GT-SEER: Geo-Temporal SEquential Embedding Rank for Point-of-interest Recommendation
Point-of-interest (POI) recommendation is an important application in location-based social networks (LBSNs), which learns the user preference and mobility pattern from check-in sequences to recommend POIs. However, previous POI recommendation systems model check-in sequences based on either tensor factorization or Markov chain model, which cannot capture contextual check-in information in sequences. The contextual check-in information implies the complementary functions among POIs that compose an individual's daily check-in sequence. In this paper, we exploit the embedding learning technique to capture the contextual check-in information and further propose the \textit{{\textbf{SE}}}quential \textit{{\textbf{E}}}mbedding \textit{{\textbf{R}}}ank (\textit{SEER}) model for POI recommendation. In particular, the \textit{SEER} model learns user preferences via a pairwise ranking model under the sequential constraint modeled by the POI embedding learning method. Furthermore, we incorporate two important factors, i.e., temporal influence and geographical influence, into the \textit{SEER} model to enhance the POI recommendation system. Due to the temporal variance of sequences on different days, we propose a temporal POI embedding model and incorporate the temporal POI representations into a temporal preference ranking model to establish the \textit{T}emporal \textit{SEER} (\textit{T-SEER}) model. In addition, We incorporate the geographical influence into the \textit{T-SEER} model and develop the \textit{\textbf{Geo-Temporal}} \textit{{\textbf{SEER}}} (\textit{GT-SEER}) model.
Code (0)
등록된 구현이 없습니다.
Tasks
Recommendation SystemsSimilar Papers 제목 키워드 기반
ForeSeer: Product Aspect Forecasting Using Temporal Graph Embedding
Developing text mining approaches to mine aspects from customer reviews has been well-studied due to its importance in understanding customer needs and product attributes. In contrast, it remains unclear how to predict t…
Graph EmbeddingLink PredictionSeER: An Explainable Deep Learning MIDI-based Hybrid Song Recommender System
State of the art music recommender systems mainly rely on either matrix factorization-based collaborative filtering approaches or deep learning architectures. Deep learning models usually use metadata for content-based f…
Collaborative FilteringDeep LearningRecommendation SystemsTemporal SequencesSeer: Language Instructed Video Prediction with Latent Diffusion Models
Imagining the future trajectory is the key for robots to make sound planning and successfully reach their goals. Therefore, text-conditioned video prediction (TVP) is an essential task to facilitate general robot policy …
DenoisingGPUPredictionVideo PredictionSequential Prediction of Social Media Popularity with Deep Temporal Context Networks
Prediction of popularity has profound impact for social media, since it offers opportunities to reveal individual preference and public attention from evolutionary social systems. Previous research, although achieves pro…
PredictionSocial Media Popularity PredictionRainSeer: Fine-Grained Rainfall Reconstruction via Physics-Guided Modeling
Reconstructing high-resolution rainfall fields is essential for flood forecasting, hydrological modeling, and climate analysis. However, existing spatial interpolation methods-whether based on automatic weather station (…