DHSIRS: a novel deep hybrid side information-based recommender system

Yengikand, Amir Khani; Meghdadi, Majid; Ahmadian, Sajad

doi:10.1007/s11042-023-15021-9

Cited by 8 publications

(2 citation statements)

References 55 publications

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“…Among the selected models, LDA, TF-IDF, SVM, CF, and MF emerged as the top five most frequently mentioned models, appearing in over 500 papers. It is important to note that while some recently gained substantial attention, such as BERT [34], CF [95], LSTM [96,97], DNN [98], and GRU [99,100], our study encompasses models from various time periods.…”

Section: Model Trendsmentioning

confidence: 99%

Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review

Farshidi,

Rezaee,

Mazaheri

et al. 2023

Preprint

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Context: User intent modeling is a crucial process in Natural Language Processing that aims to identify the underlying purpose behind a user’s request, enabling personalized responses. With a vast array of approaches introduced in the literature (over 13,000 papers in the last decade), understanding the related concepts and commonly used models in AI-based systems is essential. Method: We conducted a systematic literature review to gather data on models typically employed in designing conversational recommender systems. From the collected data, we developed a decision model to assist researchers in selecting the most suitable models for their systems. Additionally, we performed two case studies to evaluate the effectiveness of our proposed decision model. Results: Our study analyzed 59 distinct models and identified 74 commonly used features. We provided insights into potential model combinations, trends in model selection, quality concerns, evaluation measures, and frequently used datasets for training and evaluating these models. Contribution: Our study contributes practical insights and a comprehensive understanding of user intent modeling, empowering the development of more effective and personalized conversational recommender systems. With the Conversational Recommender System, researchers can perform a more systematic and efficient assessment of fitting intent modeling frameworks.

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Section: Model Trendsmentioning

confidence: 99%

Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review

Farshidi,

Rezaee,

Mazaheri

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…One of the common strategies to combine features extracted from different networks is to use a fusion layer [3,17]. In this paper, different from other works, we use two types of attention mechanisms to merge the features extracted by the feature extraction component.…”

Section: B Feature Composition Componentmentioning

confidence: 99%

Facial Expression Recognition Based on Separable Convolution Network and Attention Mechanism

Khani Yengikand,

Farrokhi Afsharyan,

Nejati

2023

itrc

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Facial expression recognition using deep learning methods has been one of the active research fields in the last decade. However, most of the previous works have focused on the implementation of the model in the laboratory environment, and few researchers have addressed the real-world challenges of facial expression recognition systems. One of the challenges of implementing the face recognition system in the real environment (e.g. webcam or robot) is to create a balance between accuracy and speed of model recognition. Because, increasing the complexity of the neural network model leads to an increase in the accuracy of the model, but due to the increase in the size of the model, the recognition speed of the model decreases. Therefore, in this paper, we propose a model to recognize the seven main emotions (Happiness, sadness, anger, surprise, fear, disgust and natural), which can create a balance between accuracy and recognition speed. Specifically, the proposed model has three main components. First, in the feature extraction component, the features of the input images are extracted using a combination of normal and separable convolutional networks. Second, in the feature integration component, the extracted features are integrated using the attention mechanism. Finally, the merged features are used as the input of the multi-layer perceptron neural network to recognize the input facial expression. Our proposed approach has been evaluated using three public datasets and images received via webcam

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A Robust Approach for Hybrid Personalized Recommender Systems

Nam

2023

Lecture Notes in Computer Science

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DHSIRS: a novel deep hybrid side information-based recommender system

Cited by 8 publications

References 55 publications

Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review

Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review

Facial Expression Recognition Based on Separable Convolution Network and Attention Mechanism

A Robust Approach for Hybrid Personalized Recommender Systems

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