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A Hybrid Restaurant Recommender
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 55 - Number 16 |
| Year of Publication: 2012 |
| Authors: Prerna Dwivedi, Nikita Chheda |
10.5120/8840-3071
|
Prerna Dwivedi, Nikita Chheda . A Hybrid Restaurant Recommender. International Journal of Computer Applications. 55, 16 ( October 2012), 20-25. DOI=10.5120/8840-3071
Abstract
In any e-commerce application, the recommender systems play a vital role as they assist the prospective buyers in making proper decisions on the basis of the recommendations that the system provides. Recommender systems aim at providing the users with effective recommendations based on their intuitions and preferences. The two very old techniques commonly used for providing automated recommendations are collaborative filtering and knowledge based filtering techniques. However, both these techniques have certain drawbacks when used separately. In this paper, we propose architecture for designing hybrid recommender system that combines the advantages of both the techniques; thereby improving accuracy. The proposed approach uses a combination of personalised recommendations (based on individuals past behaviour), social recommendations (based on past behaviour of similar users) and item-based recommendations (based on restaurant database). This combination overcomes all the drawbacks that are faced when these techniques are used separately. In this paper, we have described the application of such a system within the domain of restaurants.
References
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- "Developing a Restaurant Recommender System" by Fredrik Kalseth, Supervised by Marilyn Walker.
- 'Analysis of Recommender Systems' Algorithms' by Emmanouil Vozalis, Konstantinos G. Margaritis
- Personallogic recommender system: http://www. personallogic. com.
- 'Combining Collaborative Filtering and Knowledge-Based Approaches for Better Recommendation Systems', Thomas Tran School of Information Technology and Engineering University of Ottawa
- 'Web usage mining: Discovery and Applications of usage patterns from web data' by Jaideep Srivastava, Robert Cooley.
- 'Web Usage Mining' By Bamshad Mobasher.
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