# A Location-Time-Aware Factorization Machine Based on Fuzzy Set Theory for Game Perception
## 2 Related Work  
In the field of user experience, most of the related work on game user perception focuses on the impact of traditional service quality parameters on subjective user-perceived quality [3; 4; 5]. Traditional data-driven models use linear models to generate user game perception scores such as logistic regression and linear regression models [6]. In recent years, most user perception prediction models have been predicted using machine learning techniques. For example, research uses nonlinear support vector machines for QoE modeling, which uses random forests to build prediction models [7], etc. These methods offer important advantages for effective user perception prediction. However, latent variable models [8] have shown that these machine learning models have not learned the potential but useful feature interactions well in the game QoE task dataset. Factorization machines (FM) [9] provide a predictor which can model and take feature interactions into account efficiently in linear time complexity. At present, although FM has not been applied in the field of game perception, it has carried out a lot of research in the quality of services (QoS) prediction of network services. QoS prediction for network services only focuses on the impact of network parameters on QoS. Different from the service quality prediction of network services, game user perception not only pays attention to network parameters but also pays attention to the user's personal characteristics and the influence of service characteristics in the game on the end user's evaluation of experience quality. Their ultimate purpose is the same: to enhance the quality of service to improve user perception. [PERSON] et al. [10] first introduced a factorization machine into the quality-of-service prediction and proposed an EFMPred model by combining embedding techniques. [PERSON] et al. [11] proposed an LBFM model by converting location information to neighborhood information in the factorization machine. Then [PERSON] et al. [12] proposed the LANFM model, which added the location information to the factorization machine by one-hot encoding. [PERSON] et al. [13] proposed an LDFM model which uses location projections to extend the record of database and information entropy to enhance valid features. In the case of low data volume as well as few features, these algorithms expand the dataset, adding features by combining features and performing well in the case of sparse data. However, the weights for different cross-features are the same, which will introduce noise and make the model choose a suboptimal solution. [PERSON] et al. [14, 15] proposed the AFM model, which used a neural network modification of FM and added an attention mechanism to assign different weights to the second-order combinations of different features. Subsequently, [PERSON] et al. [16] proposed an IFM model by introducing an interaction-aware mechanism consisting of feature aspects and field aspects. While these algorithms improve the performance of the model by introducing an attention mechanism to obtain the weights of feature interactions, the training time increases significantly and consumes more computational cost. In conclusion, game user perception based on machine learning is becoming an increasing research hotspot. Some research exists on linear user perception evaluation based on network parameters. However, there are some research gaps in the field of game user perception models that consider the nonlinear effects of multiple factors. On the one hand, linear models are restrictive. The factors in real life are not simply linear, and the data are mostly sparsely distributed, which requires further research on machine learning algorithms. On the other hand, existing research has only built models based on network parameters. The effects of other factors and interactions between features on perception are not considered. In summary, we refer to FM in the study.