A Neural Network-Inspired Approach for Improved and True Movie Recommendations.

Muhammad Ibrahim,Bakhtiar Kasi,Imran Sarwar Bajwa,Riaz Ul-Amin

doi:10.1155/2019/4589060

Abstract

In the last decade, sentiment analysis, opinion mining, and subjectivity of microblogs in social media have attracted a great deal of attention of researchers. Movie recommendation systems are the tools, which provide valuable services to the users. The data available online are growing gradually because the online activities of users or viewers are increasing day by day. Because of this, big data, analytics, and computational issues have raised. Therefore, we have to improve recommendations services upon the traditional one to make the recommendation system significant and efficient. This article presents the solution for these issues by producing the significant and efficient recommendation services using multivariates (ratings, votes, Twitter likes, and reviews) of movies from multiple external resources which are fetched by the web bot and managed by the Apache Hadoop framework in a distributed manner. Reviews are analyzed by a deep semantic analyzer based on the recurrent neural network (RNN/LSTM attention) with user movie attention (UMA) to produce the emotion. The proposed recommender evaluates multivariates and produces a more significant movie recommendation list according to the taste of the user on a mobile app in an efficient way.

Highlights

“Recommendation systems” are services that use Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques to provide the empirical solutions of the recommendations for various application frameworks and services [1]
Our work is on extremity grouping of movie reviews, where an opinionated report is labeled with semantic emotions of the microblog text or reviews and emotions [22] using a semantic parser based on the recurrent neural network (RNN/LSTM) [23, 24]
Ere are several activation functions like sigmoid which ranges from 0 to 1, hyperbolic function which ranges from −1 to 1, and softmax function whose output in categorical distribution and ReLu function is a feedforward neural network. e ANN is not an algorithm; it is a framework for several machine learning algorithms to solve a complex work. erefore, we can say that it is a collection of neurons or networks of neurons. e recurrent neural network (RNN/LSTM) processes the sequence semantically, which is the basic structure of deep neural networks

Summary

Introduction

“Recommendation systems” are services that use Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques to provide the empirical solutions of the recommendations for various application frameworks and services [1]. Most of the recommender systems are univariate and use ratings and reviews or tweets [17], and other few are bivariate (sentiment score and likes) [18,19,20]. We used the hierarchical neural network (HNN) based on LSTM attention, which impaled the global user and movie information via word and sentence-level attention for document representation. Web content (ratings, reviews, likes, votes, smiley, images, and stars) is useful for recommendation services. Movie recommendation systems provide services to users using content-based filtering algorithms [35], collaborative filtering [36], and some combined forms to make a hybrid filtering algorithm [37]. We developed a pilot version for these problems, which consists of a mobile app, a web scraper, and a multivariate recommender to provide the significant services for movie recommendation in an efficient way.

Methods

Results

Discussion

Conclusion