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Bharani Kumar Depuru is a well known IT personality from Hyderabad. He is the Founder and Director of AiSPRY and 360DigiTMG. Bharani Kumar is an IIT and ISB alumni with more than 18+ years of experience, he held prominent positions in the IT elites like HSBC, ITC Infotech, Infosys, and Deloitte. He is a prevalent IT consultant specializing in Industrial Revolution 4.0 implementation, Data Analytics practice setup, Artificial Intelligence, Big Data Analytics, Industrial IoT, Business Intelligence and Business Management. Bharani Kumar is also the chief trainer at 360DigiTMG with more than Ten years of experience and has been making the IT transition journey easy for his students. 360DigiTMG is at the forefront of delivering quality education, thereby bridging the gap between academia and industry.
'Users' are typically the rows in the data utilised for the analysis, and 'Items' will be the columns. (from retail ecommerce context)
Whether a user has purchased or not
Whether user has rated the product or not
How many products each user has purchased?
What is the rating provided by the user?
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The values, such as ratings columns, are occasionally split by frequency. The quantity of buyers or raters is referred to as the frequency. The ratings are normalised throughout this procedure.
applied often on e-commerce sites. To create personalised tactics to propose products with a high likelihood of being purchased, customers' purchasing behaviours are examined.
It is necessary to respond to these two basic questions. Recommendations in turn assist the user develop confidence in themselves and brand loyalty.
The most often used method is collaborative filtering, which relies on user similarity metrics.
Cosine Based Similarity:
Cos(A,B) = A•B / |A|*|B|
Correlation Based Similarity:
Corr(AB) = Covariance (A,B) / Stdev (A) * Stdev (B)
Euclidean distance
Manhattan distance, etc.
List the products that the individual is MOST LIKELY to buy from the list of products that customers with comparable buying patterns have already purchased.
Costly memory and lazy learning
Compute is relatively costly - n computations for similarities
Making strategic decisions in comparison to suggestions with "Better accuracy and offline" vs. "Slightly lower accuracy and online"
The number of vacant cells in rating matrices makes them large and sparse.
The sparse rating matrix is handled using the SVD algorithm.
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