scholarly journals Improving Movie Recommendation Systems Filtering by Exploiting User-Based Reviews and Movie Synopses

2020 ◽  
pp. 187-199
Author(s):  
Konstantina Iliopoulou ◽  
Andreas Kanavos ◽  
Aristidis Ilias ◽  
Christos Makris ◽  
Gerasimos Vonitsanos
Keyword(s):  
Recommendation Systems ◽  
Movie Recommendation

scholarly journals A STUDY ON MOVIE RECOMMENDER SYSTEMS BASED ON WORDPRESS PLATFORM

2020 ◽  
Vol 7 (6) ◽  
pp. 152-155
Author(s):  
Tran Thi Thanh
Keyword(s):  
Recommender Systems ◽  
High Performance ◽  
Information Filtering ◽  
Recommendation Systems ◽  
New Era ◽  
History Of ◽  
Movie Recommendation ◽  
User Friendly ◽  
Search History

The rapid growth of data collection has led to a new era of information. Data is being used to create more efficient systems and this is where Recommendation Systems come into play. Recommender systems are among the most effcient tools for information filtering to improve the quality of search results and provide items that are more relevant to the search item or are realted to the search history of the user, especially from big data on Internet. Among those, movie recommendation systems are the useful tools to assist users in classifying them with similar interests. This makes them a central part of websites and e-commerce applications. This paper aims to describe the implementation of a movie recommender system built on the Wordpress platform to be able to take advantage of the plugin support system and outstanding management and statistical features. The obtained results indicate that the proposed approach may provide high performance regarding reliability, efficiency, and accuracy. Moreover, the user-friendly interface and suitable display for devices ranging from desktop to mobile devices are also the advantages.

scholarly journals Research on Recommendation Systems using Deep Learning Models

2019 ◽  
Vol 8 (4) ◽  
pp. 10544-10551
Keyword(s):  
Deep Learning ◽  
Recommender Systems ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Learning Models ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Future Preference ◽  
Movie Recommendation ◽  
News Recommendation

Recommender System is the effective tools that are accomplished of recommending the future preference of a set of products to the consumer and to predict the most likelihood items. Today, customers has the ability to purchase or sell different items with advancement of e-commerce website, nevertheless it made complicate to investigate the majority of appropriate items suitable for the interest of the consumer from many items. Due to this scenario, recommender systems that can recommend items appropriate for user's interest and likings have become mandatory. In recent days, various recommendation methods are applied to resolve the data abundance setback in numerous application areas like movie recommendation, e-commerce, news recommendation, song recommendation and social media. Even if all the available current recommender systems are successful in generating reasonable predictions, these recommendation system still facing the issues like accuracy, cold-start, sparsity and scalability problem. Deep learning, the recently developed sub domain of machine learning technique is utilized in recommendation systems to enhance the feature of predicted output. Deep Learning is used to generate recommendations and the research challenges specific to recommendation systems when using Deep Learning are also presented. In this research, the basic terminologies, the fundamental concepts of Recommendation engine and a wide-ranging review of deep learning models utilized in Recommender Systems are presented.

Collaborative Filtering-Based Recommendation System Using Time Decay Model

2021 ◽  
Vol 17 (3) ◽  
pp. 85-100
Author(s):  
Jayaraman Parthasarathy ◽  
Ramesh Babu Kalivaradhan
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
User Engagement ◽  
Confidence Bound ◽  
Time Decay ◽  
Specific Data ◽  
Movie Recommendation ◽  
Upper Confidence Bound ◽  
F Measure

Online collaborative movie recommendation systems attempt to help customers accessing their favourable movies by gathering exactly comparable neighbors between the movies from their chronological identical ratings. Collaborative filtering-based movie recommendation systems require viewer-specific data, and the need for collecting viewer-specific data diminishes the effectiveness of the recommendation. To solve this problem, the authors employ an effective multi-armed bandit called upper confidence bound, which is applied to automatically recommend the movies for the users. In addition, the concept of time decay is provided in a mathematical definition that redefines the dynamic item-to-item similarity. Then, two patterns of time decay are analyzed, namely concave and convex functions, for simulation. The experiment test the MovieLens 100K dataset. The proposed method attains a maximum F-measure of 98.45 whereas the existing method reaches a minimum F-measure of only 95.60. The presented model adaptively responds to new users, can provide a better service, and generate more user engagement.

scholarly journals COMPARISON MODELS OF MACHINE LEARNING FOR MOVIE RECOMMENDATION SYSTEMS

2021 ◽  
Vol 335 (1) ◽  
pp. 26-31
Author(s):  
A.Y. Zhubatkhan ◽  
Z.A. Buribayev ◽  
S.S. Aubakirov ◽  
M.D. Dilmagambetova ◽  
S.A. Ryskulbek
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Systematic Approach ◽  
Recommendation Systems ◽  
Machine Learning Algorithms ◽  
Travel Planning ◽  
System Recommendation ◽  
History Of ◽  
Movie Recommendation ◽  
The Right

The trend of the Internet makes the presentation of the right content for the right user inevitable. To this end, recommendation systems are used in areas such as music, books, movies, travel planning, e-commerce, education, and more. One of the most popular recommendation systems in the world is Netflix, which generated record profits during quarantine in the first quartile of 2020. The systematic approach of recommendations is based on the history of user selections, likes and reviews, each of which is interpreted to predict future user selections. This article provides a meaningful analysis of various recommendation systems, such as content-based, collaborative filtering and popularity. We reviewed 7 articles published from 2005 to 2019 to discuss issues related to existing models. The purpose of this article is to compare machine learning algorithms in the Surprise library for a recommendation system. Recommendation system has been implemented and quality has been evaluated using the MAE and RMSE metrics.

scholarly journals An enhanced framework for solving cold start problem in movie recommendation systems

2021 ◽  
Vol 24 (3) ◽  
pp. 1628
Author(s):  
Salma Adel Elzeheiry ◽  
N. E. Mekky ◽  
A. Atwan ◽  
Noha A. Hikal
Keyword(s):  
Logistic Regression ◽  
Recommendation Systems ◽  
Principal Component ◽  
Cold Start ◽  
Absolute Error ◽  
Similarity Score ◽  
Least Square ◽  
Sufficient Information ◽  
Proposed Model ◽  
Movie Recommendation

<p>Recommendation systems (RSs) are used to obtain advice regarding decision-making. RSs have the shortcoming that a system cannot draw inferences for users or items regarding which it has not yet gathered sufficient information. This issue is known as the cold start issue. Aiming to alleviate the user’s cold start issue, the proposed recommendation algorithm combined tag data and logistic regression classification to predict the probability of the movies for a new user. First using alternating least square to extract product feature, and then diminish the feature vector by combining principal component analysis with logistic regression to predict the probability of genres of the movies. Finally, combining the most relevant tags based on similarity score with probability and find top N movies with high scores to the user. The proposed model is assessed using the root mean square error (RMSE), the mean absolute error (MAE), recall@N and precision@N and it is applied to 1M, 10M and 20M MovieLens datasets, resulting in an accuracy of 0.8806, 0.8791 and 0.8739.</p>

scholarly journals A Movie Recommendation System Based on Differential Privacy Protection

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Min Li ◽  
Yingming Zeng ◽  
Yue Guo ◽  
Yun Guo
Keyword(s):  
Privacy Protection ◽  
Recommendation System ◽  
Differential Privacy ◽  
Recommendation Systems ◽  
Current Recommendation ◽  
Protection Scheme ◽  
The Past ◽  
Collaborative Filtering Algorithm ◽  
Movie Recommendation ◽  
Privacy Budget

In the past decades, the ever-increasing popularity of the Internet has led to an explosive growth of information, which has consequently led to the emergence of recommendation systems. A series of cloud-based encryption measures have been adopted in the current recommendation systems to protect users’ privacy. However, there are still many other privacy attacks on the local devices. Therefore, this paper studies the encryption interference of applying a differential privacy protection scheme on the data in the user’s local devices under the assumption of an untrusted server. A dynamic privacy budget allocation method is proposed based on a localized differential privacy protection scheme while taking the specific application scene of movie recommendation into consideration. What is more, an improved user-based collaborative filtering algorithm, which adopts a matrix-based similarity calculation method instead of the traditional vector-based method when computing the user similarity, is proposed. Finally, it was proved by experimental results that the differential privacy-based movie recommendation system (DP-MRE) proposed in this paper could not only protect the privacy of users but also ensure the accuracy of recommendations.

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