# Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities
## 6 Conclusions and Future Directions  
In this paper, we presented a detailed survey of RS that introduces different types of RSs as collaborative filtering, content-based, demographic-based, utility-based, knowledge-based, and hybrid-based. Different combination strategies of hybrid-based systems are also presented and categorized into weighted, mixed, switching, feature combination, feature augmentation, cascade, and meta-level. We presented four main challenges that affect the performance of a recommendation system, including cold-start, data sparsity, scalability and diversity, and metrics used to evaluate its performance. Furthermore, we show how recommendation systems have been adopted in e-commerce and various domains such as transportation, E-health, agriculture, and media. We rely on a substantial body of research to describe multiple applications in each field. We can conclude that: (1) The emergence need of more robust recommendation algorithms has led to wider applications of RSs. For instance, highly accurate deep learning methods are resulted in applying RSs in the health industry. (2) Technology advancements and smartphones have facilitated the use of RSs in daily life. For example, RSs recommend a path to drivers and passengers or the ones assisting farmers' tasks. Therefore, the effectiveness of RSs is verified in numerous areas, and they are increasingly popular. Future research  
\begin{table}
\begin{tabular}{c l l} \hline \hline
**Area** & **Application** & **Reference** \\ \hline \multirow{2}{*}{e-commerce} & Items recommendations to buyers & [4; 5] \\ \cline{2-3}  & Movie or video recommendations & [43] \\ \hline \multirow{3}{*}{Transportation} & Path Recommendation for transporting goodOr passengers & [8; 39; 49] \\ \cline{2-3}  & Recommendations to Tourists & [50; 51; 52] \\ \cline{2-3}  & Venue recommendation & [53; 54; 55] \\ \hline \multirow{3}{*}{e-health} & Medical advice or treatment plan recommendation & [6; 63; 64] \\ \cline{2-3}  & Recommending Personalized services to patients & [44] \\ \cline{2-3}  & Appointments recommendation to clinicians & [45] \\ \cline{2-3}  & Health recommendations in mobile systems & [59] \\ \cline{2-3}  & Healthy behavioral recommendations & [61] \\ \cline{2-3}  & Diet recommendation & [62] \\ \hline \multirow{3}{*}{Agriculture} & Fertilizer recommendation to farmers & [7] \\ \cline{2-3}  & Crops issue recommendation & [47] \\ \cline{2-3}  & Assisting farmers inquiries & [48] \\ \cline{2-3}  & Agricultural products recommendation & [65] \\ \cline{2-3}  & Crop cultivation suggestion & [40; 66; 67; 68] \\ \hline \multirow{3}{*}{Media} & Event recommendations & [80] \\ \cline{2-3}  & Museum recommendations & [81; 82] \\ \cline{1-1} \cline{2-3}  & Multimedia recommendations & [83; 84; 85] \\ \cline{1-1} \cline{2-3}  & Open Social Networks recommendations & [86; 87; 88; 89; 90] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Business adoption of Recommendation Systems (RSs) in five application areas.  
includes incorporating technological opportunities such as blockchain, IoT, and RSs. With the growing number of deep learning-based RSs and the number of users and items in online platforms, new approaches that scale well with massive datasets are a future direction to add.  
Conceptualization, Z.F. and R.K.; methodology, Z.F., M.E., D.N., A.I, and R.K.; resources, Z.F., M.E., D.N., A.I., and R.K.; writing--original draft preparation, Z.F., M.E., D.N., A.I., and R.K.; writing--review and editing, Z.F., M.E., D.N., A.I., and R.K.; visualization, Z.F., M.E., D.N., A.I., and R.K.; supervision, R.K.; project administration, R.K.; funding acquisition, R.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Ryerson University, Start-up Fund and the APC was funded by the same fund.  
The authors declare no conflict of interest.