Novel Scheme for Movie Recommendation System Using User Similarity and Opinion Mining: A Recent Study

2021 ◽  
pp. 138-148
Author(s):  
R. Nagamanjula ◽  
A. Pethalakshmi
Keyword(s):  
Recommendation System ◽  
Opinion Mining ◽  
User Similarity ◽  
Movie Recommendation

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

2019 ◽  
Vol 2019 ◽  
pp. 1-19 ◽  
Author(s):  
Muhammad Ibrahim ◽  
Imran Sarwar Bajwa ◽  
Riaz Ul-Amin ◽  
Bakhtiar Kasi
Keyword(s):  
Neural Network ◽  
Data Analytics ◽  
Recommendation System ◽  
Opinion Mining ◽  
Big Data Analytics ◽  
Mobile App ◽  
Apache Hadoop ◽  
External Resources ◽  
Movie Recommendation ◽  
Day By Day

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.

scholarly journals Movie Recommendation Model from Data through Online Streaming

2021 ◽  
Vol 9 (8) ◽  
pp. 1549-1551
Author(s):  
Runali Komurlekar
Keyword(s):  
Recommendation System ◽  
Rating Scale ◽  
Training Model ◽  
User Preference ◽  
Consumption Pattern ◽  
User Similarity ◽  
Average Rating ◽  
System User ◽  
Movie Recommendation ◽  
The Right

Abstract: With the Pandemic era and easy availability of internet, potential of digital movie and tv series industry is in huge demand. Hence it has led to developing an automatic movie recommendation engine and has become a popular issue. Some of these problems can be solved or at least be minimized if we take the right decisions on what kind of movies to ignore, what movies to consider. This paper examines the recommendations that are obtained with considering the sample movies that have never got an above-average rating, where average rating is defined here as the mid-value between 0 and maximum rating used, for example, 2.5 in 1 to 5 rating scale. The technique used is “collaborative filtering”. Comparison of different pre-training model, it is tried to maximize the effectiveness of semantic understanding and make the recommendation be able to reflect meticulous perception on the relationship between user utilisation and user preference. Keywords: movie recommendation system, user similarity, user similarity, consumption pattern

scholarly journals User profile correlation-based similarity (UPCSim) algorithm in movie recommendation system

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Triyanna Widiyaningtyas ◽  
Indriana Hidayah ◽  
Teguh B. Adji
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
User Behavior ◽  
Correlation Coefficients ◽  
User Profile ◽  
Profile Data ◽  
Similarity Algorithm ◽  
Previous Algorithm ◽  
Movie Recommendation ◽  
Recommendation Accuracy

AbstractCollaborative filtering is one of the most widely used recommendation system approaches. One issue in collaborative filtering is how to use a similarity algorithm to increase the accuracy of the recommendation system. Most recently, a similarity algorithm that combines the user rating value and the user behavior value has been proposed. The user behavior value is obtained from the user score probability in assessing the genre data. The problem with the algorithm is it only considers genre data for capturing user behavior value. Therefore, this study proposes a new similarity algorithm – so-called User Profile Correlation-based Similarity (UPCSim) – that examines the genre data and the user profile data, namely age, gender, occupation, and location. All the user profile data are used to find the weights of the similarities of user rating value and user behavior value. The weights of both similarities are obtained by calculating the correlation coefficients between the user profile data and the user rating or behavior values. An experiment shows that the UPCSim algorithm outperforms the previous algorithm on recommendation accuracy, reducing MAE by 1.64% and RMSE by 1.4%.

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