scholarly journals A Survey on Various Techniques for Personal Movie Recommendation

2020 ◽  
pp. 156-163
Author(s):  
Sonal Babu ◽  
Madhu K P
Keyword(s):  
Collaborative Filtering ◽  
Optimization Algorithm ◽  
Comparative Evaluation ◽  
Recommendation System ◽  
Optimization Algorithms ◽  
Time Varying ◽  
Filtering Technique ◽  
Movie Recommendation ◽  
Insight Into

Recommendation system plays an important role in helping users to find appropriate products and contents they usually want. There are various recommendation techniques for recommending items to users. These recommendations can be optimized to be more accurate by means of optimization algorithms. This paper focuses on survey of such recommendation techniques and optimization algorithms in the personalized movie recommendation domain. The comparative evaluation of recommendation techniques is also done. This paper gives an insight into recommendation system, various recommendation techniques and optimization algorithms. Collaborative filtering technique along with time varying multiarmed optimization algorithm will give most appropriate recommendations.

scholarly journals A Recommendation Engine for Predicting Movie Ratings Using a Big Data Approach

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1215
Author(s):  
Mazhar Javed Awan ◽  
Rafia Asad Khan ◽  
Haitham Nobanee ◽  
Awais Yasin ◽  
Syed Muhammad Anwar ◽  
...  
Keyword(s):  
Big Data ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Robust Clustering ◽  
Search Data ◽  
Filtering Technique ◽  
Streaming Services ◽  
Movie Recommendation ◽  
Movie Ratings ◽  
Als Algorithm

In this era of big data, the amount of video content has dramatically increased with an exponential broadening of video streaming services. Hence, it has become very strenuous for end-users to search for their desired videos. Therefore, to attain an accurate and robust clustering of information, a hybrid algorithm was used to introduce a recommender engine with collaborative filtering using Apache Spark and machine learning (ML) libraries. In this study, we implemented a movie recommendation system based on a collaborative filtering approach using the alternating least squared (ALS) model to predict the best-rated movies. Our proposed system uses the last search data of a user regarding movie category and references this to instruct the recommender engine, thereby making a list of predictions for top ratings. The proposed study used a model-based approach of matrix factorization, the ALS algorithm along with a collaborative filtering technique, which solved the cold start, sparse, and scalability problems. In particular, we performed experimental analysis and successfully obtained minimum root mean squared errors (oRMSEs) of 0.8959 to 0.97613, approximately. Moreover, our proposed movie recommendation system showed an accuracy of 97% and predicted the top 1000 ratings for movies.

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%.

Item-Based Collaborative Filtering Blockchain for Secure Movie Recommendation System

2021 ◽  
pp. 937-948
Author(s):  
Shikhar Kumar Padhy ◽  
Ashutosh Kumar Singh ◽  
P. Vetrivelan
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Movie Recommendation

Collaborative filtering in movie Recommendation System based on Rating and Genre

IJARCCE ◽  
2017 ◽  
Vol 6 (3) ◽  
pp. 465-467
Author(s):  
Ashwani Kumar Singh ◽  
P. Beaulah Soundarabai
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Movie Recommendation

scholarly journals Movie Recommendation System using Machine Learning

2021 ◽  
Vol 9 (VI) ◽  
pp. 4778-4781
Author(s):  
Ghanashyam Vibhandik
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Cosine Similarity ◽  
Daily Lives ◽  
Movie Content ◽  
Movie Recommendation ◽  
The Many ◽  
The Individual ◽  
Content Based Filtering ◽  
Filtering Approach

Movies are very significant in our lives. It is one of the many forms of entertainment that we encounter in our daily lives. It is up to the individual to decide whatever type of film they choose to see, whether it is a comedy, romantic film, action film, or adventure film. However, the issue is locating acceptable content, as there is a large amount of information created each year. As a result, finding our favourite film is really difficult. The goal of this research is to improve the regular filtering technique's performance and accuracy. A recommendation system can be implemented using a variety of approaches. Content-based filtering and collaborative filtering strategies are employed in this work. The content-based filtering approach analyses the user's history/past behaviour and recommends a list of comparable movies depending on their input. K-NN algorithms and collaborative filtering are also employed in this paper to improve the accuracy of the results. Cosine similarity is utilised in this work to quickly discover comparable information. The correctness of the cosine angle is measured by cosine similarity. People may quickly find their favourite movie content thanks to all of this.

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