scholarly journals A Movie Recommendation System Based on A Convolutional Neural Network

2021 ◽  
Vol 9 (12) ◽  
pp. 1445-1455
Author(s):  
Jyoti Kumari
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Cosine Similarity ◽  
The Other ◽  
Human Effort ◽  
Movie Recommendation ◽  
Content Based Filtering

Abstract: Due to its vast applications in several sectors, the recommender system has gotten a lot of interest and has been investigated by academics in recent years. The ability to comprehend and apply the context of recommendation requests is critical to the success of any current recommender system. Nowadays, the suggestion system makes it simple to locate the items we require. Movie recommendation systems are intended to assist movie fans by advising which movie to see without needing users to go through the time-consuming and complicated method of selecting a film from a large number of thousands or millions of options. The goal of this research is to reduce human effort by recommending movies based on the user's preferences. This paper introduces a method for a movie recommendation system based on a convolutional neural network with individual features layers of users and movies performed by analyzing user activity and proposing higher-rated films to them. The proposed CNN approach on the MovieLens-1m dataset outperforms the other conventional approaches and gives accurate recommendation results. Keywords: Recommender system, convolutional neural network, movielens-1m, cosine similarity, Collaborative filtering, content-based filtering.

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.

scholarly journals Book Recommendation System using Matrix Factorization

2021 ◽  
Vol 9 (VI) ◽  
pp. 4578-4582
Author(s):  
K. Venkata Ruchitha
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Matrix Factorization ◽  
Recommendation System ◽  
Previous History ◽  
Speed Up ◽  
Area Unit ◽  
Content Based Filtering

In recent years, recommender systems became more and more common and area unit applied to a various vary of applications, thanks to development of things and its numerous varieties accessible, that leaves the users to settle on from bumper provided choices. Recommendations generally speed up searches and create it easier for users to access content that they're curious about, and conjointly surprise them with offers they'd haven't sought for. By victimisation filtering strategies for pre-processing the information, recommendations area unit provided either through collaborative filtering or through content-based Filtering. This recommender system recommends books supported the description and features. It identifies the similarity between the books supported its description. It conjointly considers the user previous history so as to advocate the identical book.

scholarly journals Movie Recommending System Using Collaborative Filtering

2021 ◽  
Vol 9 (VII) ◽  
pp. 1034-1038
Author(s):  
P. Rama Rao
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Cosine Similarity ◽  
Nearest Neighbours ◽  
And Performance ◽  
Content Based Filtering

Movies are one of the sources of entertainment, but the problem is in finding the content of our choice because content is increasing every year. However, recommendation systems plays here an important role for finding the content of desired domain in these situations. The aim of this paper is to improve the accuracy and performance of a filtration techniques existed. There are several methods and algorithms existed to implement a recommendation system. Content-based filtering is the simplest method, it takes input from the users, checks the movie and its content and recommends a list of similar movies. In this paper, to prove the effectiveness of our system, K-NN algorithms and collaborative filtering are used. Here, the usage of cosine similarity is done for recommending the nearest neighbours.

scholarly journals Recommendation System: Techniques and Issues

2019 ◽  
Vol 8 (3) ◽  
pp. 2821-2824
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Information Overload ◽  
Daily Life ◽  
Hybrid Approach ◽  
The Internet ◽  
Individual Interest ◽  
The Many ◽  
Content Based Filtering

In daily life user searched the many things over the internet on the basis of requirement with the help of search engines. Recommendation systems are widely used on the internet to help the user in discover the products or services that are best with their individual interest. RS effectively reduce the information overload by providing personalized suggestions to user when searching for items like movies, songs, or books etc. The main aim of RS is to help the users by providing the surface of information that relevant to them, fulfill their needs and their task. The paper provides an overview of RS and analyze the different approaches used for develop RS that include collaborative filtering, content-based filtering and hybrid approach of recommender system.

scholarly journals A novel Based-Approach Composed of Clustering Algorithm & Cosine Similarity for Products Recommendation

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Collaborative Filtering ◽  
Clustering Algorithm ◽  
Recommendation System ◽  
Cosine Similarity ◽  
New Approach ◽  
System A ◽  
Weak Points ◽  
Cosine Similarity Measure ◽  
High Level ◽  
Content Based Filtering

There are huge tons of transactions being accomplished online every day. This implies that ecommerce is facing the problem of data and information overloads. While customers are shopping via websites, they spend a lot of time to search for the required products based on their needs. This problem can easily be alleviated by having an accurate recommendation system based on a strong algorithm and confident measures in this regard. There are two main techniques for products recommendation; content-based filtering and collaborative filtering. If one of these two techniques implemented on the e-commerce system, a lot of limitations and weak points will appear. This paper aims at generating an optimal list of product, which, in turn, generates an accurate and reliable list of items. The new approach is composed of three components; clustering algorithm, user-based collaborative filtering, and the Cosine similarity measure. This approach implemented using a real dataset of past experienced users. The accuracy of the search results is a matter to users, it recommends the most appropriate products to users of the e-commerce website. This approach shows trustworthy results and achieved a high level of accuracy for recommending products to users.

scholarly journals The Machine Learning Algorithm for Solving the Problem of Generating Recommendations for Goods and Services

2020 ◽  
Vol 10 (4) ◽  
pp. 5-16
Author(s):  
V.A. Sudakov ◽  
I.A. Trofimov
Keyword(s):  
Machine Learning ◽  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Goods And Services ◽  
Complex Models ◽  
Combined Algorithm ◽  
Content Based Filtering

The article proposes an unsupervised machine learning algorithm for assessing the most possible relationship between two elements of a set of customers and goods / services in order to build a recommendation system. Methods based on collaborative filtering and content-based filtering are considered. A combined algorithm for identifying relationships on sets has been developed, which combines the advantages of the analyzed approaches. The complexity of the algorithm is estimated. Recommendations are given on the efficient implementation of the algorithm in order to reduce the amount of memory used. Using the book recommendation problem as an example, the application of this combined algorithm is shown. This algorithm can be used for a “cold start” of a recommender system, when there are no labeled quality samples of training more complex models.

scholarly journals On the Smaller Number of Inputs for Determining User Preferences in Recommender Systems

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2138
Author(s):  
Sang-Min Choi ◽  
Dongwoo Lee ◽  
Chihyun Park
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
Recommendation System ◽  
Magic Number ◽  
User Profile ◽  
User Preferences ◽  
User Preference ◽  
Minimum Number ◽  
Movie Recommendation ◽  
Content Based Filtering

One of the most popular applications for the recommender systems is a movie recommendation system that suggests a few movies to a user based on the user’s preferences. Although there is a wealth of available data on movies, such as their genres, directors and actors, there is little information on a new user, making it hard for the recommender system to suggest what might interest the user. Accordingly, several recommendation services explicitly ask users to evaluate a certain number of movies, which are then used to create a user profile in the system. In general, one can create a better user profile if the user evaluates many movies at the beginning. However, most users do not want to evaluate many movies when they join the service. This motivates us to examine the minimum number of inputs needed to create a reliable user preference. We call this the magic number for determining user preferences. A recommender system based on this magic number can reduce user inconvenience while also making reliable suggestions. Based on user, item and content-based filtering, we calculate the magic number by comparing the accuracy resulting from the use of different numbers for predicting user preferences.

scholarly journals Movie Recommendation System by Using Collaborative Filtering

2021 ◽  
Vol 9 (9) ◽  
pp. 888-892
Author(s):  
Rupal Verma
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Daily Life ◽  
Modern Technology ◽  
Types Of Knowledge ◽  
Movie Recommendation ◽  
Database Connectivity ◽  
Content Based Filtering ◽  
Cold Start Problem ◽  
Filtering Approach

Abstract: This is the era of modern technology where we are all surrounded and covered by technology. This eases our daily life and saves our time and one of the most important techniques that played a very important role in our day-to-day life is the recommendation system. The recommendation system is used in various fields like it is used to recommend products, books, videos, movies, news, and many more. In this paper, we use a Recommendation system for movies we built or a movie recommendation system. It is based on a collaborative filtering approach that makes use of the information provided by the users, analyzes them and recommends movies according to the taste of users. The recommended movie list sorted according to the ratings given to this system is developed in python by using pycharm IDE and MYSQL for database connectivity. The presented recommendation system generates recommendations using various types of knowledge and data about users. Our Recommendation system recommends movies to each and every user by their previous searching history. Here we use some searching techniques as well. We also tried to overcome the cold start problem we use Movielens database. Keywords: Collaborative-filtering, Content-based filtering, Clustering, Recommendation system searching technique, Movies

Axiomatic Analysis of Language Modelling of Recommender Systems

2017 ◽  
Vol 25 (Suppl. 2) ◽  
pp. 113-127 ◽  
Author(s):  
Daniel Valcarce ◽  
Javier Parapar ◽  
Álvaro Barreiro
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Cosine Similarity ◽  
The Other ◽  
Language Models ◽  
Axiomatic Analysis ◽  
Likelihood Model ◽  
Language Modelling ◽  
Neighbourhood Algorithm

Language Models constitute an effective framework for text retrieval tasks. Recently, it has been extended to various collaborative filtering tasks. In particular, relevance-based language models can be used for generating highly accurate recommendations using a memory-based approach. On the other hand, the query likelihood model has proven to be a successful strategy for neighbourhood computation. Since relevance-based language models rely on user neighbourhoods for producing recommendations, we propose to use the query likelihood model for computing those neighbourhoods instead of cosine similarity. The combination of both techniques results in a formal probabilistic recommender system which has not been used before in collaborative filtering. A thorough evaluation on three datasets shows that the query likelihood model provides better results than cosine similarity. To understand this improvement, we devise two properties that a good neighbourhood algorithm should satisfy. Our axiomatic analysis shows that the query likelihood model always enforces those constraints while cosine similarity does not.

Identifying Attack Models for Securing Cluster-Based Recommendation System

2020 ◽  
Vol 14 ◽  
Author(s):  
Amreen Ahmad ◽  
Tanvir Ahmad ◽  
Ishita Tripathi
Keyword(s):  
Collaborative Filtering ◽  
Recommender System ◽  
Recommendation System ◽  
Relevant Information ◽  
System Function ◽  
Recommendation Algorithm ◽  
Underlying Network ◽  
Entire Dataset ◽  
Attack Models ◽  
Wide Usage

: The immense growth of information has led to the wide usage of recommender systems for retrieving relevant information. One of the widely used methods for recommendation is collaborative filtering. However, such methods suffer from two problems, scalability and sparsity. In the proposed research, the two issues of collaborative filtering are addressed and a cluster-based recommender system is proposed. For the identification of potential clusters from the underlying network, Shapley value concept is used, which divides users into different clusters. After that, the recommendation algorithm is performed in every respective cluster. The proposed system recommends an item to a specific user based on the ratings of the item’s different attributes. Thus, it reduces the running time of the overall algorithm, since it avoids the overhead of computation involved when the algorithm is executed over the entire dataset. Besides, the security of the recommender system is one of the major concerns nowadays. Attackers can come in the form of ordinary users and introduce bias in the system to force the system function that is advantageous for them. In this paper, we identify different attack models that could hamper the security of the proposed cluster-based recommender system. The efficiency of the proposed research is validated by conducting experiments on student dataset.

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