# An Empirical Recommendation Framework to Support Location-Based Services +
## 2 Related Work and Comparative Analysis
### Location-Based Services  
The availability of GIS technology the Location-Based Social Networks (LBSNs) adds a new dimension of recommendations. Using geotagged places, [PERSON] et al. (2018) provided a RS system to explore public interest. They work on the user's score to map the interests and to reduce the  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Check-in Place** & \(\mathbf{u_{a}}\) & \(\mathbf{u_{b}}\) & \(\mathbf{u_{c}}\) & \(\mathbf{u_{d}}\) \\ \hline Airport & 0 & 0 & 1 & 1 \\ Movie Theatre & 2 & 1 & 0 & 0 \\ Drama Theatre & 1 & 1 & 0 & 0 \\ University & 0 & 0 & 1 & 0 \\ Library & 0 & 1 & 1 & 0 \\ Restaurant & 1 & 2 & 0 & 0 \\ Park & 2 & 0 & 0 & 0 \\ Museum & 0 & 2 & 2 & 2 \\ Shopping Mall & 0 & 0 & 2 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Check-In Details.  
Figure 1: User check-in spots.  
cold start problem they considered the user which is new in the system. To improve the marketing approach, [PERSON] et al. [3] analyzed the marketing strategy which involves LBS. They also provided some instructions to overcome consumer problems and limitations of the companies. [PERSON] et al. [4] took a similar approach to identify the factors regarding economic benefits. [PERSON] et al. [5] presented some topics on LBS which includes analyzing, evaluating the LBS data. Similarly, [PERSON] et al. [6] evaluated the possible potentiality of LBS on the science and technology field.  
Based on the location of users, LBS provides information which is very helpful to analyze the behavior of users. [PERSON] et al. [7] introduced a hybrid approach based on the Zaltman metaphor elicitation technique (ZMET) to identify the factors which influence the quality of LBS and provides a better development of services. Based on the mobile location, [PERSON] et al. [8] reviewed the evolution process of LBS and tried to find the challenges towards the development of LBS. Decision-making ability, the interaction between users, and privacy on social media are some challenging features which affect the services. Analyzing the geo-tagged data some systems predict possible visitors. [PERSON] et al. [9] provided a prediction approach based on matrix factorization using previous places, time, and distances. Then an influence-mining model to consider those who are related to the user. [PERSON] et al. [10] focuses on the classifications of services and provides a messaging alert to mobile devices using GIS technology.  
Presently, themed street provides a unique recognition of a specific region intending to develop the economics. [PERSON] et al. [11] introduced a clustering approach to identifying the commercial themed street for the LBS. Mobile devices are the common platform for the location-bases service because of their portability. Taking account of this advantage, [PERSON] et al. [12] introduced a neural network to work on these heterogeneous data to improve marketing strategies. Though smartphones with GPS are the best possible choices to get the benefits of LBS, the privacy issue is a big risky concern. Considering this issue, [PERSON] et al. [13] analyzed the privacy level of the users and their performance expectancy.