Title: Neural Network Based Prediction in Recommender

Abstract:

This paper aims to contribute to the cold start problem in recommender system with Neural Network based approach. There are several attempts in academia and in the industry to improve the recommender system. For instance, latent matrix factorization is an algorithm that solves the recommendation problem, it produces efficient outcomes from the core problem. Latent factors are not directly observed but are inferred from other factors. It can be computed by assuming a specific number of such factors and then transforming the large user-item matrix into a smaller matrix based on previously assumed factors. These smaller matrices can be multiplied to reproduce a close approximation to the original user-item matrix using a technique called matrix factorization. Assuming that the matrix can be written as the product of two low-rank matrices, matrix factorization techniques seek to retrieve missing or corrupted entries. Matrix factorization approximates the matrix entries by a simple fixed-function — namely, the inner product — acting on the corresponding row and column latent feature vectors. Substituting a neural architecture for the inner product that learns from the data, improves recommendation problem and deals with the cold start problem

Biography:

Karishma Nanda has her expertise in AI and passion for Neural networks. Her open and contextual evaluation creates new pathways for improving Recommender System. System-Architect with 5 years of experience involving E2E design, development, and implementation of applications built on Pega. Currently pursuing Masters in Artificial Intelligence & Data Science. I have Strong database, Programming skills, machine learning algorithms, data visualization concepts. Possess good communication and interpersonal skills, self-motivated, a quick learner, a team player, have a good understanding of Agile methodology. Specialty in designing Smart Systems based on MAPE-K and utilizing AI and Data Science. Looking for a challenging role where I can utilize my knowledge of system design, AI, Data Science, Machine Learning.