Buyagain Grocery Recommender Algorithm for Online Shopping of Grocery and Gourmet Foods

Buyagain Grocery Recommender Algorithm for Online Shopping of Grocery and Gourmet Foods

Sharon J. Moses (VIT University, Vellore, India) and L.D. Dhinesh Babu (VIT University, Vellore, India)
Copyright: © 2018 |Pages: 17
DOI: 10.4018/IJWSR.2018070101
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Online shopping of grocery and gourmet products differ from other shopping activities due to its routine nature of buy-consume-buy. The existing recommendation algorithms of ecommerce websites are suitable only to render recommendation for products of one time purchase. So, in order to identify and recommend the products that users are likely to buy again and again, a novel recommender algorithm is proposed based on linguistic decision analysis model. The proposed buyagain recommender algorithm finds the semantic value of the user comments and computes the semantic value along with the user rating to render recommendation to the user. The efficiency of the buyagain recommender algorithm is evaluated using the grocery and gourmet dataset of amazon ecommerce websites. The end result proves that the algorithm accurately recommends the product that the user likes to purchase once again.
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1. Introduction

The need to retrieve the specific and interested information from the mammoth amount of information paved the way to the evolution of recommendation systems. In the digitalized world, recommendation system assists and helps the users in finding the right information or right product at the precise time. Advantageousness nature of recommendation system made many web based applications ranging from small personal web app to huge e-commerce websites to construct their own personified recommendation system. Existing recommendation algorithms correlate the user ratings on items or compute the user or item related information to render personified recommendations. This kind of traditional recommendation algorithms works well with the one time purchases whereas grocery shopping is a repeated and frequented purchase activity. For example, if a user purchases a movie or a book he/she is not going to buy the same product again instead the user will search for a new one whereas in grocery purchase user need to buy the same product again and again at frequent intervals. Because grocery and gourmet food shopping remain as a necessity and it is blended with the everyday activity of a people. Also the presence of ecommerce giants in the field of grocery and gourmet foods indicates the hidden business value in the shopping market of the grocery business (Dastin, 2017) (Guebert, 2017). Even though the traditional recommendation algorithm assists the user with new recommendations it fails when a user wanted to purchase the same item again (Li, Dias, Jarman, El-Deredy, & Lisboa, 2009). This kind of recommendation scenario especially in the field of grocery and gourmet foods created a significance necessity to develop a futuristic recommendation system to recommend groceries. Therefore, in this work a futuristic recommendation system for groceries is proposed based on the linguistic decision analysis to assist the user in grocery shopping. The proposed buyagain recommendation algorithm, analyses the user comments based on linguistic rules to find the probability of the user to buy the same product again. Initially the semantic value of the user comment is calculated by summing up the significant value and polarity value of the user comment. After the estimation of the semantic value, aggregation value for the item is calculated by computing the semantic value along with the user rating on item. Finally, by mapping the aggregation value to the fuzzy membership function the recommendations are rendered to the user. The paper is constructed as follows: section two details the existing work of grocery recommendation system. In section three the proposed recommendation algorithm is detailed and in section four the proposed algorithm is evaluated using an amazon real world datasets on grocery and gourmet food shopping. Section five concludes the paper with references.

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