conference on information and knowledge management. ACM; 2004. pp. 406–415.
[161]
Ziegler CN, McNee SM, Konstan JA, Lausen G. Improving recommendation lists through topic diversification. In: Proceedings of the 14th international conference on world wide web. ACM; 2005. pp. 22–32.
Recommendations
Parallel Ratio Based CF for Recommendation System
ICCCNT '16: Proceedings of the 7th International Conference on Computing Communication and Networking TechnologiesWith the increase in E-commerce, Recommendation Systems are getting popular to provide recommendations of various items (movies, books, music) to users. To build the Recommendation System (RS), Collaborative Filtering (CF) techniques are proven ...
An effective recommendation method for cold start new users using trust and distrust networks
Recommendation systems analyze the purchasing behavior (e.g., item ratings) of users to learn about their preferences and recommend products or services that may be of interest to them. However, as new users require time to become familiar with ...
Finding group shilling in recommendation system
WWW '05: Special interest tracks and posters of the 14th international conference on World Wide WebIn the age of information explosion, recommendation system has been proved effective to cope with information overload in e-commerce area. However, unscrupulous producers shill the systems in many ways to make profit, and it makes the system imprecise ...
Comments
Information & Contributors
Information
Published In
© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
Publisher
Springer-Verlag
Berlin, Heidelberg
Publication History
Published: 03 September 2023
Accepted: 03 August 2023
Received: 25 August 2021
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- View Citations1Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 30 Aug 2024
Other Metrics
Citations
Cited By
View all- Behera GNain NSoni R(2024)Integrating user-side information into matrix factorization to address data sparsity of collaborative filteringMultimedia Systems10.1007/s00530-024-01261-830:2Online publication date: 18-Feb-2024
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in