Movie Recommender System Using Parameter Tuning of User and Movie Neighbourhood via Co-Clustering

Airen, Sonu; Agrawal, Jitendra

doi:10.1016/j.procs.2023.01.096

Cited by 9 publications

(2 citation statements)

References 5 publications

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“…[75], [67], [81], [42], [68], [44], [82], [23], [45], [83], [31], [13], [77], [1], [35], [46], [47], [48], [14], [69], [74], [72], [51], [52], [88], [85], [87] Normalized Discounted Cumulative Gain (NDCG) 𝑁𝐷𝐶𝐺@𝐾 = 𝐷𝐶𝐺@𝑘 𝐼𝐷𝐶𝐺@𝐾 [7], [67], [22], [40], [70], [49], [76], [6], [89], [90], [91], [92] Precision 𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛(𝑢) = [39], [81], [22], [28], [12], [48], [76], [71], [89], [36], [97], [95], [98], [92...…”

Section: Below Inmentioning

confidence: 99%

Systematic Literature Review on Recommender System: Approach, Problem, Evaluation Techniques, Datasets

Saifudin,

Widiyaningtyas

2024

IEEE Access

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Recommender systems are needed with the presence of the internet and social media. The benefits that are felt in the recommender system can make it easier for users to find suitable products and recommend other products, specifically with lots of information. Recommender systems continue to develop over time. This has led many researchers to continue to find the latest approach and evaluation techniques by comparing the performance of previously existing recommender systems. The main approaches that are often used in recommender systems are Content-Based Filtering (CBF), Collaborative Filtering (CF), and Hybrid Filtering (HBF). This time, we focus on conducting a Systematic Literature Review (SLR) of several research articles and analyzing methods for algorithms developed in building recommender systems. The SLR method consists of three stages: planning, implementation, and reporting. The research used as a comparison is between 2019-2023 using various existing data sets. There were 72 primary studies where the Collaborative Filtering method was used in 46 studies, Content-Based Filtering was used in 11 studies, and the Hybrid Filtering method was used in 15 studies. The results of this SLR process show the advantages and disadvantages of each method and type of evaluation developed in building a recommender system. Apart from that, several challenges arise with various existing problems. However, the model-based collaborative filtering method is one method that can minimize the problems of cold start, data sparsity, and scalability.

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Section: Below Inmentioning

confidence: 99%

Systematic Literature Review on Recommender System: Approach, Problem, Evaluation Techniques, Datasets

Saifudin,

Widiyaningtyas

2024

IEEE Access

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“…These systems use various approaches of the machine and deep learning [5,6], matrix completion or factorization [7][8][9][10][26][27][28], and lately, GNNs to recommend movies [3,[11][12][13][14]. In the following, we divide these approached into three categories: collaborative filter [1,2,4,8,9,15,16], content filter [4,17,19,22], and hybrid [20,21,29,30].…”

Section: Literature Reviewmentioning

confidence: 99%

MoRGH: Movie Recommender System using GNNs on Heterogeneous Graphs

Ziaee,

Rahmani,

Nazari

2024

Preprint

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Nowadays, with the advent of movies and TV shows and the competition between different movie streamer companies and movie databases to attract more users, movie recommenders have become a major prerequisite for customer satisfaction. Most of the previously introduced methods used collaborative, content-based, and hybrid filtering techniques, where neural network-based approaches, and matrix completion are the major approaches of most recent movie recommender systems. The major drawbacks of previous systems are not considering side information, cold start problem, and neglecting valuable information hidden when the data is presented in a heterogeneous graph like movie-movie or user-user information. In this paper, we propose a novel inductive approach called MoRGH which first constructs a graph of similar movies by considering the information available in movies’ plot synopsis and genres. Second, constructs a heterogeneous graph of users and movies by taking into account the movies’ rating information available in the Movielens dataset. Third, mitigates the drawbacks of previous methods by performing a GNN and GAE-based model to exploit a hybrid approach of collaborative and content-based approaches. Finally recommends, accurate and more personalized recommendations for each user, which outperforms previous state-of-the-art models w.r.t RMSE scores with an average of 0.1 based on movie ratings.

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Learning Rate Scheduler for Multi-criterion Movie Recommender System

Airen,

Agrawal

2024

Lecture Notes in Networks and Systems

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Movie Recommender System Using Parameter Tuning of User and Movie Neighbourhood via Co-Clustering

Cited by 9 publications

References 5 publications

Systematic Literature Review on Recommender System: Approach, Problem, Evaluation Techniques, Datasets

Systematic Literature Review on Recommender System: Approach, Problem, Evaluation Techniques, Datasets

MoRGH: Movie Recommender System using GNNs on Heterogeneous Graphs

Learning Rate Scheduler for Multi-criterion Movie Recommender System

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