Recommendation systems (RS) play a crucial role in assisting individuals in making suitable selections from an extensive array of products or services. This significantly mitigates the predicament of being overwhelmed by excessive information. RS finds powerful utility in online industries by vending products over the internet or furnishing online services. Given the potential for business expansion through their implementation, RS is relevant in such domains. This comprehensive review article overviews RS and its diverse variations and extensions. Specifically, this review provides a thorough comparative analysis for each method that encompasses many techniques employed in RS, encompassing content-based filtering, collaborative filtering, hybrid, and miscellaneous approaches. Notably, the article delves into the manifold applications of RS across various practical domains. Additionally, the assortment of evaluation metrics utilized across RS is explored. Finally, we conclude by encapsulating the distinct challenges RS encounters, which enhance their precision and dependability.
Abbasi-MoudZ.Vahdat-NejadH. and SadriJ., Tourism recommendation system based on semantic clustering and sentiment analysis.Expert Systems with Applications167 (2021).
2.
AdomaviciusG. and TuzhilinA., Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions, IEEE Transactions on Knowledge and Data Engineering17(6) (2005), 734–749.
3.
AggarwalC.C., Content-based recommender systems. In Recommender Systems, 2016, pp. 139–166.
4.
BerkovskyS. and FreyneJ., Web personalization and recommender systems. In Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015, pp. 2307–2308.
5.
Blanco-FernandezY.L’opez-NoresM.Gil-SollaA.CabrerM.R. and Pazos-AriasJ.J., Exploring synergies between content-based filtering and spreading activation techniques in knowledge-based recommender systems.Information Sciences181(21) (2011), 4823–4846.
6.
BobadillaJ.HernandoA.OrtegaF. and BernalJ., A framework for collaborative filtering recommender systems.Expert Systems with Applications38(12) (2011), 609–14.
7.
BrutM. and SedesF., Ontology-based solution for personalized recommendations in e-learning systems. methodological aspects and evaluation criterias. In 2010 10th IEEE International Conference on Advanced Learning Technologies, IEEE, 2010, pp. 469–471.
8.
BurkeR., Hybrid web recommender systems. In The Adaptive Web, 2007, pp. 377–408.
9.
CamiB.R.HassanpourH. and MashayekhiH., A content-based movie recommender system based on temporal user preferences. In 2017 3rd Iranian Conference on Intelligent Systems and Signal Processing (ICSPIS), IEEE, 2017, pp. 121–125.
10.
CamposL.M.D.Fernandez-LunaJ.M.HueteJ.F. and Rueda-MoralesM.A., Combining content-based and collaborative recommendations: A hybrid approach based on bayesian networks.International Journal of Approximate Reasoning51(7) (2010), 785–799.
11.
CantadorI.Fernández-TobíasI.BerkovskyS. and CremonesiP., Cross-domain recommender systems, Recommender Systems Handbook (2015), pp. 919–959.
12.
ChenL.-S.HsuF.-H.ChenM.-C. and HsuY.-C., Developing recommender systems with the consideration of product profitability for sellers, Information Sciences178(4) (2008), 1032–1048.
13.
ChoJ.KangS.HyunD. and YuH., Unsupervised proxy selection for session-based recommender systems. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2021, pp. 327–336.
14.
de Souza Pereira MoreiraG.JannachD. and da CunhaA.M., On the importance of news content representation in hybrid neural session-based recommender systems, CoRR, abs/1907.07629, 2019.
15.
ElahiM.RicciF. and RubensN., A survey of active learning in collaborative filtering recommender systems, Computer Science Review20 (2016), 29–50.
16.
FangH.GuoG.ZhangD. and ShuY., Deep learning-based sequential recommender systems: Concepts, algorithms, and evaluations, In International Conference on Web Engineering, 2019, pp. 574–577.
17.
GhazanfarM. and Prugel-BennettA., An improved switching hybrid recommender system using naive bayes classifier and collaborative fltering, 2010.
18.
GuanX.LiC.-T. and GuanY., Matrix factorization with rating completion: An enhanced svd model for collaborative filtering recommender systems.IEEE Access27(678) (2017), 668–27.
19.
GunawardanaA. and alC.M., A unified approach to building hybrid recommender systems.RecSys9 (2009), 117–124.
20.
GunawardanaA. and ShaniG., A survey of accuracy evaluation metrics of recommendation tasks.Journal of Machine Learning Research10 (Dec 2009), 2935–2962.
21.
HarperF.M. and KonstanJ.A., The movielens datasets: History and context.Acm Transactions on Interactive Intelligent Systems (tiis)5(4) (2015), 1–19.
22.
HeR. and McAuleyJ., Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering, In proceedings of the 25th international conference on world wide web, 2016, pp. 507–517.
23.
HerlockerJ.L.KonstanJ.A.TerveenL.G. and RiedlJ.T., Evaluating collaborative filtering recommender systems.ACM Transactions on Information Systems (TOIS)22(1) (2004), 5–53.
24.
HernandoA.BobadillaJ.OrtegaF. and GutierrezA., A probabilistic model for recommending to new cold-start non-registered users, Information Sciences376, 2017, 216–232.
25.
HidasiB.KaratzoglouA.BaltrunasL. and TikkD., Session-based recommendations with recurrent neural networks. arXiv preprint, 2015.
26.
IsinkayeF.FolajimiY. and OjokohB., Recommendation systems: Principles, methods, and evaluation, Egyptian Informatics Journal16(3) (2015), 261–273.
27.
JannachD.MobasherB. and BerkovskyS., Research directions in session-based and sequential recommendation.User Modeling and User-Adapted Interaction30(4) (2020), 609–616.
28.
KhanM.M.IbrahimR. and GhaniI., Cross domain recommender systems: a systematic literature eview, ACM Computing Surveys (CSUR)50(3) (2017), 1–34.
29.
KompanM. and BielikovaM., Content-based news recommendation. In International conference on electronic commerce and web technologies, 2010, pp. 61–72.
30.
KoukiP.FakhraeiS.FouldsJ.EirinakiM. and GetoorL., Hyper: A flexible and extensible probabilistic framework for hybrid recommender systems. In Proceedings of the 9th ACM Conference on Recommender Systems, ACM, 2015, pp. 99–106.
31.
LeeS.K.ChoY.H. and KimS.H., Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations, Information Sciences180(11) (2010), 2142–2155.
32.
LekakosG. and CaravelasP., A hybrid approach for movie recommendation.Multimedia Tools and Applications36(1-2) (2008), 55–70.
33.
LiQ.TangX.WangT.YangH. and SongH., Unifying task-oriented knowledge graph learning and recommendation.IEEE Access115(828) 2019, 816–115.
34.
LindenG.SmithB. and YorkJ., Amazon.com recommendations: Item-to-item collaborative filtering.IEEE Internet Computing7(1) (2003), 76–80.
35.
LiuY.LyuC.LiuZ. and CaoJ., Exploring a large-scale multimodal transportation recommendation system, Transportation Research Part C: Emerging Technologies126 (2021).
36.
MontanerM.LopezB. and RosaJ.L.D.L., A taxonomy of recommender agents on the internet, Artificial Intelligence Review19(4) (2003), 285–330.
37.
MooneyR.J. and RoyL., Content-based book recommending using learning for text categorization, In Proceedings of the fifth ACM conference on Digital libraries, ACM, 2000, pp. 195–204.
38.
MotaD.de CarvalhoC.V. and ReisL.P., Otilia – an architecture for the recommendation of teaching-learning techniques supported by an ontological approach, In (2014), 1–7.
NguyenL.V. and JungJ.J., Crowdsourcing platform for collecting cognitive feedbacks from users: A case study on movie recommender system. In Springer Series in Reliability Engineering, Springer International Publishing, 2020, pp. 139–150.
41.
NguyenL.V. and JungJ.J., Sabre: Cross-domain crowdsourcing platform for recommendation services. In International Symposium on Intelligent and Distributed Computing, Springer, 2022, pp. 213–223.
42.
NguyenL.V.JungJ.J. and HwangM., Ourplaces: cross-cultural crowdsourcing platform for location recommendation services, ISPRS International Journal of Geo-Information9(12) (2020), 711.
43.
NguyenL.V.NguyenT.-H. and JungJ.J., Content-based collaborative filtering using word embedding. In Proceedings of the International Conference on Research in Adaptive and Convergent Systems, ACM, Oct 2020, pp. 96–100.
44.
NguyenL.V.VoQ.-T. and NguyenT.-H., Adaptive knn-based extended collaborative filtering recommendation services, Big Data and Cognitive Computing7(2) (2023), 106.
45.
NguyenL.V.VoQ.-T. and PhanT.-T.-H., A survey of recommendation systems: Datasets, evaluation methods, and application domains. In 23rd International Conference on Intelligent Systems Design and Applications, Springer, 2023, pp. 00–00.
46.
PanW.XiangE.W.LiuN.N. and YangQ., Transfer learning in collaborative filtering for sparsity reduction. In Twenty-fourth AAAI conference on artificial intelligence, 2010.
47.
PannielloU.TuzhilinA. and GorgoglioneM., Comparing context-aware recommender systems in terms of accuracy and diversity, User Modeling and User-Adapted Interaction24(1-2) (2014), 35–65.
48.
PazzaniM.J., A framework for collaborative, content-based and demographic fltering, Artifcial Intelligence Review13(5-6) (1999), 393–408.
49.
PengT.WangW.GongX.TianY.YangX. and MaJ., A graph indexing approach for content-based recommendation system. In 2010 Second International Conference on Multimedia and Information Technology, vol. 1. IEEE, 2010, pp. 93–97.
50.
QianF.ZhangY.ZhangY. and DuanZ., Community-based user domain model collaborative recommendation algorithm, TSINGHUA Science and Technology18(4) (2013), 353–359.
QuadranaM.KaratzoglouA.HidasiB. and CremonesiP., Personalizing session-based recommendations with hierarchical recurrent neural networks, In Proceedings of the Eleventh ACM Conference on Recommender Systems, 2017, pp. 130–137.
53.
RicciF.RokachL. and ShapiraB., Introduction to recommender systems handbook. In Recommender Systems Handbook, 2011, pp. 1–35.
54.
RuoccoM.SkredeO.S.L. and LangsethH., Inter-session modeling for a session-based recommendation.In Proceedings of The2 (2017), 24–31.
55.
SaifH.HeY. and AlaniH., Semantic sentiment analysis of twitter. In The Semantic Web–ISWC 2012: 11th International Semantic Web Conference, Boston, MA, USA, November 11–15, 2012, Proceedings, Part I 11, Springer, 2012, pp. 508–524.
56.
SharmaL. and GeraA., A survey of recommendation system: Research challenges.International Journal of Engineering Trends and Technology (IJETT)4(5) (2013), 1989–1992.
57.
SinghP.K.PramanikP.K.D.DeyA.K. and ChoudhuryP., Recommender systems: an overview, research trends, and future directions, International Journal of Business and Systems Research15(1) (2021), 14–52.
58.
SongL.TekinC. and SchaarM.V.D., Online learning in large-scale contextual recommender systems.IEEE Transactions on Services Computing9(3) (2014), 433–445.
59.
SunY.YuanF.YangM.WeiG.ZhaoZ. and LiuD., A generic network compression framework for sequential recommender systems, In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020, pp. 1299–1308.
60.
TahmasebiF.MeghdadiM.AhmadianS. and ValiallahiK., A hybrid recommendation system based on profile expansion technique to alleviate cold start problem, Multimedia Tools and Applications80(2) (2021), 2339–2354.
61.
TanY.K.XuX. and LiuY., Improved recurrent neural networks for session-based recommendations, in Proceedings of the 1st workshop on deep learning for recommender systems, 2016, pp. 17–22.
62.
ThongN.T., et al., Hifcf: An effective hybrid model between picture fuzzy clustering and intuitionistic fuzzy recommender systems for medical diagnosis.Expert Systems with Applications42(7) (2015), 3682–3701.
63.
Vuong NguyenL.NguyenT.-H.JungJ.J. and CamachoD., Extending collaborative filtering recommendation using word embedding: A hybrid approach, Concurrency and Computation: Practice and Experience35(16) (2023), e6232.
64.
WanJ.WangD.HoiS.C.H.WuP.ZhuJ.ZhangY. and LiJ., Deep learning for content-based image retrieval: A comprehensive study. In Proceedings of the 22nd ACM international conference on Multimedia, ACM, 2014, pp. 157–166.
65.
WangS.CaoL.WangY.ShengQ.Z.OrgunM.A. and LianD., A survey on session-based recommender systems, ACM Computing Surveys (CSUR)54(7) (2021), 1–38.
66.
WangS.HuL.WangY.CaoL.ShengQ.Z. and OrgunM., Sequential recommender systems: challenges, progress and prospects. arXiv preprint, 2019.
67.
WangS.HuL.WangY.HeX.ShengQ.Z.OrgunM.CaoL.WangN.RicciF. and YuP.S., Graph learning approaches to recommender systems: A review. arXiv preprint, 2020.
68.
WangS.HuL.WangY.HeX.ShengQ.Z.OrgunM.A.CaoL.RicciF. and YuP.S., Graph learning based recommender systems: A review. arXiv preprint, 2021.
69.
YangX.GuoY.LiuY. and SteckH., A survey of collaborative filtering-based social recommender systems.Computer Communications41 (2014), 1–10.
70.
ZhangD.HsuC.-H.ChenM.ChenQ.XiongN. and LloretJ., Cold-start recommendation using bi-clustering and fusion for large-scale social recommender systems, IEEE Transactions on Emerging Topics in Computing2(2) (2013), 239–250.
71.
ZhaoZ.-D. and ShangM.-S., User-based collaborative-filtering recommendation algorithms on hadoop. In 2010 Third International Conference on Knowledge Discovery and Data Mining, IEEE, 2010, pp. 478–481.
72.
ZhengY.AgnaniM. and SinghM., Identifcation of grey sheep users by histogram intersection in recommender systems, In International Conference on Advanced Data Mining and Applications, 2017, pp. 148–161.
