# GT-SEER: Geo-Temporal SEquential Embedding Rank for Point-of-interest Recommendation

arXiv: https://arxiv.org/abs/1606.05859 · HTML: https://mlsanity.com/paper/1606.05859/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jun 2016.

## Built on by (most cited first, 2 of 2)

- [Spatial Object Recommendation with Hints: When Spatial Granularity Matters](https://mlsanity.com/paper/2101.02969/) (2021)
- [Pre-training Contextual Location Embeddings in Personal Trajectories via Efficient Hierarchical Location Representations](https://mlsanity.com/paper/2310.01252/) (2023)

## Abstract (arXiv)

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 textitSEquential textitEmbedding textitRank (textitSEER) model for POI recommendation. In particular, the textitSEER 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 textitSEER 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 textitTemporal textitSEER (textitT-SEER) model. In addition, We incorporate the geographical influence into the textitT-SEER model and develop the textitGeo-Temporal textitSEER (textitGT-SEER) model.