Movie Recommendation System Using Collaborative Filtering

Wu, Ching-Seh; Garg, Deepti; Bhandary, Unnathi

doi:10.1109/icsess.2018.8663822

Cited by 56 publications

(13 citation statements)

References 5 publications

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“…Back propagation is used to train the framework. Wu et al [15] describe the use of a recommender system via two collaborative models. The paper uses the commonality between entities to build a system, using user and item collaborative model techniques.…”

Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: A novel hybrid feature combination method for enhanced movie recommendations with user resemblance and attitude mining

Lavanya

Bharathi

2021

Pers Ubiquit Comput

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Most movie recommendation methods use hard-clustering and simple collaborative filtering techniques in order to achieve their end results. However, these methods tend to overlook crucial aspects of both users and items. When these methods hard cluster a movie item into a cluster, they turn a blind eye to the fact that the item also exhibits some properties of another cluster’s items. Recommender systems facilitate users and relevant things expeditiously supported their requests and historic communications with alternative customers. Recommendation systems are a crucial portion of signifying things particularly in streaming amenities. For streaming motion-picture show services like Netflix, recommendation methods are vital for serving to users notice fresh movies to get pleasure from. However, massive amounts of information will turn out restrictions in recommendations due to accuracy as a result of diversity and meagerness problems. Our work proposes a unique hybrid technique that mixes collaborative filtering and characteristics of demographic filtering technique to point the close users, and associate against one another. This technique has been established over associate in tending analysis of the way to cut back the blunders in grading estimates supported users’ earlier communications that ends up in improved prediction accuracy in among completely different algorithms. Additionally, a feature combination technique is utilized that progresses the expectation accuracy and to check our method, using MovieLens 1M dataset, we contended an offline assessment, already available assessment tactics, and compared the same with the output factors to support authenticating the proposed procedure.

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Section: Related Workmentioning

confidence: 99%

RETRACTED ARTICLE: A novel hybrid feature combination method for enhanced movie recommendations with user resemblance and attitude mining

Lavanya

Bharathi

2021

Pers Ubiquit Comput

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“…Paper [1] discusses about different filtering algorithms and its usage. It mainly demonstrates collaborative filtering algorithm utilizing Apache Mahout which were used to construct a movie recommender system.…”

Section: II Work In This Areamentioning

confidence: 99%

Movie Recommendation System Using Machine Learning Algorithms

Jadhav¹,

KB²

2022

JMC

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Movies are one kind of enjoyment, however the challenge is locating the specified resources among the lots of movies discharged annually. To search out a show what users are seeking out through this technology is incredibly troublesome. However, recommendation structures return abundant handier in these things. Searching is the first step depending on which the movies are recommended to the user. To search a movie the user may use any of the parameter like name of the actor, director, genres etc. Searching based on one of the parameter and recommending movies based on the parameter is how the current recommendation system works. The movies are recommended with the help of filtering technique i,e collaborative filtering which recommends the movies based on user preference and ratings of the movies. This can be further enhanced by taking multiple parameters (2 in this case) for searching the movies based on which the recommendation can be done. With the help of multiple parameters (2 in this case) the user can be more specific in what he wants to search so that it helps in more accurate recommendations of the movies.

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“…However, there is always an opportunity for improvement. In [10] authors executed a movie recommendation using collaborative filtering. This system is created using Apache Mahout and evaluates the ratings to give movie recommendations.…”

Section: Eai Endorsed Transactions On Creative Technologiesmentioning

confidence: 99%

Movie Recommender System using Machine Learning

Malik

2022

EAI Endorsed Trans Creat Tech

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In this research, we propose a movie recommender system that can recommend movies to both new and existing customers. It searches movie databases for all of the relevant data, such as popularity and beauty that is required for a recommendation. We apply both content-based and collaborative filtering and evaluate their advantages and disadvantages. To build a system that delivers more exact movie recommendations, we employ hybrid filtering, which is a combination of the outcomes of these two processes. The recommendation engines are also used for business purposes and to make strategies for organizations. Due to the growing demands of customers and user’s recommendation systems plays a huge role. These recommender systems also help us to utilize our time in the busy world by giving us more relevant searches. These systems are generally used with the movie’s websites or with many commercial applications and are of great use. This type of recommendation system can be also used for precise results. It will make movies suggestions more relevant as per the need of the users.

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Movie Recommendation System Using Collaborative Filtering

Cited by 56 publications

References 5 publications

RETRACTED ARTICLE: A novel hybrid feature combination method for enhanced movie recommendations with user resemblance and attitude mining

RETRACTED ARTICLE: A novel hybrid feature combination method for enhanced movie recommendations with user resemblance and attitude mining

Movie Recommendation System Using Machine Learning Algorithms

Movie Recommender System using Machine Learning

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