A Multi-Criteria Recommender System for Tourism Using Fuzzy Approach
Abstract
Recommender Systems have been widely used in Information and Communication Technology (ICT). The main reason for this extensive use is to decrease the problem of information explosion. Collaborative Filtering techniques, which attempt to predict what information will meet a user’s needs based on data coming from similar users, are becoming increasingly popular as ways to combat information overload. Collaborative Filtering techniques usage has shown significant advantages in tourism service recommendations. Accuracy improvement of Collaborative Filtering techniques for tourism recommender systems has been an important issue in the previous studies. Therefore, this study aims to improve the recommendation accuracy of Collaborative Filtering techniques for tourism recommender systems. In this study, the method of recommendation is developed using fuzzy C-means algorithm for user-based and item-based models. Two similarity measures, Pearson Correlation and Cosine, are used for similarity calculations of users and items in both user-based and item-based models. Mean Absolute Error (MAE) is then used as an evaluation metric to show the accuracy improvement of proposed method. The experimental results on TripAdvisor dataset with several comparisons are presented to show the enhancement of proposed method predictive accuracy. The experimental results demonstrated that the user-based model of recommendation which uses fuzzy C-means algorithm remarkably improves the recommendation predictive accuracy with MAE=0.72 in relation to the item-based recommendation model with MAE=0.73. Since the proposed recommender system improves the accuracy of Collaborative Filtering techniques, the recommender system will be a promising recommendation method for item recommendation task in tourism domain.
Keywords
References
Cramer, H., Evers, V., Ramlal, S., Van Someren, M., Rutledge, L., Stash, N., ... & Wielinga, B. (2008). The effects of transparency on trust in and acceptance of a content-based art recommender. User Modeling and User-Adapted Interaction, 18(5), 455-496.
Adomavicius, G., & Kwon, Y. (2007). New recommendation techniques for multicriteria rating systems. Intelligent Systems, IEEE, 22(3), 48-55.
Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. Knowledge and Data Engineering, IEEE Transactions on, 17(6), 734-749.
Adomavicius, G., Manouselis, N., & Kwon, Y. (2011). Multi-criteria recommender systems. In Recommender systems handbook (pp. 769-803). Springer US.
Agarwal, J., Sharma, N., Kumar, P., Parshav, V., Srivastava, A., & Goudar, R. H. (2013, January). Intelligent search in E-Tourism services using Recommendation System: Perfect guide for tourist. In Intelligent Systems and Control (ISCO), 2013 7th International Conference on (pp. 410-415). IEEE.
Ahn, H. J. (2008). A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Information Sciences, 178(1), 37-51.
Bilge, A., & Kaleli, C. (2014, May). A multi-criteria item-based collaborative filtering framework. In Computer Science and Software Engineering (JCSSE), 2014 11th International Joint Conference on (pp. 18-22). IEEE.
Bonnin, G., & Jannach, D. (2014). Automated generation of music playlists: survey and experiments. ACM Computing Surveys (CSUR), 47(2), 26
Bordogna, G., & Pasi, G. (2010). A flexible multi criteria information filtering model. Soft computing, 14(8), 799-809.
Bostandjiev, S., O'Donovan, J., & Höllerer, T. (2012, September). Tasteweights: a visual interactive hybrid recommender system. In Proceedings of the sixth ACM conference on Recommender systems (pp. 35-42). ACM.
Burke, R. (2007). Hybrid web recommender systems. In The adaptive web (pp. 377-408). Springer Berlin Heidelberg.
Cho, Y. H., Kim, J. K., & Kim, S. H. (2002). A personalized recommender system based on web usage mining and decision tree induction. Expert Systems with Applications, 23(3), 329-342.
Finn, A., Wang, L., & Frank, T. (2009). Attribute perceptions, customer satisfaction and intention to recommend e-services. Journal of Interactive Marketing, 23(3), 209-220.
Fuchs, M., & Zanker, M. (2012). Multi-criteria ratings for recommender systems: an empirical analysis in the tourism domain (pp. 100-111). Springer Berlin Heidelberg
Jannach, D., Karakaya, Z., & Gedikli, F. (2012, June). Accuracy improvements for multi-criteria recommender systems. In Proceedings of the 13th ACM Conference on Electronic Commerce (pp. 674-689). ACM.
Jannach, D., Lerche, L., Gedikli, F., & Bonnin, G. (2013). What recommenders recommend–an analysis of accuracy, popularity, and sales diversity effects. InUser Modeling, Adaptation, and Personalization (pp. 25-37). Springer Berlin Heidelberg.
Jannach, D., Zanker, M., & Fuchs, M. (2014). Leveraging multi-criteria customer feedback for satisfaction analysis and improved recommendations.Information Technology & Tourism, 14(2), 119-149.
Karypis, G. (2001, October). Evaluation of item-based top-n recommendation algorithms. In Proceedings of the tenth international conference on Information and knowledge management (pp. 247-254). ACM.
Konstan, J. A., & Riedl, J. (2012). Recommender systems: from algorithms to user experience. User Modeling and User-Adapted Interaction, 22(1-2), 101-123.
Lee, S., Yang, J., & Park, S. Y. (2004, January). Discovery of hidden similarity on collaborative filtering to overcome sparsity problem. In Discovery Science(pp. 396-402). Springer Berlin Heidelberg.
Linden, G., Smith, B., & York, J. (2003). Amazon. com recommendations: Item-to-item collaborative filtering. Internet Computing, IEEE, 7(1), 76-80.
Lo Storto, C. (2013). Evaluating ecommerce websites cognitive efficiency: An integrative framework based on data envelopment analysis. Applied ergonomics, 44(6), 1004-1014.
Lucas, J. P., Luz, N., Moreno, M. N., Anacleto, R., Almeida Figueiredo, A., & Martins, C. (2013). A hybrid recommendation approach for a tourism system.Expert Systems with Applications, 40(9), 3532-3550.
Melville, P., & Sindhwani, V. (2010). Recommender systems. In Encyclopedia of machine learning (pp. 829-838). Springer US.
Melville, P., Mooney, R. J., & Nagarajan, R. (2002, July). Content-boosted collaborative filtering for improved recommendations. In AAAI/IAAI (pp. 187-192).
Nilashi, M., & Ibrahim, O. B. (2013). A model for detecting customer level intentions to purchase in B2C websites using TOPSIS and fuzzy logic rule-based system. Arabian Journal for Science and Engineering, 39 (3), 1907-1922.
Nilashi, M., bin Ibrahim, O., & Ithnin, N. (2014a). Hybrid recommendation approaches for multi-criteria collaborative filtering. Expert Systems with Applications, 41(8), 3879-3900.
Nilashi, M., bin Ibrahim, O., & Ithnin, N. (2014b). Multi-criteria collaborative filtering with high accuracy using higher order singular value decomposition and Neuro-Fuzzy system. Knowledge-Based Systems, 60, 82-101.
Nilashi, M., bin Ibrahim, O., Ithnin, N., & Sarmin, N. H. (2015b). A multi-criteria collaborative filtering recommender system for the tourism domain using Expectation Maximization (EM) and PCA–ANFIS. Electronic Commerce Research and Applications, 14(6), 542-562.
Nilashi, M., Fathian, M., Gholamian, M. R., bin Ibrahim, O., Talebi, A., & Ithnin, N. (2011). A comparative study of adaptive neuro fuzzy inferences system (ANFIS) and fuzzy inference system (FIS) approach for trust in B2C electronic commerce websites. JCIT, 6 (9), 25-43.
Nilashi, M., Ibrahim, O. B., Ithnin, N., & Zakaria, R. (2015c). A multi-criteria recommendation system using dimensionality reduction and Neuro-Fuzzy techniques. Soft Computing, 19(11), 3173-3207.
Nilashi, M., Jannach, D., bin Ibrahim, O., & Ithnin, N. (2015a). Clustering-and regression-based multi-criteria collaborative filtering with incremental updates. Information Sciences, 293, 235-250.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.