Preference-Search based Recommendation System for Accommodation Facilitator : A Review
DOI:
https://doi.org/10.32628/CSEIT1952245Keywords:
Accommodation, Renters, Preference-Based Search, Example-Critiquing, Recommendation SystemAbstract
Finding an online house or rented property that meet renters’ requirements is increasingly getting difficult due to large pool of choices available with the renters’ before finalizing accommodation. A renter may spend considerable amount of time exploring numerous online resources to locate accommodation that fit his requirements. Furthermore, renters may not always express their preferences in a manner that easily matches their requirements. Exploring and searching for accommodation online has been done mainly through database queries that return a list of the most suitable accommodations. Recommendation systems methods can be applied to smooth the task of finding desired and required accommodation online. This study proposes a recommendation system that enables renters to carry out a preference-based search on rental properties for accommodation. In this paper preference-based search technique is combined with method called as example-critiquing. Rather than executing a query against the database, this combined approach prompts the renters to express some preferences on rental properties for accommodation. Than this method will be used to construct a preference model for the user, and finally generates a list of properties that best match that preferences.
References
- Viappiani, P., & Faltings, B., 2006: Design and Implementation of Preference-based Search, Ecole Polytechnique Fédérale de Lausanne, Artificial Intelligence Lab, Lausanne
- Ricci, F., Rokach, L., Shapira, B., & Kantor, P., 2011: Introduction to Recommender System Handbook. In Recommender System Handbook (pp. 1-35), Springer
- Yuan, X., Lee, J.-H., Kim, S.-J., & Kim, Y.-H., 2013: Toward a user- oriented recommendation system for real estate websites, Information Systems, 38(2), 231-243
- Hurley, G., & Wilson, D. C. (2001). DubLet: An Online CBR System for Rental Property Recommendation. In D. W. Aha, & I. Watson (Eds.), Case-Based Reasoning Research and Development (pp. 660-674). Springer Link.
- Sharma, R., & Singh, R. (2016, 05). Evolution of Recommender Systems from Ancient Time to Modern Era: A Survey. Indian Journal of Science and Technology, 9 (20), 1-12.
- Ekstrand, M., Riedl, T., & Konstan, J. (2010). Collaborative Filtering Recommender Systems. Foundations and Trends in Human-Computer Interaction, 4 (2), 81-173.
- Schafer, J., Konstan, J., & Riedl, J. (2001, 01). E-commerce Recommendation Applications. Data Mining and Knowledge Discovery, 5 (1), 115-153.
- Burke, R. D., 2002: Hybrid Recommender Systems: Survey and Experiments: User modeling and User-Adapted Interaction. The Journal of Personalization Research, 12(4), 331-370
- Felfernig, A., Friedrich, G., Jannach, D., & Zanker, M., 2011: Developping Constraint-based Recommenders. In F. Ricci, L. Rokach, B. Shapira, & P. B. Kantor, Recommender Systems Handbook, Springer.
- Pazzani, M. J. (1999). A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligence Review, 13, 393-408.
- Karypis, G., Konstan, J., Sarwar, B., & Riedl, J. (2001). Item-Based Collaborative Filtering Recommendation Algorithms. 10th International World Wide Web Conference (pp. 285-295). Hong-Kong: WWW10.
- Chen, L., & Pu, P. (2012). Critiquing-based recommenders: survey and emerging trends. User Modeling and User-Adapted Interaction, 22 (1), 125-150.
- Pu, P., & Faltings, B. (2000). Enriching buyers’ experiences: the SmartClient approach. SIG- CHI Conference on Human Factors in Computing Systems (pp. 289-296). New York: ACM.
- Chen, L., & Pu, P., 2007: Preference-based Organisation Interfaces: Aiding User Critiques in Recommender Systems. In C. Conati, K. McCoy, & G. Paliouras (Eds.), User Modeling 2007: 11th International Conference (pp. 77-86), Springer Berlin Heidelberg
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