Analysis of Movie Recommendation Systems; with and without considering the low rated movies

Reddy, Muppana Mahesh; Kanmani, R. Sujithra; Surendiran, B.

doi:10.1109/ic-etite47903.2020.453

Cited by 22 publications

(3 citation statements)

References 13 publications

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“…IOM is used to escalate the performance of the systems. Reddy et al [ 12 ] show the effect of ignoring movies that have not gotten an average rating. The model of the movie of the user is done after taking into account all the different movies, then the predictions of the movies of the user are done after neglecting movies that have not gotten an average rating, and the predictions are compared with the predictions when all the movies are taken.…”

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RETRACTED ARTICLE: A novel hybrid feature combination method for enhanced movie recommendations with user resemblance and attitude mining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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“…Based on customer past purchases, consumer browsing habits, and user segments, author analyse and evaluate three variants of a CF-based recommender system. In [6] procedure used by the authors was collaborative filtering and the similarity measured used was the Pearson correlation coefficient. The dataset was taken from Movie-Lens-100k and the ratings above 2.5 was taken into consideration for 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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“…With the new products or the products that have low ratings from users, the recommendation system suffers from the sparsity problem [7]. This is where insufficient transactional and feedback data are available for inferring specific user's similarities, which affects the accuracy and performance of the recommender system [8], [9]. However, many works [10]- [12] demonstrate that other related information, such as user's demographics, incomes, or education level, can be integrated into the recommendation system.…”

Section: Introductionmentioning

confidence: 99%

Conceptual framework of recommendation system with hybrid method

Panyatip

Kaenampornpan

Chomphuwiset

2023

IJEECS

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Recommendation system relies on information of user preference and user behavior in order to recommend the useful information. The existing recommendation systems still have problems for new users and new items. This research proposes a new hybrid method to develop the conceptual framework of recommendation system that deals with new user and new movie data. The data used consists of a data from MovieLens and the internet movie database (IMDB). This work introduces a hybrid recommendation system which based on a combination of content-based filtering (CBF) and collaborative filtering (CF). Pre-filtering data is performed by finding an optimal number of clusters by calculating the total within cluster sum of square. In order to reduce the complexity of data and increase the relevance of the user-item ratings, the fuzzy c-mean (FCM) is employed. Then the similarity is calculated by using item-based method, the K-nearest neighbors and weight sum of the rating are applied. Finally, to recommend the movies, the research found that for new user data the precision is at 85% and mean absolute eror (MAE) value 2.1011. For new item data, the result of research obtains the precision at 87% and MAE value 2.0031. In conclusion, the new hybrid method developed can recommend movie efficiently.

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Analysis of Movie Recommendation Systems; with and without considering the low rated movies

Cited by 22 publications

References 13 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 Recommender System using Machine Learning

Conceptual framework of recommendation system with hybrid method

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