# A CNN-Based Advertisement Recommendation through Real-Time User Face Recognition
## 5 Discussion and Conclusions  
As computer vision and information technology advance, an environment has been established to collect various types of data such as facial expressions and purchase history. However, most online video recommender systems have built dynamic user profiles by capturing facial expressions through heuristic techniques. Such an approach is challenging when it comes to recommending advertisement videos in real time to new users without past video historical data. In this study, we propose a novel recommender system using computer vision and a deep learning approach to improve the limitations of existing video recommender systems. Thus, we developed a CNN-based prediction system to predict how a user enjoys an advertising video by recognizing their facial expressions. We also  
Figure 11: The performance evaluation of recommender systems for existing users. Figure 10: The performance evaluation of recommender systems for the new users. applied SIFT to recommend advertisement videos to new users to search for users with similar preferences in real time. The experiment results are as follows. First, the proposed CNN-based rating prediction system outperforms other systems in the preference rating predictions of advertising videos for new users and users with video records. The proposed CNN-based recommender systems predict user preferences based on real-time user facial expressions. Although the recommendation performance for new users varies depending on the video type, the overall predictive performance is excellent. However, the proposed method cannot offer a stable recommended performance considering the overall historical data. That is, when considering the overall historical data, the CF technique can be used more efficiently. Accordingly, we find that facial expressions are critical factors in recommendations to new users. Second, the proposed KPS-based advertisement recommender system indicates excellent performance when recommending large-size recommendation lists to both new and existing users. That is, when the recommended list size is small, the CF-based system shows a more stable recommendation performance. Therefore, we can determine that it is more suitable to use data such as click history than real-time user facial expression data when providing a small size recommendation list. Nevertheless, we found it more effective to recommend advertisements based on real-time user facial expressions when several advertisements were recommended. The academic and practical implications of this study are as follows. First, we proposed a methodology for recommending online advertising videos through a deep learning approach. In previous studies, when measuring user facial expressions, these were collected through heuristic techniques, while we elaborately extracted user facial expressions by applying SIFT deep learning techniques. Similar users are calculated based on such facial expression characteristics, and customized advertisements are recommended in real time. We have contributed to the expansion of research areas related to recommender systems through this study. Second, existing recommender system research mainly uses memory-based CF techniques. However, the memory-based CF technique is a lazy learning technique that produces a result through a heuristic technique whenever a recommendation is required without building a model. As the types of online videos have become diverse, and the number of users has increased, the data size has increased, and the existing algorithms have consumed a lot of time and resources. In online advertising videos, in particular, it is necessary to quickly calculate and find recommendation items using real-time information and finally make personalized recommendations for each user. This study expands research on recommender systems by building a CNN-based model that recommends online advertising videos to users in real time after constructing a model using actual data.