# BERT4 Loc: BERT for Location--POI Recommender System
## 2 Related Work  
This section covers some state-of-the-art works in this line of research. The recommender systems assume that the users who share behavioral preferences are more likely to form connections with each other. Social recommender systems target social media [18] such as Facebook, Instagram, and Twitter. SocialMF [19] is a social recommender system that learns the users' preferences using the basic matrix decomposition model and then builds the profile by using the information of user's friends. SoReg [20] is another social recommender that solves the overfitting problem in SocialML. TrustSVD [21], another social recommender system, models the implicit behavioral data and social relationship of users. By incorporating implicit information about the user's network, this model brings the user's feature representation closer to actual application scenarios. In 2016, Google proposed the Wide&Deep [5] approach, which has generated a lot of interest in recommendation systems. This model has inspired several other models, including DeepFM [22], Deep & Cross [23], NFMs (Neural Factorization Machines) [24], AFMs (Attentional Factorization Machines) [25], DIN (Deep Interest Network) [26], and others. Sequential recommender systems [27], based on deep neural networks, also model the real-life interactions between users' actions before and after they have consumed an item. For example, in a location recommendation station, when a person visits a place, it is more appropriate to recommend other POI that are nearby and related to the user's current and past-visited POI. With recent progress in NLP, sequential recommendation models have also advanced. From the initial Markov Chain [28], through to the following RNNs, Convolutional Neural Network (CNN) models, and now the popular Transformer [7], there has been a lot of progress. The SASRec [29] sequence recommendation model is based on the self-attention mechanism used to model past user behavior and retrieve top-k recommendations. Recently, Transformer architecture [26] has been used to understand better the items that the user has clicked on by capturing the underlying sequential signals. BERT has also been adapted for recommendation tasks, as seen in BERT4 Rec [10], a Transformer-based recommender model that treats the sequence of user actions as a text sequence. Another approach combines BERT with the Neural Collaborative Filtering (NCF) framework [30], resulting in improved item recommendation performance. In a related work, a BERT-based hybrid collaborative filtering system [31] is developed to learn user and item embeddings, demonstrating enhanced performance compared to traditional collaborative filtering methods. Furthermore, BERT has been extended for sequential recommendation [32] by incorporating temporal contexts. One such study integrates time-aware attention mechanisms with BERT to capture sequential patterns in users' historical interactions. In our study, we extend the application of BERT in recommender systems specifically for POI recommendations. Our model incorporates additional item (POI) information and user features, including social relationships. Trained on a real-world dataset, our model is capable of providing real-time POI recommendations. There are some works that have been proposed for POI recommendations in recent times. For example, [PERSON] et al. [15] proposed a cross-cultural recommendation system for tourism services, outperforming existing methods in terms of accuracy. There is also a POI recommendation system [14] that considers both user preferences and geographic factors, achieving significantly better performance than previous approaches. The OurPlaces platform [16], another POI recommender system, enables cross-cultural sharing of spatial experiences and improves tourism recommendations, surpassing a baseline in terms of accuracy. iTouSPOT [33] is another framework that enhances next POI recommendations in LBSNs by capturing fine-grained user temporal preferences and utilizing sequential contextual information.