# Location-Aware Deep Interaction Forest for Web Service QoS Prediction
## 2 Related Work
### Collaborative Filtering Methods  
Memory-based collaborative filtering methods focus on using historical interactive information of users or services to complete the prediction of QoS. They mainly calculate similarity to improve prediction accuracy. [PERSON] et al. [12] proposed a new similarity calculation with different weights and a fusion of user- and item-based similarity calculation used to predict missing QoS values. [PERSON] et al. [13] proposed personalized QoS prediction for cloud services by using two different similarity rank lists. After that, [PERSON] et al. [14] found that users with lower similarity are useless, or even harmful, to the target users, so the two-stage strategy of similarity threshold and similarity fusion was used to replace the traditional TOP-K selection strategy. However, these improvements did not substantially change the mathematical formula of similarity calculation, so [PERSON] et al. [15] proposed an effective collaborative filtering approach for personalized web service recommendation.  
This approach improved the traditional Pearson correlation coefficient (PCC) similarity measurement approach by considering the influence of service personalization, and combined the algorithm based on personalized users and the algorithm based on personalized items. Although these methods are easy to implement, they are easily affected by problems such as sparseness, cold start, and poor scalability.  
Model-based collaborative filtering methods focus on using the QoS interactive matrix to learn the model, and are widely used to solve the problems mentioned above. With the introduction of matrix factorization (MF) [16] and factorization machines (FM) [17], a new research idea was derived for how to integrate feature interaction information to achieve higher prediction accuracy. Among them, [PERSON] et al. [18] believed that the QoS of web services could be predicted according to the characteristics and past experience of the user, so they proposed a personalized web service QoS prediction approach based on neighborhood integration matrix factorization. [PERSON] et al. [19] used the network map to measure the network distance between service and users and identify the neighbors of users by considering the impact of the underlying network on the QoS of web services, and they introduced the above two factors into the matrix factorization as constraints for calculation. And [PERSON] et al. [20] used a flexible matrix factorization technique and a QoS prediction of the future time interval using a time series forecasting approach based on an autoregressive integrated moving average (ARIMA) model. But the latent vectors themselves made by MF are not explanatory, so [PERSON] et al. [21] introduced graph theory into the matrix factorization model to strengthen the interpretability and accuracy; this approach divides the user-service graph into a certain number of subgraphs by dividing the maximum subgraph, and then applies both the subgraph and the user-service graph to the matrix factorization model to perform local and global analysis and prediction. In order to better integrate the contextual information and feature interaction information, [PERSON] et al. [22] directly used the similarity of geographic locations to find user neighbors, and put the features of user neighbors as user features into the factorization machine model to finish QoS prediction. On this basis, [PERSON] et al. [23] proposed a location-based factorization machine model that integrates users, services, similar users, similar services, and geographic location features into a factorization machine. For the reliability of QoS data, [PERSON] et al. [24] proposed a noise-resistant data-characteristic-aware latent factor (DCALF) model to implement highly accurate QoS prediction.