# A Location-Time-Aware Factorization Machine Based on Fuzzy Set Theory for Game Perception
## 5 Conclusions  
The LTFM model has the key advantage that FM models efficiently capture nonlinear feature interactions between single features in sparse datasets. It utilizes the idea of projection of location and time information to enhance the predictive performance of standard FM models while using fuzzy set theory to consider enhancing the correlation between feature interaction and labels. The membership module can directly compute correlations between categorical features and continuous features (or feature sets) and be used to weigh and purify interactions between individual features from different data domains. Then, we apply the proposed LTFM to the user game perception prediction problem with a dataset that records the user and service factors and the final user perception ratings for each game. Experiments show that the proposed LTFM outperforms the benchmark model in prediction results, proving the effectiveness of the algorithm. Our proposed LTFM model can be used for decision analysis on how operators can optimize user perception as an integral part of a decision support system, which can help to better describe, predict, and provide effective guidelines for the prevention of poor user perception. The implementation of predictive user game perception analysis can provide operators and game developers with an easy-to-use evaluation management tool, thus reducing operational costs and improving efficiency.  
There are still some limitations of our study and potential research worth exploring further. First, external macro conditions and contextual features of playing games (e.g., at home, subway, etc.) may have important effects on user game QoE evaluation. Considering these features, which are not well structured in the game database, in subsequent studies will further improve the plausibility of the overall prediction model performance. Second, different cognitive criteria among different users may result in sample statistical bias to accumulate rich valuation prediction features. One way to overcome this feature deficit problem is to include domain knowledge from experts of these companies as additional predictive features in future work.  
Conceptualization, X.Z. and X.X.; methodology, X.X.; formal analysis, H.S.; data curation, X.X., H.S. and X.Z.; writing--original draft preparation, X.X.; writing--review and editing, Z.J.; visualization, X.X.; project administration, Z.J.; funding acquisition, Z.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by China Mobile Communications Group Xinjiang Co., Ltd. Development Fund Project (Grant CMXJ-202100687.)  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
LFTM Location-Time-Aware Factorization Machine  
QoE Quality of Experience  
MOBA Multiplayer Online Battle Arena  
IUT-T ITU-T for ITU Telecommunication Standardization Sector  
QoS Quality of Services  
FM Factorization Machine  
NFM Neural Factorization Machine  
AFM Attention Factorization Machine  
EFMPred embedding based factorization machine  
LBFM Location-based Factorization Machine  
LANFM leveraging location information Factorization Machine  
IFM Interaction Factorization Machine  
LDFM Location-based Deep Factorization Machine  
\(\mathbf{x}_{users}\) User matrix  
\(\mathbf{x}_{new\ series}\) Service matrix after time and location projections