# Recommendation Systems: Algorithms, Challenges, Metrics, and Business Opportunities
## 5 Business Adoption and Applications
### Recommendation Systems in Media and Beyond  
The technological developments and changes in media and the increasing number of people visiting cultural places have led to an increase in various cultural items and offers. Therefore, visitors are bombarded with the information, making it difficult for them to find their interests. Thus, recommendation systems have become a vital tool to provide suggestions that ease the information overload in this area. In [80], social information as artwork attributes (e.g., type, date of creation, artist, and technical material) and user experience in a real art event is used to find the relation between users. It was shown that combining three recommender systems, including content-based, social-based, and context-based performs well in the cultural heritage domain. Museums, as one of the most important types of cultural heritages, in [81], a survey on intelligent recommender systems for museums and showed the use of users' location information and social interactions. Mobile apps have also contributed a lot in using RS systems in the cultural heritage domain. A smart search museum mobile application is used in [82] with context-aware and hybrid RSs. They have introduced a big data architecture which catches users' information--such as tastes, preferences, behaviors, needs, position, etc.--from social networks to provide recommendations.  
In addition to the adoption of RSs in the cultural heritage domain, RSs are expanded to multimedia content in text, image, video, audio, etc. to help users find their favorite multimedia content. Users' multimedia data in social media are used in [83], where users' preferences, opinions, behaviors, and feedback are incorporated using metadata, textual comments, activity logs of users on a site, and ratings. They also introduced a new RS for big data application. Video RSs are widely used, especially on Netflix and YouTube. RSs rely on metadata to provide a personalized recommendation of movies; in [84] a RS based on users' preferences using machine learning is introduced. Another multimedia RS model is presented in [85], which uses social relationship mining methods and movies' metadata, users' comments, and conversation content. The recommendation results have been enhanced by applying sentiment analysis, the SVM model, and Word2 Vec-based social relationships. Open social networks (OSN) play a critical role in providing personalized recommendations to users by exploiting the data and feedback retrieved from social relationships and consumer profiles on the network. In [86], the authors used the lexical analysis of Twitter's data and generated ranking scores to identify similar users. In [87], a novel music recommendation system based on the users' behaviors and their personality traits extracted from OSN is introduced. They embedded their findings with a content-based filtering approach to increase the recommender accuracy. In [88], the authors developed a diffusion interference method based on the OSN recommendation system. In [89], a collaborative and user-centered technique that exploits the users' relationships and interactions with the generated multimedia content is presented. The model consists of three stages--including data prefiltering,ranking, and similarity calculation--where they used a subset of the Yahoo Flicker 100 Million multimedia dataset. In [90], a new trust-based privacy-preserving framework for decentralized friend recommendation in OSNs (ARMOR), which utilizes OSN users' social trust relationships to generate a friend recommendation in a privacy persevering fashion, is presented. They adopted a real dataset that contains Facebook networks for 100 universities.  
Table 2 shows a summary of business adoption of various recommendation systems in the four categories and their references.