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A Hybrid Restaurant Recommender

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 55 - Number 16
Year of Publication: 2012
Prerna Dwivedi
Nikita Chheda

Prerna Dwivedi and Nikita Chheda. Article: A Hybrid Restaurant Recommender. International Journal of Computer Applications 55(16):20-25, October 2012. Full text available. BibTeX

	author = {Prerna Dwivedi and Nikita Chheda},
	title = {Article: A Hybrid Restaurant Recommender},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {55},
	number = {16},
	pages = {20-25},
	month = {October},
	note = {Full text available}


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.


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