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Reseach Article

A Hybrid Restaurant Recommender

by Prerna Dwivedi, Nikita Chheda
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

Prerna Dwivedi, Nikita Chheda . A Hybrid Restaurant Recommender. International Journal of Computer Applications. 55, 16 ( October 2012), 20-25. DOI=10.5120/8840-3071

@article{ 10.5120/8840-3071,
author = { Prerna Dwivedi, Nikita Chheda },
title = { A Hybrid Restaurant Recommender },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 55 },
number = { 16 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 20-25 },
numpages = {9},
url = { },
doi = { 10.5120/8840-3071 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T20:57:26.163427+05:30
%A Prerna Dwivedi
%A Nikita Chheda
%T A Hybrid Restaurant Recommender
%J International Journal of Computer Applications
%@ 0975-8887
%V 55
%N 16
%P 20-25
%D 2012
%I Foundation of Computer Science (FCS), NY, USA

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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  9. 'Web Usage Mining' By Bamshad Mobasher.
Index Terms

Computer Science
Information Sciences


Hybrid recommender system restaurant recommender system collaborative filtering and knowledge based recommender system web log records