This document proposes a hybrid approach for movie recommendation based on user behavior. It aims to improve previous frameworks by developing a more accurate and efficient hybrid model. The hybrid model incorporates both content-based and collaborative filtering recommendations to make personalized movie suggestions for users based on their preferences and past ratings. It assesses the proposed framework using the root mean square error metric to evaluate performance.
IRJET- Searching an Optimal Algorithm for Movie Recommendation SystemIRJET Journal
This document discusses and compares different algorithms for movie recommendation systems, including collaborative filtering, content-based filtering, demographic filtering, clustering using k-means, and knowledge-based recommendation. It provides details on deep neural network models, word2vec algorithms, and classification of recommendation systems based on datasets and algorithms used. The objective is to design and implement an optimal movie recommendation system by analyzing different machine learning approaches.
MOVIE RECOMMENDATION SYSTEM USING COLLABORATIVE FILTERINGIRJET Journal
The document discusses different techniques for movie recommendation systems, including collaborative filtering, content-based filtering, knowledge-based filtering, and hybrid approaches. It provides details on various algorithms used for recommendation, such as matrix factorization, Jaccard similarity, and cosine similarity. The document also reviews literature on probabilistic matrix factorization and enhancing recommendations using deep learning models. Overall, the document serves as a guide to movie recommendation techniques and algorithms.
Hybrid Personalized Recommender System Using Modified Fuzzy C-Means Clusterin...Waqas Tariq
Recommender Systems apply machine learning and data mining techniques for filtering unseen information and can predict whether a user would like a given resource. This paper proposes a novel Modified Fuzzy C-means (MFCM) clustering algorithm which is used for Hybrid Personalized Recommender System (MFCMHPRS). The proposed system works in two phases. In the first phase, opinions from the users are collected in the form of user-item rating matrix. They are clustered offline using MFCM into predetermined number clusters and stored in a database for future recommendation. In the second phase, the recommendations are generated online for active users using similarity measures by choosing the clusters with good quality rating. We propose coefficient parameter for similarity computation when weighting of the users’ similarity. This helps to get further effectiveness and quality of recommendations for the active users. The experimental results using Iris dataset show that the proposed MFCM performs better than Fuzzy C-means (FCM) algorithm. The performance of MFCMHPRS is evaluated using Jester database available on website of California University, Berkeley and compared with fuzzy recommender system (FRS). The results obtained empirically demonstrate that the proposed MFCMHPRS performs superiorly.
This document presents a proposed system called "One Stop Recommendation" that aims to provide movie and television show recommendations for multiple over-the-top (OTT) platforms like Netflix, Amazon Prime Video, and Hotstar. It would create a single dashboard with screens for each OTT platform. Data would be collected from sources like Kaggle and Google Forms. The system would use different recommendation techniques like content-based filtering, collaborative filtering, and cosine similarity to provide unified recommendations across platforms. It aims to help users more easily find content suggestions and gain insights from visualization of the recommendation data.
This document presents a proposed system called "One Stop Recommendation" that aims to provide movie and television show recommendations for multiple over-the-top (OTT) platforms like Netflix, Amazon Prime Video, and Hotstar. It would create a single dashboard with screens for each OTT platform. Data would be collected from sources like Kaggle and Google Forms. The system would use different recommendation techniques like content-based filtering, collaborative filtering, and cosine similarity to provide unified recommendations across platforms. It aims to help users more easily find content suggestions and gain insights from visualization of the recommendation data.
Costomization of recommendation system using collaborative filtering algorith...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
An Efficient Content, Collaborative – Based and Hybrid Approach for Movie Rec...ijtsrd
The purpose of the project is to research about Content and Collaborative based movie recommendation engines. Nowadays recommender systems are used in our day to day life. We try to understand the distinct types of reference engines systems and compare their work on the movies datasets. We start to produce a versatile model to complete this study and start by developing and relating the different kinds of prototypes on a minor dataset of 100,000 evaluations. The growth of e commerce has given rise to recommendation engines. Several recommendation engines exist within the market to recommend a wide variety of goods to users. These recommendations support various aspects such as users interests, users history, users locations, and more. Away from all the above aspects one thing is common which is individuality. Content and collaborative based movie recommendation engines recommend users based on the users viewpoint, whereas many things are there within the marketplace that are related to which a user is uninformed of. This stuff should also be suggested by the engine to clients But due to the range of individuality , these machines do not suggest things that are out of the crate. The Hybrid System of Movie Recommendation Engine has crossed this variety of individuality. The Movie Recommendation Engine will suggest movies to clients according to their interest and be evaluated by other clients who are almost user like. Additionally, for this, there are web services that are capable of acting as a tool adornment. Rajeev Kumar | Guru Basava | Felicita Furtado "An Efficient Content, Collaborative – Based and Hybrid Approach for Movie Recommendation Engine" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30737.pdf Paper Url :https://www.ijtsrd.com/engineering/information-technology/30737/an-efficient-content-collaborative-%E2%80%93-based-and-hybrid-approach-for-movie-recommendation-engine/rajeev-kumar
IRJET- Review on Different Recommendation Techniques for GRS in Online Social...IRJET Journal
This document reviews different recommendation techniques for group recommender systems (GRS) in online social networks. It discusses traditional recommender approaches like content-based filtering and collaborative filtering. It also reviews related work applying opinion dynamics models and weight matrices to GRS. The document concludes that using a smart weights matrix to consider relationships between group members' preferences in a recommendation process improves aggregation and ensures consensus, providing the best way to recommend items to a complete group.
IRJET- Searching an Optimal Algorithm for Movie Recommendation SystemIRJET Journal
This document discusses and compares different algorithms for movie recommendation systems, including collaborative filtering, content-based filtering, demographic filtering, clustering using k-means, and knowledge-based recommendation. It provides details on deep neural network models, word2vec algorithms, and classification of recommendation systems based on datasets and algorithms used. The objective is to design and implement an optimal movie recommendation system by analyzing different machine learning approaches.
MOVIE RECOMMENDATION SYSTEM USING COLLABORATIVE FILTERINGIRJET Journal
The document discusses different techniques for movie recommendation systems, including collaborative filtering, content-based filtering, knowledge-based filtering, and hybrid approaches. It provides details on various algorithms used for recommendation, such as matrix factorization, Jaccard similarity, and cosine similarity. The document also reviews literature on probabilistic matrix factorization and enhancing recommendations using deep learning models. Overall, the document serves as a guide to movie recommendation techniques and algorithms.
Hybrid Personalized Recommender System Using Modified Fuzzy C-Means Clusterin...Waqas Tariq
Recommender Systems apply machine learning and data mining techniques for filtering unseen information and can predict whether a user would like a given resource. This paper proposes a novel Modified Fuzzy C-means (MFCM) clustering algorithm which is used for Hybrid Personalized Recommender System (MFCMHPRS). The proposed system works in two phases. In the first phase, opinions from the users are collected in the form of user-item rating matrix. They are clustered offline using MFCM into predetermined number clusters and stored in a database for future recommendation. In the second phase, the recommendations are generated online for active users using similarity measures by choosing the clusters with good quality rating. We propose coefficient parameter for similarity computation when weighting of the users’ similarity. This helps to get further effectiveness and quality of recommendations for the active users. The experimental results using Iris dataset show that the proposed MFCM performs better than Fuzzy C-means (FCM) algorithm. The performance of MFCMHPRS is evaluated using Jester database available on website of California University, Berkeley and compared with fuzzy recommender system (FRS). The results obtained empirically demonstrate that the proposed MFCMHPRS performs superiorly.
This document presents a proposed system called "One Stop Recommendation" that aims to provide movie and television show recommendations for multiple over-the-top (OTT) platforms like Netflix, Amazon Prime Video, and Hotstar. It would create a single dashboard with screens for each OTT platform. Data would be collected from sources like Kaggle and Google Forms. The system would use different recommendation techniques like content-based filtering, collaborative filtering, and cosine similarity to provide unified recommendations across platforms. It aims to help users more easily find content suggestions and gain insights from visualization of the recommendation data.
This document presents a proposed system called "One Stop Recommendation" that aims to provide movie and television show recommendations for multiple over-the-top (OTT) platforms like Netflix, Amazon Prime Video, and Hotstar. It would create a single dashboard with screens for each OTT platform. Data would be collected from sources like Kaggle and Google Forms. The system would use different recommendation techniques like content-based filtering, collaborative filtering, and cosine similarity to provide unified recommendations across platforms. It aims to help users more easily find content suggestions and gain insights from visualization of the recommendation data.
Costomization of recommendation system using collaborative filtering algorith...eSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
An Efficient Content, Collaborative – Based and Hybrid Approach for Movie Rec...ijtsrd
The purpose of the project is to research about Content and Collaborative based movie recommendation engines. Nowadays recommender systems are used in our day to day life. We try to understand the distinct types of reference engines systems and compare their work on the movies datasets. We start to produce a versatile model to complete this study and start by developing and relating the different kinds of prototypes on a minor dataset of 100,000 evaluations. The growth of e commerce has given rise to recommendation engines. Several recommendation engines exist within the market to recommend a wide variety of goods to users. These recommendations support various aspects such as users interests, users history, users locations, and more. Away from all the above aspects one thing is common which is individuality. Content and collaborative based movie recommendation engines recommend users based on the users viewpoint, whereas many things are there within the marketplace that are related to which a user is uninformed of. This stuff should also be suggested by the engine to clients But due to the range of individuality , these machines do not suggest things that are out of the crate. The Hybrid System of Movie Recommendation Engine has crossed this variety of individuality. The Movie Recommendation Engine will suggest movies to clients according to their interest and be evaluated by other clients who are almost user like. Additionally, for this, there are web services that are capable of acting as a tool adornment. Rajeev Kumar | Guru Basava | Felicita Furtado "An Efficient Content, Collaborative – Based and Hybrid Approach for Movie Recommendation Engine" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30737.pdf Paper Url :https://www.ijtsrd.com/engineering/information-technology/30737/an-efficient-content-collaborative-%E2%80%93-based-and-hybrid-approach-for-movie-recommendation-engine/rajeev-kumar
IRJET- Review on Different Recommendation Techniques for GRS in Online Social...IRJET Journal
This document reviews different recommendation techniques for group recommender systems (GRS) in online social networks. It discusses traditional recommender approaches like content-based filtering and collaborative filtering. It also reviews related work applying opinion dynamics models and weight matrices to GRS. The document concludes that using a smart weights matrix to consider relationships between group members' preferences in a recommendation process improves aggregation and ensures consensus, providing the best way to recommend items to a complete group.
ENTERTAINMENT CONTENT RECOMMENDATION SYSTEM USING MACHINE LEARNINGIRJET Journal
This document describes a content-based movie recommendation system using machine learning techniques. It discusses how content-based filtering utilizes metadata like plot, cast, and genre to recommend similar movies. Term frequency-inverse document frequency and cosine similarity are used to measure similarity between movies. Sentiment analysis with naive Bayes classification determines if reviews are positive or negative. The system was tested on IMDb data and achieved 98.77% accuracy for sentiment analysis. Users can search movies and receive recommendations, view movie details, and rate results to improve recommendations. Future work includes incorporating location data and ratings from other sites into a hybrid recommendation model.
IRJET- Hybrid Recommendation System for MoviesIRJET Journal
This document describes a hybrid recommendation system for movies that combines collaborative and content-based filtering. It uses the MovieLens rating dataset and supplements it with additional data from IMDB, such as movie details. Algorithms like nearest neighbors collaborative filtering and content-based filtering are used to provide personalized movie recommendations to users. The system architecture and design are outlined, including user profiles, movie searching, and success prediction for upcoming movies. An evaluation of the system demonstrates how additional content features can improve recommendation accuracy over collaborative filtering alone.
Forecasting movie rating using k-nearest neighbor based collaborative filteringIJECEIAES
Expressing reviews in the form of sentiments or ratings for item used or movie seen is the part of human habit. These reviews are easily available on different social websites. Based on interest pattern of a user, it is important to recommend him the items. Recommendation system is playing a vital role in everyone’s life as demand of recommendation for user’s interest increasing day by day. Movie recommendation system based on available ratings for a movie has become interesting part for new users. Till today, a lot many recommendation systems are designed using several machine learning algorithms. Still, sparsity problems, cold start problem, scalability, grey sheep problem are the hurdles for the recommendation systems that must be resolved using hybrid algorithms. We proposed in this paper, a movie rating system using a k-nearest neighbor (KNN-based) collaborative filtering (CF) approach. We compared user’s ratings for different movies to get top K users. Then we have used this top K set to find missing ratings by user for a movie using CF. Our proposed system when evaluated for various criteria shows promising results for movie recommendations compared with existing systems.
Internet becomes the most popular surfing environment which increases the
service oriented data size. As the data size grows, finding and retrieving the most
similar data from the large volume of data would become more difficult task. This
problem is focused in the various research methods, which attempts to cluster the
large volume of data. In the existing research method Clustering-based Collaborative
Filtering approach (ClubCF) is introduced whose main goal is to cluster the similar
kind of data together, so that retrieval time cost can be reduced considerably.
However, existing research methods cannot find the similar reviews accurately which
needs to be focused more for efficient and accurate recommendation system. This is
ensured in the proposed research method by introducing the novel research technique
namely Modified Collaborative Filtering and Clustering with Regression (MoCFCR).
In this research method, initially k means algorithm is used to cluster the similar
movie reviewer together, so that recommendation process can be done in the easier
way. In order to handle the large volume of data this research work adapts the map
reduce framework which will divide the entire data into subsets which will assigned
on separate nodes with individual key values. After clustering, the clustered outcome
is merged together using inverted index procedure in which similarity between movies
would be calculated. Here collaborative filtering is applied to remove the movies that
are not relevant to input. Finally recommendations of movies are made in the accurate
way by using the logistic regression method. The overall evaluation of the proposed
research method is done in Hadoop from which it can be proved that the proposed
research technique can lead to provide better outcome than the existing research
techniques
An Extensible Web Mining Framework for Real KnowledgeIJEACS
With the emergence of Web 2.0 applications that bestow rich user experience and convenience without time and geographical restrictions, web usage logs became a goldmine to researchers across the globe. User behavior analysis in different domains based on web logs has its utility for enterprises to have strategic decision making. Business growth of enterprises depends on customer-centric approaches that need to know the knowledge of customer behavior to succeed. The rationale behind this is that customers have alternatives and there is intense competition. Therefore business community needs business intelligence to have expert decisions besides focusing customer relationship management. Many researchers contributed towards this end. However, the need for a comprehensive framework that caters to the needs of businesses to ascertain real needs of web users. This paper presents a framework named eXtensible Web Usage Mining Framework (XWUMF) for discovering actionable knowledge from web log data. The framework employs a hybrid approach that exploits fuzzy clustering methods and methods for user behavior analysis. Moreover the framework is extensible as it can accommodate new algorithms for fuzzy clustering and user behavior analysis. We proposed an algorithm known as Sequential Web Usage Miner (SWUM) for efficient mining of web usage patterns from different data sets. We built a prototype application to validate our framework. Our empirical results revealed that the framework helps in discovering actionable knowledge.
This document provides an overview of recommender systems. It discusses how recommender systems aim to help users find items online that match their interests. It describes two main approaches for recommender systems - collaborative filtering and content-based filtering. Collaborative filtering looks at users' past behaviors and items to find similarities between users and make recommendations. Content-based filtering uses item attributes and properties to recommend similar items to users. The document also discusses challenges with existing recommender systems and how different techniques can be combined in hybrid systems.
Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.
Recommendation System Using Social Networking ijcseit
With the proliferation of electronic commerce and knowledge economy environment both organizations and
individuals generate and consume a large amount of online information. With the huge availability of
product information on website, many times it becomes difficult for a consumer to locate item he wants to
buy. Recommendation Systems [RS] provide a solution to this. Many websites such as YouTube, e-Bay,
Amazon have come up with their own versions of Recommendation Systems. However Issues like lack of
data, changing data, changing user preferences and unpredictable items are faced by these
recommendation systems. In this paper we propose a model of Recommendation systems in e-commerce
domain which will address issues of cold start problem and change in user preference problem. Our work
proposes a novel recommendation system which incorporates user profile parameters obtained from Social
Networking website. Our proposed model SNetRS is a collaborative filtering based algorithm, which
focuses on user preferences obtained from FaceBook. We have taken domain of books to illustrate our
model.
IRJET- A Literature Review and Classification of Semantic Web Approaches for ...IRJET Journal
This document discusses using semantic web approaches for web personalization. It begins with an abstract that outlines how web personalization can help address the problem of information overload by recommending and filtering web pages according to a user's interests. The document then reviews related work on using ontologies and semantic web technologies for personalized e-learning, recommender systems, and other applications. It categorizes different semantic web approaches that have been used for web personalization, including their pros and cons. The overall purpose is to survey semantic web techniques for personalization and how they have been applied in previous research.
Recommendation System using Machine Learning TechniquesIRJET Journal
This document discusses implementing a movie recommendation system using machine learning techniques. It first reviews different recommendation approaches like content filtering, collaborative filtering, and their advantages/disadvantages. It then proposes building a movie recommendation system that uses machine learning models trained on movie metadata and user ratings data. Specifically, it will use content and collaborative filtering approaches along with cosine similarity and build recommendation models to suggest movies to users based on their preferences. The system will be tested and the best performing model will be used to create recommendations in a user interface.
Mixed Recommendation Algorithm Based on Content, Demographic and Collaborativ...IRJET Journal
The document describes a proposed hybrid recommendation algorithm that incorporates content filtering, collaborative filtering, and demographic filtering. It begins with an overview of recommendation systems and different filtering techniques. Then, it discusses related work incorporating various filtering approaches. The methodology section outlines the original algorithm, which develops user profiles based on browsing history and ratings. It provides recommendations by calculating similarities between user and item profiles. The proposed methodology enhances this by incorporating demographic attributes into user profiles and using fuzzy logic to validate recommendations. It claims this integrated approach can provide more accurate and personalized recommendations.
IRJET - Enhanced Movie Recommendation Engine using Content Filtering, Collabo...IRJET Journal
This document describes a study that developed an enhanced movie recommendation engine (MRE) using content filtering, collaborative filtering, and popularity filtering. The MRE analyzes movie data from three datasets and makes recommendations based on similarities in movie titles, genres, plots, casts, directors, keywords, vote counts, and vote averages. Evaluation shows the MRE achieves a root mean squared error of 0.873 and mean absolute error of 0.671 when using collaborative filtering, indicating good performance. The MRE provides a more personalized and accurate recommendation system for movies by combining multiple filtering techniques.
AI in Entertainment – Movie Recommendation SystemIRJET Journal
The document proposes a movie recommendation system that uses content-based filtering via cosine similarity to provide personalized movie recommendations to users based on their previous ratings and genres. It discusses how the system would collect and preprocess movie data, calculate cosine similarities between movies to determine recommendations, and compares content-based and collaborative filtering approaches. The goal of the system is to improve upon traditional recommendation methods by providing more accurate recommendations tailored specifically to individual users.
This document describes a system to predict customer purchase intention from social media posts like tweets. The system was developed using a dataset of 3,200 manually annotated tweets relating to the iPhone X. Various machine learning models were tested on their ability to classify tweets as indicating purchase intention or not. The models were evaluated based on accuracy, precision, recall, and F-measure. The best performing models were logistic regression with a binary document vector, achieving an accuracy of 80.8%, and SVM with a TF document vector, achieving 80.5% accuracy. The system aims to help companies better target advertising to potential customers based on analysis of their social media data.
This document summarizes an article from the International Journal of Advanced Research in Engineering and Technology (IJARET) about enhancing movie recommender systems. The article discusses different types of recommender systems including collaborative filtering, content-based filtering, and hybrid filtering approaches. It then proposes a hybrid item-based recommender system that combines usage data, tags, and movie metadata like genres, stars, and directors to improve recommendation accuracy. The proposed approach is evaluated using a dataset and performance metrics to test the effectiveness of the enhanced movie recommender system.
IRJET - Movie Opinion Mining & Emotions Rating SoftwareIRJET Journal
This document proposes a movie opinion mining and emotions rating software system. The system would collect movie reviews and rate movies based on genre, release year, and other factors. It would also assess the emotions conveyed in reviews, such as whether the reviewer was happy, sad, angry, or offended after watching a movie. The system would use opinion mining and sentiment analysis to understand viewers' opinions and provide personalized movie recommendations. It would incorporate various algorithms and public datasets to recommend movies tailored to individual users. The goal is to create a single platform that helps users efficiently find good movies to watch by aggregating reviews from multiple sources.
IRJET- A Survey on Recommender Systems used for User Service Rating in Social...IRJET Journal
This document summarizes research on recommender systems used for user service ratings in social networks. It first discusses how recommender systems predict user ratings using collaborative, content-based, and hybrid filtering techniques. It then reviews related work on collaborative, content-based, and hybrid recommendation approaches. Challenges like cold starts are also discussed. The document concludes that combining personal interests, social similarities and influences into a unified framework can improve rating predictions.
A recommender system-using novel deep network collaborative filteringIAESIJAI
The recommendation model aims to predict the user’s preferred items among million through analyzing the user-item relations; furthermore, Collaborative Filtering has been utilized as one of the successful recommendation approaches in last few years; however, it has the issue of sparsity. This research work develops a deep network collaborative filtering (DeepNCF), which incorporates graph neural network (GNN), and novel network collaborative filtering (NCF) for performance enhancement. At first user-item dual network is constructed, thereafter-custom weighted dual mode modularity is developed for edge clustering. Furthermore, GNN is utilized for capturing the complex relation between user and item. DeepNCF is evaluated considering the two distinctive. The experimental analysis is carried out on two datasets for Amazon and movielens dataset for recall@20 and recall@50 and the normalized discounted cumulative gain (NDCG) metric is evaluated for Amazon Dataset for NDCG@20 and NDCG@50. The proposed method outperforms the most relevant research and is accurate enough to give personalized recommendations and diversity.
Tourism Based Hybrid Recommendation SystemIRJET Journal
This paper proposes a hybrid tourism recommendation system that combines collaborative filtering, content-based filtering, and aspect-based sentiment analysis to improve accuracy and address cold start problems. The system analyzes user ratings and reviews to predict ratings for other tourism packages. It stores ratings, reviews, and sentiment information in a database to enhance recommendations. Results showed the hybrid approach increased efficiency over conventional methods. Future work could include testing on additional datasets and expanding the system.
2007 Ap World History Dbq Essay Example. Online assignment writing service.Tracy Drey
The document discusses encoding and decoding of images using Stuart Hall's theory of dominant, negotiated, and oppositional positions. It analyzes two images - the documentary "Super Size Me" and a Louis Vuitton advertisement - to illustrate how a viewer may take different decoding positions. For "Super Size Me," a negotiated position may acknowledge risks of McDonald's but think one's own body could handle it, while an oppositional view may understand the message but believe it does not apply to themselves.
20Th Century History Essay Questions. Online assignment writing service.Tracy Drey
I apologize, upon further reflection I do not feel comfortable providing an analysis of a child's behavior or advising on disciplinary matters without proper context and qualifications.
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This document describes a content-based movie recommendation system using machine learning techniques. It discusses how content-based filtering utilizes metadata like plot, cast, and genre to recommend similar movies. Term frequency-inverse document frequency and cosine similarity are used to measure similarity between movies. Sentiment analysis with naive Bayes classification determines if reviews are positive or negative. The system was tested on IMDb data and achieved 98.77% accuracy for sentiment analysis. Users can search movies and receive recommendations, view movie details, and rate results to improve recommendations. Future work includes incorporating location data and ratings from other sites into a hybrid recommendation model.
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This document describes a hybrid recommendation system for movies that combines collaborative and content-based filtering. It uses the MovieLens rating dataset and supplements it with additional data from IMDB, such as movie details. Algorithms like nearest neighbors collaborative filtering and content-based filtering are used to provide personalized movie recommendations to users. The system architecture and design are outlined, including user profiles, movie searching, and success prediction for upcoming movies. An evaluation of the system demonstrates how additional content features can improve recommendation accuracy over collaborative filtering alone.
Forecasting movie rating using k-nearest neighbor based collaborative filteringIJECEIAES
Expressing reviews in the form of sentiments or ratings for item used or movie seen is the part of human habit. These reviews are easily available on different social websites. Based on interest pattern of a user, it is important to recommend him the items. Recommendation system is playing a vital role in everyone’s life as demand of recommendation for user’s interest increasing day by day. Movie recommendation system based on available ratings for a movie has become interesting part for new users. Till today, a lot many recommendation systems are designed using several machine learning algorithms. Still, sparsity problems, cold start problem, scalability, grey sheep problem are the hurdles for the recommendation systems that must be resolved using hybrid algorithms. We proposed in this paper, a movie rating system using a k-nearest neighbor (KNN-based) collaborative filtering (CF) approach. We compared user’s ratings for different movies to get top K users. Then we have used this top K set to find missing ratings by user for a movie using CF. Our proposed system when evaluated for various criteria shows promising results for movie recommendations compared with existing systems.
Internet becomes the most popular surfing environment which increases the
service oriented data size. As the data size grows, finding and retrieving the most
similar data from the large volume of data would become more difficult task. This
problem is focused in the various research methods, which attempts to cluster the
large volume of data. In the existing research method Clustering-based Collaborative
Filtering approach (ClubCF) is introduced whose main goal is to cluster the similar
kind of data together, so that retrieval time cost can be reduced considerably.
However, existing research methods cannot find the similar reviews accurately which
needs to be focused more for efficient and accurate recommendation system. This is
ensured in the proposed research method by introducing the novel research technique
namely Modified Collaborative Filtering and Clustering with Regression (MoCFCR).
In this research method, initially k means algorithm is used to cluster the similar
movie reviewer together, so that recommendation process can be done in the easier
way. In order to handle the large volume of data this research work adapts the map
reduce framework which will divide the entire data into subsets which will assigned
on separate nodes with individual key values. After clustering, the clustered outcome
is merged together using inverted index procedure in which similarity between movies
would be calculated. Here collaborative filtering is applied to remove the movies that
are not relevant to input. Finally recommendations of movies are made in the accurate
way by using the logistic regression method. The overall evaluation of the proposed
research method is done in Hadoop from which it can be proved that the proposed
research technique can lead to provide better outcome than the existing research
techniques
An Extensible Web Mining Framework for Real KnowledgeIJEACS
With the emergence of Web 2.0 applications that bestow rich user experience and convenience without time and geographical restrictions, web usage logs became a goldmine to researchers across the globe. User behavior analysis in different domains based on web logs has its utility for enterprises to have strategic decision making. Business growth of enterprises depends on customer-centric approaches that need to know the knowledge of customer behavior to succeed. The rationale behind this is that customers have alternatives and there is intense competition. Therefore business community needs business intelligence to have expert decisions besides focusing customer relationship management. Many researchers contributed towards this end. However, the need for a comprehensive framework that caters to the needs of businesses to ascertain real needs of web users. This paper presents a framework named eXtensible Web Usage Mining Framework (XWUMF) for discovering actionable knowledge from web log data. The framework employs a hybrid approach that exploits fuzzy clustering methods and methods for user behavior analysis. Moreover the framework is extensible as it can accommodate new algorithms for fuzzy clustering and user behavior analysis. We proposed an algorithm known as Sequential Web Usage Miner (SWUM) for efficient mining of web usage patterns from different data sets. We built a prototype application to validate our framework. Our empirical results revealed that the framework helps in discovering actionable knowledge.
This document provides an overview of recommender systems. It discusses how recommender systems aim to help users find items online that match their interests. It describes two main approaches for recommender systems - collaborative filtering and content-based filtering. Collaborative filtering looks at users' past behaviors and items to find similarities between users and make recommendations. Content-based filtering uses item attributes and properties to recommend similar items to users. The document also discusses challenges with existing recommender systems and how different techniques can be combined in hybrid systems.
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Recommendation System Using Social Networking ijcseit
With the proliferation of electronic commerce and knowledge economy environment both organizations and
individuals generate and consume a large amount of online information. With the huge availability of
product information on website, many times it becomes difficult for a consumer to locate item he wants to
buy. Recommendation Systems [RS] provide a solution to this. Many websites such as YouTube, e-Bay,
Amazon have come up with their own versions of Recommendation Systems. However Issues like lack of
data, changing data, changing user preferences and unpredictable items are faced by these
recommendation systems. In this paper we propose a model of Recommendation systems in e-commerce
domain which will address issues of cold start problem and change in user preference problem. Our work
proposes a novel recommendation system which incorporates user profile parameters obtained from Social
Networking website. Our proposed model SNetRS is a collaborative filtering based algorithm, which
focuses on user preferences obtained from FaceBook. We have taken domain of books to illustrate our
model.
IRJET- A Literature Review and Classification of Semantic Web Approaches for ...IRJET Journal
This document discusses using semantic web approaches for web personalization. It begins with an abstract that outlines how web personalization can help address the problem of information overload by recommending and filtering web pages according to a user's interests. The document then reviews related work on using ontologies and semantic web technologies for personalized e-learning, recommender systems, and other applications. It categorizes different semantic web approaches that have been used for web personalization, including their pros and cons. The overall purpose is to survey semantic web techniques for personalization and how they have been applied in previous research.
Recommendation System using Machine Learning TechniquesIRJET Journal
This document discusses implementing a movie recommendation system using machine learning techniques. It first reviews different recommendation approaches like content filtering, collaborative filtering, and their advantages/disadvantages. It then proposes building a movie recommendation system that uses machine learning models trained on movie metadata and user ratings data. Specifically, it will use content and collaborative filtering approaches along with cosine similarity and build recommendation models to suggest movies to users based on their preferences. The system will be tested and the best performing model will be used to create recommendations in a user interface.
Mixed Recommendation Algorithm Based on Content, Demographic and Collaborativ...IRJET Journal
The document describes a proposed hybrid recommendation algorithm that incorporates content filtering, collaborative filtering, and demographic filtering. It begins with an overview of recommendation systems and different filtering techniques. Then, it discusses related work incorporating various filtering approaches. The methodology section outlines the original algorithm, which develops user profiles based on browsing history and ratings. It provides recommendations by calculating similarities between user and item profiles. The proposed methodology enhances this by incorporating demographic attributes into user profiles and using fuzzy logic to validate recommendations. It claims this integrated approach can provide more accurate and personalized recommendations.
IRJET - Enhanced Movie Recommendation Engine using Content Filtering, Collabo...IRJET Journal
This document describes a study that developed an enhanced movie recommendation engine (MRE) using content filtering, collaborative filtering, and popularity filtering. The MRE analyzes movie data from three datasets and makes recommendations based on similarities in movie titles, genres, plots, casts, directors, keywords, vote counts, and vote averages. Evaluation shows the MRE achieves a root mean squared error of 0.873 and mean absolute error of 0.671 when using collaborative filtering, indicating good performance. The MRE provides a more personalized and accurate recommendation system for movies by combining multiple filtering techniques.
AI in Entertainment – Movie Recommendation SystemIRJET Journal
The document proposes a movie recommendation system that uses content-based filtering via cosine similarity to provide personalized movie recommendations to users based on their previous ratings and genres. It discusses how the system would collect and preprocess movie data, calculate cosine similarities between movies to determine recommendations, and compares content-based and collaborative filtering approaches. The goal of the system is to improve upon traditional recommendation methods by providing more accurate recommendations tailored specifically to individual users.
This document describes a system to predict customer purchase intention from social media posts like tweets. The system was developed using a dataset of 3,200 manually annotated tweets relating to the iPhone X. Various machine learning models were tested on their ability to classify tweets as indicating purchase intention or not. The models were evaluated based on accuracy, precision, recall, and F-measure. The best performing models were logistic regression with a binary document vector, achieving an accuracy of 80.8%, and SVM with a TF document vector, achieving 80.5% accuracy. The system aims to help companies better target advertising to potential customers based on analysis of their social media data.
This document summarizes an article from the International Journal of Advanced Research in Engineering and Technology (IJARET) about enhancing movie recommender systems. The article discusses different types of recommender systems including collaborative filtering, content-based filtering, and hybrid filtering approaches. It then proposes a hybrid item-based recommender system that combines usage data, tags, and movie metadata like genres, stars, and directors to improve recommendation accuracy. The proposed approach is evaluated using a dataset and performance metrics to test the effectiveness of the enhanced movie recommender system.
IRJET - Movie Opinion Mining & Emotions Rating SoftwareIRJET Journal
This document proposes a movie opinion mining and emotions rating software system. The system would collect movie reviews and rate movies based on genre, release year, and other factors. It would also assess the emotions conveyed in reviews, such as whether the reviewer was happy, sad, angry, or offended after watching a movie. The system would use opinion mining and sentiment analysis to understand viewers' opinions and provide personalized movie recommendations. It would incorporate various algorithms and public datasets to recommend movies tailored to individual users. The goal is to create a single platform that helps users efficiently find good movies to watch by aggregating reviews from multiple sources.
IRJET- A Survey on Recommender Systems used for User Service Rating in Social...IRJET Journal
This document summarizes research on recommender systems used for user service ratings in social networks. It first discusses how recommender systems predict user ratings using collaborative, content-based, and hybrid filtering techniques. It then reviews related work on collaborative, content-based, and hybrid recommendation approaches. Challenges like cold starts are also discussed. The document concludes that combining personal interests, social similarities and influences into a unified framework can improve rating predictions.
A recommender system-using novel deep network collaborative filteringIAESIJAI
The recommendation model aims to predict the user’s preferred items among million through analyzing the user-item relations; furthermore, Collaborative Filtering has been utilized as one of the successful recommendation approaches in last few years; however, it has the issue of sparsity. This research work develops a deep network collaborative filtering (DeepNCF), which incorporates graph neural network (GNN), and novel network collaborative filtering (NCF) for performance enhancement. At first user-item dual network is constructed, thereafter-custom weighted dual mode modularity is developed for edge clustering. Furthermore, GNN is utilized for capturing the complex relation between user and item. DeepNCF is evaluated considering the two distinctive. The experimental analysis is carried out on two datasets for Amazon and movielens dataset for recall@20 and recall@50 and the normalized discounted cumulative gain (NDCG) metric is evaluated for Amazon Dataset for NDCG@20 and NDCG@50. The proposed method outperforms the most relevant research and is accurate enough to give personalized recommendations and diversity.
Tourism Based Hybrid Recommendation SystemIRJET Journal
This paper proposes a hybrid tourism recommendation system that combines collaborative filtering, content-based filtering, and aspect-based sentiment analysis to improve accuracy and address cold start problems. The system analyzes user ratings and reviews to predict ratings for other tourism packages. It stores ratings, reviews, and sentiment information in a database to enhance recommendations. Results showed the hybrid approach increased efficiency over conventional methods. Future work could include testing on additional datasets and expanding the system.
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Philippine Edukasyong Pantahanan at Pangkabuhayan (EPP) CurriculumMJDuyan
(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
- Understand the goals and objectives of the Edukasyong Pantahanan at Pangkabuhayan (EPP) curriculum, recognizing its importance in fostering practical life skills and values among students. Students will also be able to identify the key components and subjects covered, such as agriculture, home economics, industrial arts, and information and communication technology.
𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
-Define entrepreneurship, distinguishing it from general business activities by emphasizing its focus on innovation, risk-taking, and value creation. Students will describe the characteristics and traits of successful entrepreneurs, including their roles and responsibilities, and discuss the broader economic and social impacts of entrepreneurial activities on both local and global scales.
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There are most common types of recommendation
systems are as follows:
a) Content-based Filtering Recommendation and
b) Collaborative Filtering Recommendation
c) Hybrid Filtering Recommendation
a) Content-based Filtering Recommendation
This filtering recommends things to user dependent
on his past experience. For instance, assuming a user
loves just activity movie, the framework predicts his
solitary activity movies like it which he has
profoundly appraised. Thing/content for the most part
indicated by watchwords. It just spotlights a solitary
user's thoughts, contemplations and gives
expectations dependent on his advantage.
Fig 2: Content-based Filtering Recommendation
b) Collaborative Filtering Recommendation
Collaborative Filtering procedures make suggestions
for a user dependent on ratings and conduct
information of numerous users. Assuming two users
have both preferred certain regular things, the things
that one user has loved that the other user. More
users, more ratings: better outcomes.
Fig 3: Collaborative Filtering Recommendation
c) Hybrid Filtering Recommendation
It is a mix of the two strategies for example Shared
collaborative filtering recommendation framework
and content-based filtering recommendation
framework. This hybrid filtering accepts ratings of the
movie as the contribution from the users and
afterward applies the collaborative filtering and
content-based filtering and produces a suggestion list.
Fig 4: Hybrid Filtering Recommendation
II. Problem Statement
The propose framework carrying out a crossover
suggestion framework for movie proposals dependent
on user decision, choice, interest, preferences, and
behaviour that develop the properties of the past
framework with a novel methodology and extra fit
methodology that abatement the framework run-time
and decides thing relationship with immense
flawlessness.
III. Literature Survey
Pooja Khalokar, et al. [1] Portray a model is proposed
to tackle the movement suggestion issue by dealing
with the fascination subject, vacationer inclination,
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Minal V. Jamnekar et al Int J Sci Res Sci & Technol. May-June-2021, 8 (3) : 543-550
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and feeling of the attractions. It will take constant
information at that point examine it and utilize the
information for ongoing applications to suggest the
vacationer's real right intrigued information. The
proposed Sentiment information model can be
additionally applied for the voyaging suggestion
applications, like fascination suggestion, traveler
suggestion, etc. In future work, it will attempt to
interface all information accessible on various social
sites like Facebook, Twitter, and other social sites
suggest them just as their companions also.
Pooja Khalokar, et al. [2] Portray the framework takes
constant information at that point investigates it, and
utilizations the information for ongoing applications
to suggest the vacationer's real right intrigued
information. The framework gives the decision
among three techniques rating suggestion, assessment
proposal, and fascination suggestion. It gives bundle
gauging to visit organizations and ideas. Later on, it
will attempt to interface all information accessible on
various social sites like Facebook, Twitter, and other
social sites suggest them just as their companions also.
Heng-Ru Zhang, et al. [3] In this paper, we have
proposed a half-breed recommender framework for
the intelligent situation. Through changing the
suggested boundaries and contrasting the irregular
and half and half calculations, we may reach the
accompanying determinations: (1) the proportion of
arbitrary proposals has no extraordinary effect on the
presentation as long as it isn't too huge (e.g., not more
than 0.25), (2) one should utilize 𝑘NN as ahead of
schedule as could really be expected, (3) the
neighbor's number ought to be sufficiently large (e.g.,
45), (4) the review is almost direct increment as for
the number of proposals in each round, and (5) the
crossover calculation is superior to the arbitrary one.
Cai Chen, et al. [4] In this paper, we proposed another
unique user versatile mix technique for crossbreed
film suggestion. Contrasting and the customary half-
breed calculation, which utilizes a static blend
technique to join the unadulterated calculations, our
proposed calculation utilizes a unique procedure that
consolidates with the distinctive boundary for various
users. Investigations show that our proposed
calculation can essentially upgrade the exhibition.
Moreover, in this paper, we utilize the outside open
asset IMDB as the substantial information on the film,
extraordinarily, we utilize the watchwords set as the
contribution of the substance-based calculation. Tests
show that the catchphrases set gathered from IMDB is
a great asset for a suggestion. For future work,
considering the dataset accessible is developing so
quickly, we will attempt to broaden the versatility of
our proposed calculation to suggest the adaptable
dataset. We will likewise attempt to take more sorts
of assets past the catchphrases set as the contribution
of the substance-based algorithm.
Sajal Halder, et al. [5] In this paper, we have shown
that users' evaluating-based diversion suggestion
framework doesn't give high precision in the proposal
cycle. It is similarly imperative to utilize the elements
of user conduct after some time. To suggest a thing we
have proposed a calculation that utilized the
adjustment of the user's inclinations over the long
run. At the end of the day, the calculation gives
greater need to the as of late evaluated things for
proposal. The relationship of time data gives better
precision. Simultaneously, we have proposed a
mining procedure to prescribe things to the makers
with the end goal that they can anticipate users'
chose.
Harris Papadakis, et al. [6] Introduced a customized
film recommender framework application, created for
Android-worked versatile stages. As per our insight,
MovieScore is the main portable application in
writing customized film proposals. The proposed
framework depends on a novel, cutting-edge
calculation for customized proposals that shows
dynamic gradual update conduct. In this way, when
another thing shows up (for example film, user,
rating), the calculation is retrained steadily, yielding
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Minal V. Jamnekar et al Int J Sci Res Sci & Technol. May-June-2021, 8 (3) : 543-550
546
elite and versatile outcomes. The application is not
difficult to utilize and gives exact film suggestions to
the user on the fly, in light of its customized
inclinations. It additionally gives utility
functionalities as the development of a "to watch"
film rundown and refining the proposal results
through separating.
We expect to vigorously enhance the basic
recommender calculation, primarily by fusing film
metadata in its usefulness. This won't just take into
account more precise suggestions yet additionally
empower the application to legitimize to the user
every proposal provided.
Tianqi Zhou, et al. [7] portray explicit issues of the
film proposal, we utilize the Hadoop programming
model to execute a suggestion calculation depends on
the Item-based CF. Also, we give the
acknowledgment of the framework and the
comparing exploratory outcomes. The circulated
record framework HDFS and conveyed bunch
structure MapReduce on the Hadoop stage can store
the developing mass of information as well as cycle
the information in equal, which improves the
presentation of the calculation and the reaction speed
of the framework. We accept that the framework that
receives the Hadoop procedure can address the issue
of huge information and the distributed computing
climate.
Jiang Zhang, et al. [8] Fostered a novel cooperative
sifting approach called Weighted KM-Slope-VU for
quick and adaptable film suggestions, and besides
created and conveyed a customized film proposal site,
MovieWatch, to furnish users with survey
administrations and gather user input on prescribed
movie to basically assess our proposed calculation
utilizing genuine information. In particular, we
received k-intends to parcel users into a few bunches,
and afterward for each group imagined a virtual
assessment pioneer to address the entirety of the users
around there. At that point, rather than handling the
first full user thing rating framework, a decreased
virtual assessment pioneer thing grid is prepared by
the proposed Weighted Slope One-VU suggestion
calculation. Analyses of MovieLens datasets show that
our plan can accomplish the execution (estimated by
RMSE) practically identical with proposal calculations
depend on framework factorization, yet diminish
time intricacy in like manner situations. Moreover, a
common-sense film suggestion framework called
MovieWatch was created, conveyed, and opened to
the general society to gather user input on the movie
pictures prescribed to them. Our plan was then
assessed depending on this genuine criticism by
enlisting users of MovieWatch.
IV. Propose System
The proposed framework will contain a data set
comprising numerous movies. Continuous
investigation guarantees that the framework will
adjust progressively dependent on user behaviour.
Things are suggested dependent on examinations
between thing profiles and user profiles. We can
undoubtedly clarify the working of the recommender
framework by posting the Content highlights of a
thing.
Content-based recommender frameworks use need
just the rating of the concerned user and no other
user of the framework. It is subject to the connection
between users, which suggests that it is content-free.
Fig 5: System Architecture
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547
Flowchart
Fig 6: Flowchart
Algorithm
Gradient descent is applied to the MovieLens
informational collection to remove dormant
highlights, one of which takes the movie and user
predisposition into thought. While looking at the
highlights separated from the algorithms there was a
solid connection betweens extricated highlights and
movie sorts. We show that each element form a
particular classification of movie where every movie
is addressed as a mix of the classes. It works by
circling through each evaluation and movie in the
preparation information, attempts to foresee the top
max rating and comparable movie.
Input: Training Dataset
Step 1: Initialize matrices p & q of size (users * K) & (K
* movies with irregular qualities) from a uniform
dispersion over [0, 1] where K represents the quantity
of highlights that will be removed.
Step 2: Iterate over every one of them noticed ratings
in the preparation dataset.
(a) Calculate an anticipated rating relating to a
genuine rating.
(b) Calculate the mistake for the anticipated rating.
(c) Update p and q as indicated by mistake.
Output: Resulset <class_name, similarity_movie,
highest_rating>
Dataset
The Movie dataset contains 1 million user ratings
from 6,040 users on 17770 movies. Users of
MovieLens were chosen arbitrarily. All users
appraised in any event 10 movies. Every user
addressed by an exceptional id.
V. Result
The best generally results are reached by the thing-
by-thing-based methodology. It needs 170 seconds to
build the model and 3 seconds to anticipate 100,021
ratings. We need a measurement to score or rate a
movie. Figure the score for each movie. Sort the
scores and prescribe the best-evaluated movie to
users.
The best by and large results are reached by the
thing- by-thing-based technique. It needs 30 seconds
to assemble the model and 3 seconds to expect 10
movies and rating (evaluations). We need an
estimation to score or rate a movie. Compute the
score for every movie. Sort the scores and endorse the
best-assessed movie to users.
Fig 7: Python Command Prompt (Step 1)
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Minal V. Jamnekar et al Int J Sci Res Sci & Technol. May-June-2021, 8 (3) : 543-550
548
Fig 8: Login Page (Step 2)
Fig 9: Home Page (Step 3)
Fig 10: Browse Dataset (Step 4)
Fig 11: Processing (Step 5)
Fig 12: Command Prompt Output – Movie
Recommendation (Step 6)
Fig 13: Browser Output – Movie Recommendation
(Step 7)
Fig 14: Browser Output – Movie Recommendation
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Fig 15: Browser Output – Movie Recommendation
Fig 16: RMSE Graph
VI. Conclusion
In this paper, we proposed another unique user
versatile hybrid technique for movie
recommendations. A hybrid recommendations
framework that will utilize item-based and user-based
separating to give customized suggestions. Notion
examination dependent on movie audits and will
likewise join a success indicator to appraise the
achievement pace of impending movie dependent on
different boundaries. The clearest thought is to add
highlights to recommend movies with regular
entertainers, chiefs, or journalists. The hybrid
recommendation approach conquers the downsides of
every individual calculation and improves the
exhibition of the framework.
VII.Future Scope
A hybrid recommendation framework can be planned
by adding more highlights of information to give a
suggestion. Later on, we can deal with a hybrid
recommender using grouping and equivalence for
better execution. Our approach can be furthermore
loosened up to various spaces to propose songs, video,
setting, news, books, the travel business, and web
business objections, etc.
VIII. REFERENCES
[1]. Pooja P. Khalokar, Prof. Sneha U. Bohra,
“Review on Personalized Travel
Recommendation According to User Interest
using Sentiment Analysis for the Growth of
Indian Tourism”, International Journal of
Research and Analytical Reviews (IJRAR), June
2019.
[2]. Pooja P. Khalokar, Prof. Sneha U. Bohra,
“Sentiment Analysis & Tour Ratings for Best
Customized Visit Suggestions for the Expansion
of Indian Tourism”, International Journal of
Research and Analytical Reviews (IJRAR), Sept
2020.
[3]. Heng-Ru Zhang, FanMin, Xu He, and Yuan-
Yuan Xu, “A Hybrid Recommender System Based
on User-Recommender Interaction”, Hindawi
Publishing Corporation, Mathematical Problems
in Engineering, 2015.
[4]. Cai Chen, Daniel Zeng, “A Dynamic User
Adaptive Combination Strategy for Hybrid
Movie Recommendation”, IEEE, 2012.
[5]. Sajal Halder, Md. Hanif Seddiqui, and Young-
Koo Lee, “An Entertainment Recommendation
System using the Dynamics of User Behavior
over Time”, 17th International Conference on
Computer and Information Technology (ICCIT),
2014.
[6]. Harris Papadakis, Paraskevi Fragopoulou, Nikos
Michalakis, Costas Panagiotakis, “A Mobile
Application for Personalized Movie
8. International Journal of Scientific Research in Science and Technology (www.ijsrst.com) | Volume 8 | Issue 3
Minal V. Jamnekar et al Int J Sci Res Sci & Technol. May-June-2021, 8 (3) : 543-550
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Cite this article as :
Minal V. Jamnekar, Prof. Sneha U. Bohra, " A Hybrid
Approach for Movie Recommendation based on User
Behaviour", International Journal of Scientific
Research in Science and Technology(IJSRST), Print
ISSN : 2395-6011, Online ISSN : 2395-602X,Volume 8,
Issue 3, pp.543-550, May-June-2021. Available at
doi : https://doi.org/10.32628/IJSRST2183117
Journal URL : https://ijsrst.com/IJSRST2183117