Movie Recommendation System Using Bag of Words and Scikit-Learn

Bhattacharya, Sounak; Lundia, Ankit

doi:10.33564/ijeast.2019.v04i05.076

Cited by 5 publications

(2 citation statements)

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“…In this research, a deep learning model for recommendation systems is proposed by integrating Convolutional Neural Network and Matrix Factorization to add extra information and extract contexts before training, attempting to enhance recommendation accuracy and context understanding. Despite substantial previous efforts [21,63,64], this study adds additional information on both user and item description documents and applied Convolutional Neural Networks to efficiently capture their local features. Furthermore, this research adds bias to the observed ratings to avoid overfitting issues and uses Matrix Factorization to create relationships between users and items.…”

Section: Discussionmentioning

confidence: 99%

“…In order to address the above data sparseness limitation, in this paper, different factors have been added to the recommendation system such as user information, user interactions, and product description documents instead of only using review data, attempting to enhance the accuracy of the system. Moreover, traditional information retrieval methods mostly use the bag-of-words model, which ignores the context information of the text document [21].…”

Section: Introductionmentioning

confidence: 99%

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A Deep Learning Model for Context Understanding in Recommendation Systems

Hien,

Huy,

Manh

et al. 2024

IJCAI

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Due to the robust growth in the amount of data and Internet users, there has been a significant rise in information overload, hindering timely access to user demand. While information retrieval systems, such as Google, Bing, and Altavista have partially addressed this challenge, prioritization and personalization of information have yet to be fully implemented. Therefore, recommendation systems are developed to resolve the issue by filtering and segmenting important information from an enormous volume of data based on different criteria such as preferences, interests, and user behaviors. By collecting data on users' interests and purchased products, the system can predict whether a particular user would enjoy an item, thus delivering an appropriate suggestion strategy. However, the increased number of Internet users and items has resulted in sparseness in increasingly vast datasets, reducing the performance of recommendation algorithms. Therefore, this study developed a model integrating Convolutional Neural Network (CNN) and Matrix Factorization (MF) to add extra product and user information, extract contexts, and add bias to the observed ratings in the training process, attempting to enhance the recommendation accuracy and context understanding. This approach can take advantage of CNN to efficiently capture an image's or document's local features, with the combination of MF to create relationships between 2 main entities, users and items. The proposed model obtained the highest RMSE of 0.93 when predicting favorable movies for 4,000 users, with an ability to learn complex contextual features and suggest more relevant content. The results are promising and can act as a reference for developing context understanding in recommendation systems, and future work may focus on optimizing the performance and developing more text-processing techniques.Povzetek: Razvit je nov model globokega učenja, ki združuje konvolucijske nevronske mreže (CNN) in matrično faktorizacijo (MF) za izboljšanje natančnosti in razumevanja konteksta v priporočilnih sistemih.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%