Predicting Dynamic User–Item Interaction with Meta-Path Guided Recursive RNN
Y Liu, C Yin, J Li, F Wang, S Wang - Algorithms, 2022 - mdpi.com
Accurately predicting user–item interactions is critically important in many real applications,
including recommender systems and user behavior analysis in social networks. One major …
including recommender systems and user behavior analysis in social networks. One major …
Recursive RNN based shift representation learning for dynamic user-item interaction prediction
C Yin, S Wang, J Du, M Zhang - … 2020, Foshan, China, November 12–14 …, 2020 - Springer
Accurately predicting user-item interactions is critically important in many real applications
including recommender systems and user behavior analysis in social networks. One …
including recommender systems and user behavior analysis in social networks. One …
Dynamic embeddings for interaction prediction
In recommender systems (RSs), predicting the next item that a user interacts with is critical
for user retention. While the last decade has seen an explosion of RSs aimed at identifying …
for user retention. While the last decade has seen an explosion of RSs aimed at identifying …
Recursive LSTM with shift embedding for online user-item interaction prediction
Online businesses are ubiquitous nowadays. Accurately predicting the online user-item
interactions such as buying, browsing, and price comparison is critically important to many e …
interactions such as buying, browsing, and price comparison is critically important to many e …
Predictive analysis by leveraging temporal user behavior and user embeddings
The rapid growth of mobile devices has resulted in the generation of a large number of user
behavior logs that contain latent intentions and user interests. However, exploiting such data …
behavior logs that contain latent intentions and user interests. However, exploiting such data …
Learning Neighbor User Intention on User–Item Interaction Graphs for Better Sequential Recommendation
The task of sequential recommendation aims to predict a user's preference by analyzing the
user's historical behaviours. Existing methods model item transitions through leveraging …
user's historical behaviours. Existing methods model item transitions through leveraging …
Local low-rank Hawkes processes for modeling temporal user–item interactions
Hawkes processes have become very popular in modeling multiple recurrent user–item
interaction events that exhibit mutual-excitation properties in various domains. Generally …
interaction events that exhibit mutual-excitation properties in various domains. Generally …
Learning item-interaction embeddings for user recommendations
Industry-scale recommendation systems have become a cornerstone of the e-commerce
shopping experience. For Etsy, an online marketplace with over 50 million handmade and …
shopping experience. For Etsy, an online marketplace with over 50 million handmade and …
I Know Your Intent: Graph-enhanced Intent-aware User Device Interaction Prediction via Contrastive Learning
With the booming of smart home market, intelligent Internet of Things (IoT) devices have
been increasingly involved in home life. To improve the user experience of smart homes …
been increasingly involved in home life. To improve the user experience of smart homes …
Learning dynamic embeddings from temporal interactions
Modeling a sequence of interactions between users and items (eg, products, posts, or
courses) is crucial in domains such as e-commerce, social networking, and education to …
courses) is crucial in domains such as e-commerce, social networking, and education to …