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 …
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 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 …
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 …
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 …
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 …
Predicting dynamic embedding trajectory in temporal interaction networks
Modeling sequential interactions between users and items/products is crucial in domains
such as e-commerce, social networking, and education. Representation learning presents …
such as e-commerce, social networking, and education. Representation learning presents …
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 …
The difference between a click and a cart-add: learning interaction-specific embeddings
For large-scale online marketplaces with over millions of items, users come to rely on
personalized recommendations to find relevant items from their massive inventory. One …
personalized recommendations to find relevant items from their massive inventory. One …
Komen: Domain knowledge guided interaction recommendation for emerging scenarios
User-User interaction recommendation, or interaction recommendation, is an indispensable
service in social platforms, where the system automatically predicts with whom a user wants …
service in social platforms, where the system automatically predicts with whom a user wants …