Overview
Production Recommender system is useful information tool based on algorithms to provide customers with the most suitable products.
Recommendation lists are based on user preferences, item details, past user interactions.
Business Challenge
Various challenges in Product Recommendation System-
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Collection of data
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Storing the data
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Analyzing the data
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Filtering the data
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Data had a lot of missing values
Solution Offered
We aim to build such kind of recommendation platform which should not be only collaborative or content-based. It should be having some other features also linked with Hybrid Recommendation system.
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Link Analysis-Link Analysis will help us in knowing the social circle of customers.
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A Transaction between a Customer and Merchant Tracking (graph database)-This tracking will lead to that kind of data which can help us not only in recommendation but other ways also. Tracking helps us to know about our customer more like if customer frequently travels.
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Content-based filtering-Content-based filtering helps us in recommending products similar to what customer already owns.
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Collaborative filtering-Collaborative filtering will help in tracking pattern in customer’s behavior related to other customers so that if products match then, we can recommend a new product to the customer with similar liking.