Optimization Of An Online Store Price Recommendation System Using Hybrid

Royransom Nzeh, Nnmadi Johnson Ezeora, Uzo Izuchukwu, Uzo Blessing Chimezie

Abstract


Recommender Systems (RSs) are software tools and techniques that provide suggestions for items that are most likely of interest to a particular user. We take a look at the online-store price recommendation system, the processes and methodology and the steps taken to design the system. We will also evaluate the research methodology and elaborate on the basic functionalities of the recommendation system. Store price Recommender Systems (RSs) are software tools and techniques that provide suggestions for products that are most likely of interest to a particular user. This paper aim to design and develop an online store price recommendation system using Hybrid techniques, its goal is to suggest viable products to users and also provide real time cost/price of products. The system is based on user’s evaluation of items or previous purchases records. However, this has been known to expose two major issues: sparsity problem and scalability problem The proposed system is designed using the Object-Oriented Analysis and Design Methodology (OOADM) owing to the fact that it is a data driven methodology and concentrates on several views and perspectives of data. The system to be developed is an online web application that allows interaction from both user and administrator.


Keywords


Online store, recommendation system, Hybrid techniques

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References


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DOI: http://dx.doi.org/10.52155/ijpsat.v27.2.3313

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