-
Chapter and Conference Paper
Enhancing Session-Based Recommendation with Multi-granularity User Interest-Aware Graph Neural Networks
Session-based recommendation aims at predicting the next interaction based on short-term behaviors within an anonymous session. Conventional session-based recommendation methods primarily focus on studying the...
-
Article
MIN: multi-dimensional interest network for click-through rate prediction
Click-through rate (CTR) prediction is a critical task in recommender systems and online advertising systems. The extensive collection of behavior data has become popular for building prediction models by capt...
-
Article
Dual attention composition network for fashion image retrieval with attribute manipulation
Due to practical demands and substantial potential benefits, there is growing interest in fashion image retrieval with attribute manipulation. For example, if a user wants a product similar to a query image an...
-
Chapter and Conference Paper
Thompson Sampling with Time-Varying Reward for Contextual Bandits
Contextual bandits efficiently solve the exploration and exploitation (EE) problem in online recommendation tasks. Most existing contextual bandit algorithms utilize a fixed reward mechanism, which makes it di...
-
Article
Detection-by-tracking of traffic signs in videos
Continuously detecting traffic signs in a video sequence is necessary for autonomous or assisted driving scenarios, since a vehicle needs the information from the signs to facilitate navigation. Single-image b...
-
Article
JointCTR: a joint CTR prediction framework combining feature interaction and sequential behavior learning
Click-through rate (CTR) is a positive feedback of user preferences or product purchases, and its small increase can bring huge benefits. Therefore, CTR prediction plays a key role in computing advertising and...
-
Chapter and Conference Paper
Learning Image Representation via Attribute-Aware Attention Networks for Fashion Classification
Attribute descriptions enrich the characteristics of fashion products, and they play an essential role in fashion image research. We propose a fashion classification model (M2Fashion) based on multi-modal data...
-
Article
Modeling low- and high-order feature interactions with FM and self-attention network
Click-Through Rate (CTR) prediction has always been a very popular topic. In many online applications, such as online advertising and product recommendation, a small increase in CTR will bring great returns. H...
-
Chapter and Conference Paper
An Integrated Time-Aware Collaborative Filtering Algorithm
Collaborative filtering is the type of algorithm that has the most variants and is currently the most widely used in recommender systems. The advantage is that it does not require much domain knowledge, and ca...
-
Chapter and Conference Paper
A Contextual Multi-armed Bandit Approach Based on Implicit Feedback for Online Recommendation
Contextual multi-armed bandit (CMAB) problems have gained increasing attention and popularity recently due to their capability of using context information to deliver recommendation services. In this paper, we...
-
Chapter and Conference Paper
MIRD-Net for Medical Image Segmentation
Medical image segmentation is a fundamental and challenging problem for analyzing medical images due to the approximate pixel values of adjacent tissues in boundary and the non-linear feature between pixels. A...
-
Chapter and Conference Paper
Automatic Sleep Staging Based on Deep Neural Network Using Single Channel EEG
Sleep staging is the first step for sleep research and sleep disorder diagnosis. The present study proposes an automatic sleep staging model, named ResSleepNet, using raw single-channel EEG signals. Most of th...
-
Chapter and Conference Paper
Unsupervised Deep Clustering for Fashion Images
In many visual domains like fashion, building an effective unsupervised clustering model depends on visual feature representation instead of structured and semi-structured data. In this paper, we propose a fas...
-
Chapter and Conference Paper
Community-Based Matrix Factorization Model for Recommendation
Although matrix factorization has been proven to be an effective recommendation method, its accuracy is affected by the sparsity of the matrix and it cannot resolve the cold start problem. Social recommendati...
-
Chapter and Conference Paper
NSPD: An N-stage Purchase Decision Model for E-commerce Recommendation
In this paper, we proposed a scalable framework W&D (wide & deep framework) plus to capture users’ personal interest for e-commerce recommender systems by combining the advantage of W&D and Residual Units. To ...
-
Chapter and Conference Paper
An Intelligent Field-Aware Factorization Machine Model
The widely-used field-aware factorization machines model (FFM) takes the interactions of all the text features into consideration which will lead to a large number of invalid calculations. An intelligent fiel...
-
Article
Open AccessDynamic epigenetic mode analysis using spatial temporal clustering
Differentiation of human embryonic stem cells requires precise control of gene expression that depends on specific spatial and temporal epigenetic regulation. Recently available temporal epigenomic data derive...
-
Chapter and Conference Paper
Hmfs: Efficient Support of Small Files Processing over HDFS
The storage and access of massive small files are one of the challenges in the design of distributed file system. Hadoop distributed file system (HDFS) is primarily designed for reliable storage and fast acces...
-
Chapter and Conference Paper
Study and Practice of an Improving Multi-path Search Algorithm in a City Public Transportation Network
Shortest path search is one of key problems for big-scale city public transportation network (CPTN) query system. Based on the current main search algorithms and data models on multi-path search, an improving ...
