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2021 | OriginalPaper | Chapter

Recent Challenges in Recommender Systems: A Survey

Authors : Madhusree Kuanr, Puspanjali Mohapatra

Published in: Progress in Advanced Computing and Intelligent Engineering

Publisher: Springer Singapore

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Abstract

The recent revolutionary technology transformations in the internet domain have enabled us to move from static web pages to ubiquitous computing web through social networking web. In return, this has enabled the recommender systems to leave their infancy and get matured while tackling the dynamic challenges arising for users. Recommender system anticipates user requirements before the user requires them. Recommender system in various domains proves its efficiency by providing appropriate recommendations according to the preferences of the users. It is a software solution in different online applications which helps the user to make appropriate decisions and also acts as a business tool in various domains. The proposed article covers the various types of recommender systems as well as the strategies and recent challenging research issues to improve the capabilities of recommender systems.

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Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: GroupLens: applying collaborative filtering to Usenet news. Commun. ACM 40(3), 77–87 (1997)

Hill, W., Stead, L., Rosenstein, M., Furnas, G.: Recommending and evaluating choices in a virtual community of use. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 194–201. ACM Press/Addison-Wesley Publishing Co (1995)

Shardanand, U., Maes, P.: Social information filtering: Algorithms for automating” word of mouth. In: Chi, vol. 95, pp. 210–217 (1995)

Terveen, L., Hill, W., Amento, B., McDonald, D., Creter, J.: PHOAKS: a system for sharing recommendations. Commun. ACM 40(3), (1997)

Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 43–52. Morgan Kaufmann Publishers Inc. (1998)

Delgado, J., Ishii, N.: Memory-based weighted majority prediction. In SIGIR Workshop Recomm. Syst, Citeseer (1999)

Balabanovi, M., Shoham, Y.: Fab: content-based, collaborative recommendation. Commun. ACM 40(3), 66–72 (1997)

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

Soboroff, I., Nicholas, C.: Combining content and collaboration in text filtering. In: Proceedings of the IJCAI, vol. 99, pp. 86–91 (1999)

10.

Burke, R.: Knowledge-based recommender systems. Encycl. Libr. Inform. Syst. 69(Supplement 32), 175–186 (2000)

11.

Middleton, S.E., Shadbolt, N.R., De Roure, D.C.: Ontological user profiling in recommender systems. ACM Trans. Inform. Syst. (TOIS) 22(1), 54–88 (2004)

12.

Yu, C., Tang, Q.J., Liu, Z., Dong, B., Wei, Z.: A recommender system for ordering platform based on an improved collaborative filtering algorithm. In: 2018 International Conference on Audio, Language and Image Processing (ICALIP), pp. 298–302 (2018)

13.

Rohit, Singh, A.K.: Comparison of measures of collaborative filtering recommender systems: rating prediction accuracy versus usage prediction accuracy. In: 2017 International Conference on Innovations in Control, Communication and Information Systems (ICICCI), pp. 1–4 (2017)

14.

Shakirova, E.: Collaborative filtering for music recommender system. In: 2017 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), pp. 548-550. IEEE (2017)

15.

Walek, B., Spackova, P.: Content-based recommender system for online stores using expert system. In: 2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), pp. 164–165 (2018)

16.

Bahulikar, S.: Analyzing recommender systems and applying a location based approach using tagging. In: 2017 2nd International Conference for Convergence in Technology (I2CT), pp. 198–202. IEEE (2017)

17.

Pal,A., Parhi,P. Aggarwal,M.: An improved content based collaborative filtering algorithm for movie recommendations. In: Tenth International Conference on Contemporary Computing (IC3), Noida, pp. 1--3 (2017)

18.

Devika, R.V.S.: A novel model for hospital recommender system using hybrid filtering and big data techniques. 575–579 (2018). https://doi.org/10.1109/ismac.2018.8653717

19.

Kbaier, M.E.B.H., Masri, H., Krichen, S.: A personalized hybrid tourism recommender system. In: 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), pp. 244–250. IEEE (2017)

20.

Zhang, Y., Liu, X., Liu, W., Zhu, C.: Hybrid recommender system using semi-supervised clustering based on gaussian mixture model. In: 2016 International Conference on Cyberworlds (CW), pp. 155–158. IEEE (2016)

21.

Subbotin, S., Gladkova, O., Parkhomenko, A.: Knowledge-based recommendation system for embedded systems platform-oriented design. In: 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies (CSIT), vol. 1, pp. 368–373. IEEE (2018)

22.

Wonoseto, M.G., Rosmansyah, Y.: Knowledge based recommender system and web 2.0 to enhance learning model in junior high school. In: 2017 International Conference on Information Technology Systems and Innovation (ICITSI), pp. 168–171. Bandung (2017)

23.

Tsai, Y.T., Wuy, C.S., Hsuy, H.L., Liuy, T., Cheny, P.L., Keng-Te Liao, W.H.C.: A cross-domain recommender system based on common-sense knowledge bases. In: 2017 Conference on Technologies and Applications of Artificial Intelligence (TAAI), pp. 80–83. IEEE (2017)

24.

Pazzani, M. J. (1999). A framework for collaborative, content-based and demographic filtering. Artif. int. Rev. 13(5–6), 393-408

25.

Aggarwal, C.C., Wolf, J.L., Wu, K.L., Yu, P.S.: Horting hatches an egg: A new graph-theoretic approach to collaborative filtering. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 201–212. ACM (1999)

26.

Desarkar, M.S., Sarkar, S., Mitra, P.: Aggregating preference graphs for collaborative rating prediction. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 21–28. ACM (2010)

27.

Aprilianti, M., Mahendra, R., Budi, I.: Implementation of weighted parallel hybrid recommender systems for e-commerce in Indonesia. In: 2016 International Conference on Advanced Computer Science and Information Systems (ICACSIS), pp. 321–326. IEEE (2016)

28.

Goel, M., Sarkar, S.: Web site personalization using user profile information. In: International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, pp. 510–513. Springer, Berlin, Heidelberg (2002)

29.

Bakr, Albayrak, S.: User based and item based collaborative filtering with temporal dynamics. In: 2014 22nd Signal Processing and Communications Applications Conference (Siu), pp. 252–255. IEEE (2014)

30.

He, X., Chen, T., Kan, M. Y., Chen, X.: Trirank: Review-aware explainable recommendation by modeling aspects. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1661–1670. ACM (2015)

31.

Chelliah, M., Sarkar, S.: Product recommendations enhanced with reviews. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, pp. 398–399. ACM (2017)

32.

Desarkar, M.S., Saxena, R., Sarkar, S.: Preference relation based matrix factorization for recommender systems. In: International conference on user modeling, adaptation, and personalization, pp. 63–75. Springer, Berlin, Heidelberg (2012)

33.

Desarkar, M.S., Sarkar, S.: Rating prediction using preference relations based matrix factorization. In: UMAP Workshops (2012)

34.

Mallick, P., Sarkar, S. Mitra, P.: Decision recommendation system for transporters in an online freight exchange platform. In: 9th International Conference on Communication Systems and Networks (COMSNETS), Bangalore, pp. 448–453 (2017)

35.

Beel, J., Brunel, V.: Data pruning in recommender systems research: best-practice or malpractice? In: 13th ACM Conference on Recommender Systems (RecSys)

Title: Recent Challenges in Recommender Systems: A Survey
Authors: Madhusree Kuanr
Puspanjali Mohapatra
Publisher: Springer Singapore
Book: Progress in Advanced Computing and Intelligent Engineering
Print ISBN: 978-981-15-6352-2

Electronic ISBN: 978-981-15-6353-9

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-981-15-6353-9_32

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