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Developing Deep Learning Models to Raise Recommendation Accuracy

EasyChair Preprint no. 9374

6 pagesDate: November 27, 2022


— Software tools as well as techniques that offer suggestions for various items to a user are defined as “Recommendation System” (RS). Recommender systems provide suggestions that will help users to make decisions “regarding the products or items.” It is used to make recommendations by processing data from actively collected diverse type of information. Under this, one can use the extensively available information about the products through “Online Social Networks (OSN)”Da’u & Salim[5]. “Automatic Recommender system” (ARS) on the “cloud” can recommend products by giving recommendations about the product based on the “user’s questions provided via the cloud platform” (CP). Though number of studies has been conducted on Deep learning models implementation on recommendation system, still very few of them deal with improving accuracy in Recommendation System. Objective of our research is to develop deep learning models to enhance the accuracy of recommendation systems. Moreover, this research will design a deep learning models based platform for sentiment analysis with Recommender System on the E-Commerce Application.

Keyphrases: deep learning, Intelligent product recommendation systems, Recommendation Systems, Sentiment Analysis

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Geetanjali Tyagi and Susmita Ray},
  title = {Developing Deep Learning Models to Raise Recommendation Accuracy},
  howpublished = {EasyChair Preprint no. 9374},

  year = {EasyChair, 2022}}
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