# A Location-Time-Aware Factorization Machine Based on Fuzzy Set Theory for Game Perception
## 3 Problem Formulation and Algorithm
### Location Information and Time Information  
A research article [17] showed that if two users located in the same region have similar network conditions, then the experience which calls the same service in the same region is also similar. Other research [11, 18] found that it is common for individual users and independent services to be located in two or more overlapping areas. Thus, user and service information are projected into the potential space [19] in the direction of its location vectors to generate new data. Figure 1 shows that User1, User2, and User3 are located in the same user region with similar location information, and the QoS values they invoke are also similar. Both S3 and S4 are located in service region two with a similar quality of service values. In practical scenarios, the environment and quality of the network are usually similar in terms of neighboring geographic locations, which plays an important role in the prediction of experience quality for different users. After the location projection, the number of records of users calling services and the number of users and services increase without introducing additional information. Thus, it is useful to take location information from users and services into account for game user perception evaluation prediction.  
In real scenarios, the network is divided into idle time and busy time. These two concepts are relative. For example, busy time means that the network will be slower during the daytime when there are more people using or grabbing the network. The network speed will be faster during idle time. Different regions have different network usage habits, and the resulting idle time and busy time are also different. What we need to consider is that the basic network parameters (for players to enter the game during busy hours) are worse than those when they are idle, which will cause game users to perceive poorly. We need to understand the changes in user perception in different time periods, predict the user perception evaluation in different situations, and point out the direction for operators to choose optimization decisions to improve user perception. Our experiments are based on the Xinjiang University campus network. The time periods for teachers and students to access the Internet can be divided into four time periods, as shown in Table 1.  
We chose to test in the latter three time periods, to be more instructive, and divided into three time periods in chronological order: morning, midday, and evening. However, the busy hours in these three time periods are 13:00-15:00, 19:00-21:00, and 24:00-2:00, respectively. These three busy hours are the time periods when teachers and students take their meals and breaks. They are two overlapping areas in the three time periods, so using the idea of location projections, each busy time is divided into two, and two new time periods are obtained for each. Combining user and time information to obtain new features, we find two new pieces of game data after time projections. Our experiments  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Time Period** & **Time Name** \\ \hline
2:00–10:00 & Idle time \\
10:00–14:00 & Morning \\
14:00–20:00 & Noon \\
20:00–2:00 & Night \\ \hline \hline \end{tabular}
\end{table}
Table 1: Internet time allocation for teachers and students.  
Figure 1: A real game service invocation scenario.  
are tested based on the Xinjiang University campus network, and the default user location information is similar, so we integrate the projections of both the location information and time information of the service into the new service matrix. The above two ideas expand our dataset without introducing additional information. However, this aggravates the challenge posed by data sparsity.