Feature selection for movie recommendation

Çataltepe, Zehra; Uluyagmur, Mahiye; Tayfur, Esengül

doi:10.3906/elk-1303-189

Cited by 16 publications

(6 citation statements)

References 32 publications

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“…Generally, the cluster's discrimination ability and the cluster's performance depends on dimensionality reduction and it was performed in two ways (i) Feature selection, and (ii) Instance selection. Cataltepe et al [13] has developed a new feature selection method for Turkish movie recommendation system. The developed method practices user behavior, various kinds of content features, and other users' message to predict the movie ratings.…”

Section: Literature Reviewmentioning

confidence: 99%

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Ensemble Learning Based Collaborative Filtering with Instance Selection and Enhanced Clustering

Parthasarathy¹,

Devi²

2022

Computers, Materials &Amp; Continua

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Recommender system is a tool to suggest items to the users from the extensive history of the user's feedback. Though, it is an emerging research area concerning academics and industries, where it suffers from sparsity, scalability, and cold start problems. This paper addresses sparsity, and scalability problems of model-based collaborative recommender system based on ensemble learning approach and enhanced clustering algorithm for movie recommendations. In this paper, an effective movie recommendation system is proposed by Classification and Regression Tree (CART) algorithm, enhanced Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) algorithm and truncation method. In this research paper, a new hyper parameters tuning is added in BIRCH algorithm to enhance the cluster formation process, where the proposed algorithm is named as enhanced BIRCH. The proposed model yields quality movie recommendation to the new user using Gradient boost classification with broad coverage. In this paper, the proposed model is tested on Movielens dataset, and the performance is evaluated by means of Mean Absolute Error (MAE), precision, recall and f-measure. The experimental results showed the superiority of proposed model in movie recommendation compared to the existing models. The proposed model obtained 0.52 and 0.57 MAE value on Movielens 100k and 1M datasets. Further, the proposed model obtained 0.83 of precision, 0.86 of recall and 0.86 of f-measure on Movielens 100k dataset, which are effective compared to the existing models in movie recommendation.

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Section: Literature Reviewmentioning

confidence: 99%

“…Pr ecision = Number of correct recommendations relevent to the total query number of recommendations (13) recall = number of correct recommendation total number of relevant recommendation ( 14)…”

Section: Performance Measurementioning

confidence: 99%

Ensemble Learning Based Collaborative Filtering with Instance Selection and Enhanced Clustering

Parthasarathy¹,

Devi²

2022

Computers, Materials &Amp; Continua

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“…Another recommendation system example, focusing mainly on movies, is the work of Çataltepe et al [19], in which different content-based and collaborative recommendation methods for a Turkish movie recommendation system are evaluated and combined. Movie description, actors, directors, years, and genre data are gathered for their recommendation tool.…”

Section: Literature Surveymentioning

confidence: 99%

User profiling for TV program recommendation based on hybrid television standards using controlled clustering with genetic algorithms and artificial neural networks

Topalli¹,

Kılınç²

2020

Turk J Elec Eng & Comp Sci

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In this paper, an earlier method proposed by the authors to make smart recommendations utilizing artificial intelligence and the latest technologies developed for the television area is expanded further using controlled clustering with genetic algorithms (CCGA). For this purpose, genetic algorithms (GAs), artificial neural networks (ANNs), and hybrid broadcast broadband television (HbbTV) are combined to get the users' television viewing habits and to create profiles. Then television programs are recommended to the users based on that profiling. The data gathered by the developed HbbTV application for previous studies are reused in this study. These data are employed to cluster users. The number of clusters is found by CCGA, a method proposed in this paper. For each cluster formed by CCGA, a separate ANN is designed to learn the viewing habits of the users of the corresponding cluster. The weight matrices are initialized also by GA. The recommendations produced using the proposed model are then presented by the same HbbTV application developed by the authors. Clustering with GAs gives better results when compared to the well-known K-means clustering algorithm.

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“…Their proposal is focused on content-based systems and presents an RS that classifies restaurants by priority of order. Cataltepe et al [4] performed FS on each user's profile to make predictions about movie ratings. Unlike the previous works, we will analyze the impact of the FS when it comes to making the recommendation more understandable and knowing what users take into account when choosing a restaurant.…”

Section: Introductionmentioning

confidence: 99%

The role of feature selection in personalized recommender systems

Bagué-Masanés¹,

Bolón-Canedo²,

Remeseiro³

2022

ESANN 2022 Proceedings

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Recommender systems suggest products to users, based on their popularity or the users' preferences. This paper proposes a hybrid personalized recommender system based on users' tastes and also on information available about items. We used a dataset downloaded from Tri-pAdvisor, which contains some information from restaurants (items), such as price range or special diets. Feature selection techniques are employed to analyze the impact that each variable has on personalized recommendations, allowing us to understand not only the process underlying the recommendation to favor the transparency of the system, but also what users value the most when choosing a restaurant.

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Feature selection for movie recommendation

Cited by 16 publications

References 32 publications

Ensemble Learning Based Collaborative Filtering with Instance Selection and Enhanced Clustering

Ensemble Learning Based Collaborative Filtering with Instance Selection and Enhanced Clustering

User profiling for TV program recommendation based on hybrid television standards using controlled clustering with genetic algorithms and artificial neural networks

The role of feature selection in personalized recommender systems

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