A Literature Review of Recommendation Systems

Bhareti, Kushan; Perera, Shevon; Jamal, Shehan; Pallege, Manul Hiyare; Akash, Vishma; Wiieweera, Sihan

doi:10.1109/inocon50539.2020.9298450

Cited by 7 publications

(2 citation statements)

References 39 publications

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“…Hybrid recommendation system aims at improving the accuracy of recommendation by combining various recommendation algorithms. The common hybrid methods are weighted, cascaded, and so on [9]. Although the hybrid recommendation algorithm improves the accuracy of recommendation system, it also increases the computational complexity [10].…”

Section: A Recommendation Systemmentioning

confidence: 99%

GCN-Int: A Click-Through Rate Prediction Model Based on Graph Convolutional Network Interaction

Liu

Han

et al. 2021

IEEE Access

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Recommendation system has been paid growing attention in the academia community and industry community because it can solve the problem of information overload. Among a variety of methods, the click-through rate prediction model plays an important role in predicting user's attention to a specific item. To predict click-through rate, high-dimensional and sparse features are usually adopted, and the accuracy of the prediction result depends on the combination of high-order features to a great extent. Therefore, many methods have been proposed to find the low-dimensional representation from sparse high-dimensional original features, and the meaningful way of feature combination has also been mined to improve the accuracy of the model. However, the click-through rate prediction models generally have two problems. One is that they can't extract the feature interaction of non-Euclidean features very well. Another one is that it is hard to explain the inward meaning of feature interaction. In this paper, a GCN-int model based on the interaction of Graph Convolutional Network is proposed to solve the above problems. The proposed model simplifies the complex interaction among multiple features, gets a better representation of the interaction between high-order features, and improves the interpretability of feature interaction. The experimental results on the public movie recommendation dataset and our own IPTV movie recommendation dataset show that the proposed GCN-int model gets higher accuracy and efficiency compared with the state-of-the-art models.

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Section: A Recommendation Systemmentioning

confidence: 99%

GCN-Int: A Click-Through Rate Prediction Model Based on Graph Convolutional Network Interaction

Liu

Han

et al. 2021

IEEE Access

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“…However, collaborative filtering-based recommender systems typically encounter issues such as data sparsity, scalability, prediction inaccuracy, and recommendation accuracy [1]. [2], for instance, compares recommendation methodologies, whereas [3,4] classifies recommender systems that use AI algorithms. Furthermore, [4,5] categorizes the techniques based on suggestion factors.…”

Section: Introductionmentioning

confidence: 99%

CF Recommender System Based on Ontology and Nonnegative Matrix Factorization (NMF)

Mhammedi

Massari

Gherabi

et al. 2023

Lecture Notes in Networks and Systems

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Recommender systems are a kind of data filtering that guides the user to interesting and valuable resources within an extensive dataset. by providing suggestions of products that are expected to match their preferences. However, due to data overloading, recommender systems struggle to handle large volumes of data reliably and accurately before offering suggestions. The main purpose of this work is to address the recommender system's data sparsity and accuracy problems by using the matrix factorization algorithm of collaborative filtering based on the dimensional reduction method and, more precisely, the Nonnegative Matrix Factorization (NMF) combined with ontology. We tested the method and compared the results to other classic methods. The findings showed that the implemented approach efficiently reduces the sparsity of CF suggestions, improves their accuracy, and gives more relevant items as recommendations.

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Book recommendation system: reviewing different techniques and approaches

Devika,

Milton

2024

Int J Digit Libr

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A Literature Review of Recommendation Systems

Cited by 7 publications

References 39 publications

GCN-Int: A Click-Through Rate Prediction Model Based on Graph Convolutional Network Interaction

GCN-Int: A Click-Through Rate Prediction Model Based on Graph Convolutional Network Interaction

CF Recommender System Based on Ontology and Nonnegative Matrix Factorization (NMF)

Book recommendation system: reviewing different techniques and approaches

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