Abstract
A recommender system is basically a type of information filtering system that suggests/recommends items based on the factors that constitute what the user is most interested in. The recommendations are typically provided in relation to different decision-making processes. Tourism is a social phenomenon where people deliberately travel in search of recreation, well-being, cultural exploration or get themselves softened up. But, the amount of information available online keeps expanding at exponential rates and thus the users have expressed their feeling of frustration at how challenging it is to find the appropriate information. This problem is called information overload. This is where the recommendation system comes into play which helps in solving the information overload problem. The hybrid system addresses the disadvantages where location-based recommendation systems are used individually, of which the most notable is the cold start issue. Furthermore, in order to improve the accuracy of the prediction to recommend items, these systems search for the ideal fusion of different approaches. Thus, the hybrid recommendation method solves the challenges like ‘cold start problem’, inability to capture changes in user behavior, sparsity and selecting correct choices for users. This paper explores the hybrid recommendation systems and other filtering techniques used in various fields, their challenges, how they can also be used for tourism recommender systems based on the longitudes and latitudes.
References
- Swamy, Pravinkumar, Sandeep Tiwari, and Kunal Pawar. "Tourist Place Recommendation System”
- Manjare, P. A., M. P. Vninawe, M. M. Dabhire, M. R. Bonde, and M. D. Charhate. "Recommendation System Based on Tourist Attraction." Int. Res. J. Eng. Technol. 3 (2016): 877-881
- Kbaier, Mohamed Elyes Ben Haj, Hela Masri, and Saoussen Krichen. "A personalized hybrid tourism recommender system." In 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), pp. 244-250. IEEE, 2017.
- Vahidi Farashah, Mohammadsadegh, Akbar Etebarian, Reza Azmi, and Reza Ebrahimzadeh Dastjerdi. "A hybrid recommender system based-on link prediction for movie baskets analysis." Journal of Big Data 8 (2021): 1-24.
- Ganjare, Riteshwari, Riya Sahu, Pratidnya Kharate, Vaishnavi Lohakare, and Gomati Sharnagat. "Hybrid Recommendation System for Tourism based Social Network, and AI."
- Forouzandeh, Saman, Mehrdad Rostami, and Kamal Berahmand. "A hybrid method for recommendation systems based on tourism with an evolutionary algorithm and topsis model." Fuzzy Information and Engineering 14, no. 1 (2022): 26-50.
- Li, Xixi, Jiahao Xing, Haihui Wang, Lingfang Zheng, Suling Jia, and Qiang Wang. "A hybrid recommendation method based on feature for offline book personalization." arXiv preprint arXiv:1804.11335 (2018).
- Yadav, Sambhav, and Sushama Nagpal. "An improved collaborative filtering based recommender system using bat algorithm." Procedia computer science 132 (2018): 1795-1803.
- Son, Le Hoang. "HU-FCF: a hybrid user-based fuzzy collaborative filtering method in recommender systems." Expert Systems with Applications: An International Journal 41, no. 15 (2014): 6861-6870.
- Ha, Taehyun, and Sangwon Lee. "Item-network-based collaborative filtering: A personalized recommendation method based on a user's item network." Information Processing & Management 53, no. 5 (2017): 1171-1184.
- Najafabadi, Maryam Khanian, Azlinah Hj Mohamed, and Mohd Naz’ri Mahrin. "A survey on data mining techniques in recommender systems." Soft Computing 23 (2019): 627-654.
- Silveira, Thiago, Min Zhang, Xiao Lin, Yiqun Liu, and Shaoping Ma. "How good your recommender system is? A survey on evaluations in recommendation." International Journal of Machine Learning and Cybernetics 10 (2019): 813-831.