Food Recommendation using Classifier and Modified Apriori Algorithm
Sreyam Dasgupta1, Suparna Karmakar2
1Sreyam Dasgupta, Computer Science, Vellore Institute of Technology, Vellore, India.
2Suparna Karmakar, Master Of Computer Applications (MCA), Cambridge Institute Of Technology (permanently affiliated to VTU), Bengaluru, India.
Manuscript received on September 15, 2019. | Revised Manuscript received on 27 September, 2019. | Manuscript published on October 10, 2019. | PP: 3967-3970 | Volume-8 Issue-12, October 2019. | Retrieval Number: L34711081219/2019©BEIESP | DOI: 10.35940/ijitee.L3471.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In today’s world, computer technologies have advanced a lot. One of its greatest gifts to the world is Artificial Intelligence. Natural Language Processing (NLP) and Machine Learning (ML) are two of its subdomains. In this paper, modified versions of two common NLP and ML algorithms have been used to classify food reviews and provide suitable recommendations from them. Currently, reviews can be classified into positive and negative reviews, but it becomes difficult when one review says positive about item A and negative about item B. Moreover, the current Apriori algorithm doesn’t consider the feedbacks from customers (reviews). Modified classifier algorithm and consequently, modified Apriori algorithm has been used to classify each statement part by part and provide recommendations, not just on previous purchases but also using the reviews about above-mentioned purchases. The algorithms can be used for purposes other than food analysis also – wherever purchases and reviews are involved. For e.g., e-commerce companies can use the algorithms to predict and recommend suitable items a user may be interested in.
Keywords: Food Reviews, Classification, Apriori Algorithm, Recommendation
Scope of the Article: Algorithm Engineering