Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Rough Sets and Rule Induction from Indiscernibility Relations Based on Possible World Semantics in Incomplete Information Systems with Continuous Domains Read chapter Read first chapter

2021 | OriginalPaper | Chapter

Literature Review with Study and Analysis of the Quality Challenges of Recommendation Techniques and Their Application in Movie Ratings

Authors : Hagar El Fiky, Wedad Hussein, Rania El Gohary

Published in: Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

During the past few decades, the web-based services and organizations like Amazon, Netflix and YouTube have been raised aggressively. These web services have shown the demand for the recommender systems and their growing place in our lives. More steps deeper, we noticed that the severity of the quality and accuracy of these recommendation systems is very high to match users with same interests. For that reason and for being in competitive position with the most outstanding recommendation web services, these recommendation systems should be always monitored and evaluated from a quality perspective. However, due to the steep growth rate of the available web-based services, new challenges like data sparsity, scalability problem and cold start issue have been burst and threaten the performance and the quality of the predicted recommendations. Accordingly, many data scientists and researchers got excited to figure out ways for these challenges especially if they are in scaled environments and distributed systems. These solutions could be achieved using multiple approaches such as machine learning and data mining.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Earthquakes and Thermal Anomalies in a Remote Sensing Perspective
next chapter Predicting Student Retention Among a Homogeneous Population Using Data Mining
Literature
1.
Burke, R., Felfernig, A., Goeker, M.: Recommender systems: an overview. AI Mag. 32(3), 13–18 (2016)
2.
Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems—An Introduction. Cambridge University Press, Cambridge (2010)
3.
Linden, G., Smith, B., York, J.: Recommendations—item-to-item collaborative filtering. IEEE Internet Comput. 7(1), 76–80 (2003)CrossRef
4.
Isinkaye, F.O., Folajimi, Y.O., Ojokoh, B.A.: Recommendation systems: principles, methods and evaluation. Egypt. Inf. J. 16(3), 261–273 (2015)
5.
Adomavicius, G., Zhang, J.: Impact of data characteristics on recommender systems performance. ACM Trans. Manage. Inf. Syst. 3(1), 3:1–3:17 (2012)
6.
Stern, D.H., Herbrich, R., Graepel, T.: Matchbox: large scale online Bayesian recommendations. In: Proceedings of the 18th International Conference on World Wide Web, pp. 111–120. ACM, New York (2009)
7.
Unger, M., Bar, A., Shapira, B., Rokach, L.: Towards latent context aware recommendation systems. Knowl. Based Syst. 104, 165–178 (2016)CrossRef
8.
Verma, J.P., Patel, B., Patel, A.: Big data analysis: recommendation system with hadoop framework. In: Computational Intelligence & Communication Technology, pp. 92–97. IEEE (2015)
9.
Arazy, O., Kumar, N., Shapira, B.: Improving social recommender systems. IT Prof. Mag. 11(4), 31–37 (2009)
10.
Bridge, D., Göker, M.H., McGinty, L., Smyth, B.: Case-based recommender systems. Knowl. Eng. Rev. 20(03), 315–20 (2005)
11.
Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 291–324. Springer, Berlin (2007)
12.
Ponnam, L.T., et al.: Movie recommender system using item based collaborative filtering technique. In: International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS), vol. 1, pp. 56–60. IEEE (2016)
13.
14.
Sardianos, C., Varlamis, I., Eirinaki, M.: Scaling collaborative filtering to large-scale bipartite rating graphs using Lenskit and Spark. In: BigDataService, IEEE Second International Conference, pp. 70–79 (2017)
15.
Shanahan, J.G., Dai, L.: Large scale distributed data science using apache spark. In: Conference Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 2323–2324 (2015)
16.
Tao, Y.: Design of large-scale mobile advertising recommendation system. In: 2015 4th International Conference on Computer Science and Network Technology (ICCSNT), pp. 763–767. IEEE (2015)
17.
Eirinaki, M., Gao, J., Varlamis, I., Tserpes, K.: Recommender systems for large-scale social networks: a review of challenges and solutions. Future Gener. Comput. Syst. 78, 412–417 (2017)
18.
Ma, H., King, I., Lyu, M.R.: Learning to recommend with explicit and implicit social relations. ACM Trans. Intell. Syst. Technol. 2(3), Article No. 29 (2011)
19.
Gurini, D.F., Gasparetti, F., Micarelli, A., Sansonetti, G.: Temporal people-to-people recommendation on social networks with sentiment-based matrix factorization. Future Gener. Comput. Syst. 78(P1), 430–439 (2018)CrossRef
20.
Su, X., Khoshgoftaar, T.M.: A survey of collaborative filtering techniques. Adv. Artif. Intell. 2009, 4 (2009)CrossRef
21.
Sardianos, C., Tsirakis, N., Varlamis, I.: A survey on the scalability of recommender systems for social networks. In: Social Networks Science: Design, Implementation, Security, and Challenges. Springer, Berlin, pp. 89–110 (2018)
22.
Ricci, F., Rokach, L., Shapira, B.: Introduction to Recommender Systems Handbook, pp. 1–35. Springer, Berlin (2011)
23.
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Comput. 42(8), 30–37 (2009)CrossRef
24.
Huang, Y., Cui, B., Jiang, J., Hong, K., Zhang, W., Xie, Y.: Real-time video recommendation exploration. In: Conference Proceedings of the 2016 International Conference on Management of Data, pp. 35–46 (2016)
25.
Liu, C.L., Wu, X.W.: Large-scale recommender system with compact latent factor model. Expert Syst. Appl. 64, 467–475 (2016)CrossRef
26.
Göksedef, M., Gündüz-Ögüdücü, S.: Combination of web page recommender systems. Expert Syst. Appl. 37(4), 2911–2922 (2010)
27.
Smirnova, E., Vasile, F.: Contextual sequence modeling for recommendation with recurrent neural networks. In: 2nd Workshop on Deep Learning for Recommender Systems (DLRS 2017), pp. 2–9. ACM Press, New York (2017)
28.
Zanotti, G., Horvath, M., Barbosa, L., Immedisetty, V., Gemmell, J.: Infusing collaborative recommenders with distributed representations. In: Conference Proceedings of the 1st Workshop on Deep Learning for Recommender Systems. ACM, Boston, MA, USA, pp. 35–42 (2016)
29.
Ma, H., Yang, H., Lyu, M.R., King. I.: SoRec: Social Recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 931–40, Oct 2008
30.
Yin, H., Sun, Y., Cui, B., Hu, Z., Chen, L.: LCARS: a location-content-aware recommender system. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 221–229. ACM (2013)
31.
Niu, J., Wang, L., Liu, X., Yu, S.: FUIR: fusing user and item information to deal with data sparsity by using side information in recommendation systems. J. Network Comput. Appl. 70, 41–50 (2016)CrossRef
32.
Shahabi, C., Chen, Y.S.: Web information personalization: challenges and approaches. In: Databases in Networked Information Systems, pp. 5–15. Springer, Berlin (2003)
33.
Billsus, D., Pazzani, M.J.: Learning collaborative information filters. In: ICML, pp. 46–54 (1998)
34.
Shahabi, C., Banaei-Kashani, F., Chen, Y.S., McLeod, D.: Yoda: an accurate and scalable web-based recommendation system. In: Cooperative Information Systems, pp. 418–432 (2001)
35.
Khusro, S., Ali, Z., Ullah, I.: Recommender systems: issues, challenges, and research opportunities. In: Information Science and Applications (ICISA) 2016. LNEE, vol. 376, pp. 1179–1189. Springer, Singapore (2016)
36.
Sollenborn, M., Funk, P.: Category-based filtering and user stereotype cases to reduce the latency problem in recommender systems. In: Advances in Case-Based Reasoning, pp. 395–405. Springer, Berlin (2002)
37.
Polat, H., Du, W.: Privacy-preserving collaborative filtering using randomized perturbation techniques (2003)
38.
Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29(2–3), 131–163 (1997)CrossRef
39.
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. (TOIS) 22(1), 5–53 (2004)
40.
Véras, D., Prota, T., Bispo, A., Prudêncio, R., Ferraz, C.: A literature review of recommender systems in the television domain. Expert Syst. Appl. 42(22), 90, 46–76 (2015)
41.
Ferri, C., Hernández-Orallo, J., Modroiu, R.: An experimental comparison of performance measures for classification. Pattern Recogn. Lett. 30(1), 27–38 (2009)CrossRef
42.
Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’99), pp. 230–237. ACM (1999)
43.
Felfernig, A., Jeran, M., Ninaus, G., Reinfrank, F., Reiterer, S., Stettinger, M.: Basic approaches in recommendation systems. In: Recommendation Systems in Software Engineering, pp. 15–37. Springer, Berlin (2014)
44.
Smith, B., Linden, G.: Two decades of recommender systems at amazon.com. IEEE Internet Comput. 21(3), 12–18 (2017)
45.
Nunes Barbosa, L., Gemmell, J., Horvath, M., Heimfarth, T.: Distributed user-based collaborative filtering on an opportunistic network. In: IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA), pp. 266–273, Krakow (2018)
46.
Ekstrand, M.D., Riedl, J.T., Konstan, J.A.: Collaborative filtering recommender systems. Found. Trends Hum. Comput. Interact. 4(2), 1–94 (2011)CrossRef
47.
Takacs, G., Pilászy, I., Németh, B., Tikk, D.: Scalable collaborative filtering approaches for large recommender systems. J. Mach. Learn. Res. 10, 623–656 (2009)
48.
Zarzour, H., Al-Sharif, Z., Al-Ayyoub, M., Jararweh, Y.: A new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques. In: 9th International Conference on Information and Communication Systems (ICICS), pp. 102–106, Irbid (2018)
49.
Arora, S., Goel, S.: Improving the accuracy of recommender systems through annealing. In: Lecture Notes in Networks and Systems, pp. 295–304 (2017)
50.
Koohi, H., Kiani, K.: User based collaborative filtering using fuzzy c-means. Measurement 91, 134–139 (2016)
51.
Kim, K., Ahn, H.: Recommender systems using cluster-indexing collaborative filtering and social data analytics. Int. J. Prod. Res. 55(17), 5037–5049 (2017)CrossRef
52.
Meng, S., Dou, W., Zhang, X., Chen, J.: KASR: a keyword-aware service recommendation method on MapReduce for big data applications. IEEE Trans. Parallel Distrib. Syst. 25(12), 3221–3231 (2014)CrossRef
53.
Zarzour, H., Maazouzi, F., Soltani, M., Chemam, C.: An improved collaborative filtering recommendation algorithm for big data. In: Proceedings of the 6th IFIP International Conference on Computational Intelligence and its Applications 2018, pp. 102–106. CIIA (2018)
54.
Tian, H., Liang, P.: Personalized service recommendation based on trust relationship. Sci. Program. 2017, 1–8 (2017)
55.
Pazzani, M., Billsus, D.: Learning and revising user profiles: the identification of interesting web sites. In: Michalski, R.S., Wnek, J. (eds.) Machine Learning, vol. 27, pp. 313–331 (1997)
56.
Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web, vol. 4321, pp. 325–341. Lecture Notes in Computer Science. Springer, Berlin (2007)CrossRef
57.
Burke, R.: Knowledge-based recommender systems. Encycl. Libr. Inf. Syst. 69(32), 180–200 (2000)
58.
Felfernig, A., Friedrich, G., Jannach, D., Zanker, M.: An integrated environment for the development of knowledge-based recommender applications. IJEC 11(2), 11–34 (2006)
59.
Felfernig, A., Friedrich, G., Gula, B., Hitz, M., Kruggel, T., Melcher, R., Riepan, D., Strauss, S., Teppan, E., Vitouch, O.: Persuasive recommendation: exploring serial position effects in knowledge-based recommender systems. In: 2nd International Conference of Persuasive Technology 2007. LNCS, vol. 4744, pp. 283–294. Springer, Berlin (2007)
60.
Mandl, M., Felfernig, A., Teppan, E., Schubert, M.: Consumer decision making in knowledge based recommendation. J. Intell. Inf. Syst. 37(1), 1–22 (2010)CrossRef
61.
Felfernig, A., Shchekotykhin, K.: Debugging user interface descriptions of knowledge-based recommender applications. In: Proceedings of the 11th International Conference on Intelligent User Interfaces, IUI 2006, pp. 234–241. ACM Press, New York (2006)
62.
Kabinsingha, S., Chindasorn, S., Chantrapornchai, C.: Movie rating approach and application based on data mining. IJEIT 2(1), 77–83 (2012)
63.
64.
Ricci, F.: Context-aware music recommender systems: workshop keynote abstract. In: Proceedings of the 21st International Conference Companion on World Wide Web, pp. 865–866. ACM, Lyon (2012)
65.
Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Ricci, F., Rokach, L., Shapira, B. (eds.), Recommender Systems Handbook. Springer, Berlin, pp. 191–226 (2015)
66.
Song, L., Tekin, C., Schaar, M.V.D.: Online learning in large-scale contextual recommender systems. IEEE Trans. Serv. Comput. 9(3), 433–445 (2016)CrossRef
67.
Bennett, J., Lanning, S.: The Netflix prize. In: Proceedings of the KDD Cup Workshop, pp. 3–6. ACM, New York (2007)
68.
Zhou, Y., Wilkinson, D., Schreiber, R., Pan, R.: Large-scale parallel collaborative filtering for the Netflix prize. In: Proceedings of the 4th International Conference on Algorithmic Aspects in Information and Management 2008. LNCS, vol. 5034, pp. 337–348. Springer, Berlin (2008)
69.
Bao, Z., Xia, H.: Movie Rating Estimation and Recommendation, CS229 Project, Stanford University (2012)
70.
71.
Seroussi, Y., Bohnert, F., Zukerman, I.: Personalised rating prediction for new users using latent factor models. In: De Bra, P., Gronbaek, K. (eds.) Proceedings of the 22nd ACM Conference on Hypertext and Hypermedia, pp. 47–56. Association for Computing Machinery (ACM) (2011)
72.
Basu, S.: Movie rating prediction system based on opinion mining and artificial neural networks. In: Kamal, R., Henshaw, M., Nair, P. (eds.) International Conference on Advanced Computing Networking and Informatics. Advances in Intelligent Systems and Computing, vol. 870. Springer, Singapore (2019)
73.
74.
75.
76.
Metadata
Title
Literature Review with Study and Analysis of the Quality Challenges of Recommendation Techniques and Their Application in Movie Ratings
Authors
Hagar El Fiky
Wedad Hussein
Rania El Gohary
Copyright Year
2021
Book
Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges

Print ISBN: 978-3-030-59337-7

Electronic ISBN: 978-3-030-59338-4

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-59338-4_12

Premium Partner

    Image Credits