A Manhattan distance based hybrid recommendation system

Begüm UYANIK; Günce Keziban ORMAN

doi:10.18100/ijamec.1232090

Research ArticlesverifiedPeer-ReviewedOpen Access

Pages: 20-29

A Manhattan distance based hybrid recommendation system

Begüm UYANIK fingerprint•

Günce Keziban ORMAN fingerprint

Publication DateMarch 31, 2023

Volume / IssueVol. 11, No. 1 (pp. 20-29)

DOI Identifier10.18100/ijamec.1232090

subject

Abstract

Many online service providers use a recommendation system to assist their customers' decision-making by generating recommendations. Accordingly, this paper proposes a new recommendation system for tourism customers to make online reservations for hotels with the features they need, saving customers time and increasing the impact of personalized hotel recommendations. This new system combined collaborative and content-based filtering approaches and created a new hybrid recommendation system. Two datasets containing customer information and hotel features were analyzed by Recency, Frequency, Monetary (RFM) method in order to identify customers according to their purchasing nature. The main idea of the recommendation system is to establish correlations between users and products and make the decision to choose the most suitable product or information for a particular user. As a result of the exponential growth of online data, this vast amount of information for use in the tourism industry can be leveraged by decision-makers to make purchasing decisions[20]. Filtering, prioritizing, and beneficially presenting relevant information reduces this overload. There are following three main ways that recommendation systems can generate a recommendation list for a user; content-based, collaborative-based, and hybrid approaches[1]. This paper describes each category and its techniques in detail. RFM Analysis is used to identify customer segments by measuring customers' purchasing habits. It is the process of labeling customers by determining the Recency, Frequency, and Monetary values of their purchases and ranking them on a scoring model. Scoring is based on how recently they bought (Recency), how often they bought (Frequency), and purchase size (Monetary). Experimental results show that the accuracy of behavior analysis using Manhattan distance-based hybrid filtering is greatly improved compared to collaborative and content-based algorithms.

Keywords:recommender systemhybrid approachRFM AnalysisCollaborative FilteringContent based filtering

Author Affiliations

Begüm UYANIK — galatasaray universityfingerprint0000-0003-0942-5075
Günce Keziban ORMAN — GALATASARAY UNIVERSITYfingerprint0000-0003-0402-8417

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References (22)

Cited Literature

1
H. Poor, An Introduction to Signal Detection and Estimation. New York: Springer-Verlag, 1985.
2
S. Chen, B. Mulgrew, and P. M. Grant, “A clustering technique for digital communications channel equalization using radial basis function networks,” IEEE Trans. Neural Networks, vol. 4, pp. 570–578, July 1993.
3
G. R. Faulhaber, “Design of service systems with priority reservation,” in Conf. Rec. 1995 IEEE Int. Conf. Communications, pp. 3–8.
4
J. Williams, “Narrow-band analyzer (Thesis or Dissertation style),” Ph.D. dissertation, Dept. Elect. Eng., Harvard Univ., Cambridge, MA, 1993.
5
Motorola Semiconductor Data Manual, Motorola Semiconductor Products Inc., Phoenix, AZ, 1989.
6
R. J. Vidmar. (1992, August). On the use of atmospheric plasmas as electromagnetic reflectors. IEEE Trans. Plasma Sci. [Online]. 21(3). pp. 876—880. Available: http://www.halcyon.com/pub/journals/21ps03-vidmar
7
Isinkaye, F. O., Folajimi, Y. O., & Ojokoh, B. A. (2015). Recommendation systems: Principles, methods and evaluation. Egyptian Informatics Journal, 16(3), 261-273.
8
Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-based systems, 46, 109-132.
9
Seyednezhad, S. M., Cozart, K. N., Bowllan, J. A., & Smith, A. O. (2018). A review on recommendation systems: Context-aware to social-based. arXiv preprint arXiv:1811.11866.
10
Pazzani, M. J., & Billsus, D. (2007). Content-based recommendation systems. In The adaptive web (pp. 325-341). Springer, Berlin, Heidelberg.
11
Shani, G., & Gunawardana, A. (2011). Evaluating recommendation systems. In Recommender systems handbook (pp. 257-297). Springer, Boston, MA.
12
Seyednezhad, S. M., Cozart, K. N., Bowllan, J. A., & Smith, A. O. (2018). A review on recommendation systems: Context-aware to social-based. arXiv preprint arXiv:1811.11866.
13
Hill, W., Stead, L., Rosenstein, M., & Furnas, G. (1995, May). 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).
14
Chien, Y. H., & George, E. I. (1999, January). A bayesian model for collaborative filtering. In AISTATS.
15
Sun, Z., & Luo, N. (2010, August). A new user-based collaborative filtering algorithm combining data-distribution. In 2010 International Conference of Information Science and Management Engineering (Vol. 2, pp. 19-23). IEEE.
16
Kaya, T., & Kaleli, C. (2022). A novel top-n recommendation method for multi-criteria collaborative filtering. Expert Systems with Applications, 198, 116695.
17
Lv, X. (2021, March). Analysis and Optimization Strategy of Travel Hotel Website Reservation Behavior Based on Collaborative Filtering. In 2021 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS) (pp. 362-365). IEEE.
18
Alsayat, A. (2022). Customer decision-making analysis based on big social data using machine learning: a case study of hotels in Mecca. Neural Computing and Applications, 1-22.
19
Wu, J., Liu, C., Wu, Y., Cao, M., & Liu, Y. (2022). A novel hotel selection decision support model based on the online reviews from opinion leaders by best worst method. International Journal of Computational Intelligence Systems, 15(1), 1-20.
20
Bueno, I., Carrasco, R. A., Porcel, C., Kou, G., & Herrera-Viedma, E. (2021). A linguistic multi-criteria decision making methodology for the evaluation of tourist services considering customer opinion value. Applied Soft Computing, 101, 107045.
21
Chang, J. L., Li, H., & Bi, J. W. (2022). Personalized travel recommendation: a hybrid method with collaborative filtering and social network analysis. Current Issues in Tourism, 25(14), 2338-2356.
22
Berker Türker, B., Tugay, R., Öğüdücü, Ş., & Kızıl, İ. (2020). Hotel Recommendation System Based on User Profiles and Collaborative Filtering. arXiv e-prints, arXiv-2009.

A Manhattan distance based hybrid recommendation system

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