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Self Attentive Product Recommender – a Hybrid Approach with Machine Learning and Neural Network

EasyChair Preprint no. 1845

5 pagesDate: November 5, 2019


People are choosing products from online ratings and comments. Experience from Amazon , Flipkart and other leading online shopping portals in India, the buyer ‘s product choice is mostly based on the other buyer’s review. We have found an interesting case study of Netflix video recommendation based on so many criteria. Product recommendation is one of the demanding area of recent time where efficiency of prediction of which product a buyer can choose over other hundreds of product is a challenging task. Artificial intelligence has helped researchers in developing algorithm that has self aware method for machine with machine learning, deep learning and natural language processing. In this research, we are introducing a hybrid approach of recommendation for products. There are many ways to find out the people who have similar choice and combining their choices can lead us to suggestions for other products. Collaborative filtering algorithms have been in use for Recommender systems for a long time now. They have been successful in solving many issues of the systems which are in use in the market. User behaviour analysis, sentiment score, product reviews, popularity score can be a decisive factor along with neural network with classification method can lead to more efficient results. In this research, we present some of more potential areas of working on Collaborative Filtering technique with machine learning and deep learning techniques . Self attentive product recommendation is one such technique which focuses on automated form for recommendation which is independent of a dataset and its data type. We have examined other approaches for combining multiple algorithms for predicting user ratings and also discuss some results from the analysis of various strategies used by prior researchers and find solutions to them.

Keyphrases: collaborative filtering, content-based filtering, Negative Sampling, neural network, recommendation techniques

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Devanshu Dudhia and Sonal Dave and Shweta Yagnik},
  title = {Self Attentive Product Recommender – a Hybrid Approach with Machine Learning and Neural Network},
  howpublished = {EasyChair Preprint no. 1845},

  year = {EasyChair, 2019}}
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