Shopping Recommendation System Design Based On Deep Learning

Li, Haihan; Qi, Guanglei; He, Nana; Xinri, Dong

doi:10.1109/icsp51882.2021.9409009

Cited by 6 publications

(4 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By combining explicit and implicit user feedback, user profiles, and item content data, recommendations are made using different content generated by different users. At the model level, content-based recommendation algorithms and collaborative filtering algorithms are combined [22] 4. Applying deep learning in social network-based recommendation systems [23].…”

Section: Collaborative Filteringmentioning

confidence: 99%

The News Delivery Channel Recommendation Based on Granular Neural Network

Liu

2023

Preprint

View full text Add to dashboard Cite

With the continuous maturation and expansion of neural network technology, deep neural networks have been widely utilized as the fundamental building blocks of deep learning in a variety of applications, including speech recognition, machine translation, image processing, and the creation of recommendation systems. Therefore, many re-al-world complex problems can be solved by the deep learning techniques. As is known, traditional news recommendation systems mostly employ techniques based on collaborative filtering and deep learning, but the performance of these algorithms is constrained by the sparsity of the data and the scalability of the approaches. In this paper, we propose a recommendation model using granular neural network model to recommend news to appropriate channels by analyzing the properties of news. Specifically, a specified neural network serves as the foundation for the granular neural network that the model is considered to be build. Different information granularities are attributed to various types of news material, and different information granularities are released between networks in various ways. When processing data, granular output is created, which is compared to the interval values pre-set on various plat-forms and used to quantify the analysis's effectiveness. The analysis results could help the media to match the proper news in depth, maximize the public attention of the news and the utilization of media resources.

show abstract

Section: Collaborative Filteringmentioning

confidence: 99%

The News Delivery Channel Recommendation Based on Granular Neural Network

Liu

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Haihan et al [78] developed a data crawler to obtain user reviews and product information from the database of a mall. A CNN-based model then extracts salient features to learn and predict matching rates between users and commodities.…”

Section: Cnnmentioning

confidence: 99%

A Systematic Study on a Customer’s Next-Items Recommendation Techniques

et al. 2022

View full text Add to dashboard Cite

A customer’s next-items recommender system (NIRS) can be used to predict the purchase list of a customer in the next visit. The recommendations made by these systems support businesses by increasing their revenue and providing a more personalized shopping experience to customers. The main objective of this paper is to provide a systematic literature review of the domain to analyze the recent techniques and assist future research. The paper examined 90 selected studies to answer the research questions concerning the key aspects of NIRSs. To this end, the main contribution of the paper is that it provides detailed insight into the use of conventional and deep learning techniques, the popular datasets, and specialized metrics for developing and evaluating these systems. The study reveals that conventional machine learning techniques have been quite popular for developing NIRSs in the past. However, more recent works have mainly focused on deep learning techniques due to their enhanced ability to learn sequential and temporal information. Some of the challenges in developing NIRSs that need further investigation are related to cold start, data sparsity, and cross-domain recommendations.

show abstract

“…The authors of [ 11 ] used dynamic time warping to distinguish characters based on their temporal data similarity. However, due to their wide range of application, from finance to construction, from information technology to healthcare application, machine learning methods are becoming increasingly prominent in handwritten character recognition [ 21 , 22 , 23 , 24 , 25 ]. In [ 12 ], a GAN-based machine learning method was used to solve a classification problem, while [ 8 ] describes the use of a deep-learning-trained neural network method to collect sufficient data.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Character Recognition from Handwriting Motion Data Captured Using IMU and Force Sensors

Alemayoh

Shintani

Lee

et al. 2022

Sensors

View full text Add to dashboard Cite

Digitizing handwriting is mostly performed using either image-based methods, such as optical character recognition, or utilizing two or more devices, such as a special stylus and a smart pad. The high-cost nature of this approach necessitates a cheaper and standalone smart pen. Therefore, in this paper, a deep-learning-based compact smart digital pen that recognizes 36 alphanumeric characters was developed. Unlike common methods, which employ only inertial data, handwriting recognition is achieved from hand motion data captured using an inertial force sensor. The developed prototype smart pen comprises an ordinary ballpoint ink chamber, three force sensors, a six-channel inertial sensor, a microcomputer, and a plastic barrel structure. Handwritten data of the characters were recorded from six volunteers. After the data was properly trimmed and restructured, it was used to train four neural networks using deep-learning methods. These included Vision transformer (ViT), DNN (deep neural network), CNN (convolutional neural network), and LSTM (long short-term memory). The ViT network outperformed the others to achieve a validation accuracy of 99.05%. The trained model was further validated in real-time where it showed promising performance. These results will be used as a foundation to extend this investigation to include more characters and subjects.

show abstract

Shopping Recommendation System Design Based On Deep Learning

Cited by 6 publications

References 2 publications

The News Delivery Channel Recommendation Based on Granular Neural Network

The News Delivery Channel Recommendation Based on Granular Neural Network

A Systematic Study on a Customer’s Next-Items Recommendation Techniques

Deep-Learning-Based Character Recognition from Handwriting Motion Data Captured Using IMU and Force Sensors

Contact Info

Product

Resources

About