# Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities
## 5 Business Adoption and Applications
### Recommendation Systems in Transportation  
RSs can assist in diverse ways with the increasing use of Global Positioning System (GPS)-enabled devices, especially mobile devices. Because information overload problems become worse when using mobile devices. The development of wireless communication services and position detection techniques such as RFID or GPS have promoted location-based information systems. RSs play a significant role in path recommendation, smart transport application of goods [8; 57], tourism industry [58; 59; 60], or venue recommendation [61]. To predict users' location and suggest the best pathways, RS can use users' location data and integrate it with public transportation system data. An algorithm is proposed in [49] to predict users' path and recommend the best bus line. A prototype of their proposed algorithm is also developed for Android cell phones. Inputs of this system are the users' route and bus lines' data. RSs are used in smart transport applications with different methods, such as optimization [57] or clustering [8]. For instance, a clustering-based RS is developed in [8] for the transportation of goods. They reduced data dimensionality using principal component analysis (PCA) and applied a K-means method for clustering transporters based on their distance to users. This system generates a recommendation of the best available transporters for a user based on their location and transporters' profile. Some RSs aim to help users, mainly tourists, to find places such as the best restaurants. The proposed RS in [59] takes users' personal information and restaurants' attributes as input and applies a Bayesian network to calculate recommendation score and show recommended restaurants. Another restaurant RS is proposed in [62]. Collaborative filtering recommendation is used in [60] to help users explore the attractions of a city. They used histories of user's activities and visitors log to recommend locations. The proposed system enables users to collaboratively share their photos and experiences and provide more recommendations of places. The advantages of using a RS in transportation are to provide recommendations to users regardless of their location and time due to mobile devices. Mobiles have provided the possibility to access valuable information about a user's physical location. Even though mobile devices are becoming the main platform for information access, users may have difficulty finding recommendations using small-screen devices. The more users have to scroll pages, the lower the chance for an item to be found. Another challenge of applying RSs in transportation is data sparsity, as most of these systems rely on locations where a user has visited. The number of physical locations that users visit is limited, resulting in a sparse user-item matrix. Also, users may visit locations where they have never been before, making it challenging to apply collaborative filtering methods based on the user's history. Social networks are widely used in recommending locations. Reference [11] describes location-based social network RSs (LBSN) based on the relationship between user, activities, and locations. Historical information of users' location differentiates LBSN recommenders from traditional ones. Also, social networks have facilitated the use of LBSN recommenders. They are used to provide location, activity, or friend recommendations to people. Activity is considered to give a location's suggestions to satisfy a user's demand for activities such as sports, museums, restaurants, etc. For location RSs, some researchers have considered users' temporal attributes [63], and some others relied on both temporal and spatial attributes [64]. We have categorized applications of RSs in transportation into:  
1. Recommending trip
2.