Yanchi Liu

Where to Go Next: A Spatio-temporal LSTM model for Next POI Recommendation

2018 · arXiv (Cornell University)

Next Point-of-Interest (POI) recommendation is of great value for both location-based service providers and users. Recently Recurrent Neural Networks (RNNs) have been proved to be effective on sequential recommendation tasks. However, existing RNN solutions rarely …

MixLLM: Dynamic Routing in Mixed Large Language Models

2025

Xinyuan Wang, Yanchi Liu, Wei Cheng, Xujiang Zhao, Zhengzhang Chen, Wenchao Yu, Yanjie Fu, Haifeng Chen. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human …

POI Recommendation: A Temporal Matching between POI Popularity and User Regularity

2016

Point of interest (POI) recommendation, which provides personalized recommendation of places to mobile users, is an important task in location-based social networks (LBSNs). However, quite different from traditional interest-oriented merchandise recommendation, POI recommendation is more …

Sequential Recommender System based on Hierarchical Attention Networks

2018

With a large amount of user activity data accumulated, it is crucial to exploit user sequential behavior for sequential recommendations. Conventionally, user general taste and recent demand are combined to promote recommendation performances. However, existing …

Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation

2019 · Proceedings of the AAAI Conference on Artificial Intelligence

Next Point-of-Interest (POI) recommendation is of great value for both location-based service providers and users. However, the state-of-the-art Recurrent Neural Networks (RNNs) rarely consider the spatio-temporal intervals between neighbor check-ins, which are essential for modeling …

Recurrent Convolutional Neural Network for Sequential Recommendation

2019

The sequential recommendation, which models sequential behavioral patterns among users for the recommendation, plays a critical role in recommender systems. However, the state-of-the-art Recurrent Neural Networks (RNN) solutions rarely consider the non-linear feature interactions and …

Graph Contextualized Self-Attention Network for Session-based Recommendation

2019

Session-based recommendation, which aims to predict the user's immediate next action based on anonymous sessions, is a key task in many online services (e.g., e-commerce, media streaming). Recently, Self-Attention Network (SAN) has achieved significant success …

Feature-level Deeper Self-Attention Network for Sequential Recommendation

2019

Sequential recommendation, which aims to recommend next item that the user will likely interact in a near future, has become essential in various Internet applications. Existing methods usually consider the transition patterns between items, but …