Product Recommendation System For Ecommerce Apps Using Email Through Hybrid Approach

Abstract

Product Recommendation System having the Hybrid approach would serve as the personalized product recommendation engine which you keep in mind the needs of the shopper, and process the recommendation similarly. This would be an advantage to both the Store Owner as well the buyers. There are various different systems available in the market which serve the same purpose but do have few limitations which could led to major disadvantage like the Cold Start problem. The systems available, mainly work on Collaborative Filtering or Content Based Filtering.But both of them have some or other limitations. Hence the product recommendation system designed will use the Hybrid Approach which will be a combination of both the algorithms i.e. content based as well as collaborative. Hence it solve the major problem of the cold start where the new users do not get any similarity, thus no recommendations. There is even the issue persisting to retain the customers in the competitive market which will be overcome by giving the feature of Churn prediction. This would help to hold the potential customers by churn prediction and retention, tracking their history, therefore save the cost and time eventually of acquiring the new customers.Thus , will prove a better recommendation engine for the Store Owners, help them to increase the revenue and sales.