# Activity Recommendation Model Using Rank Correlation for Chronic Stress Management
## 2 Materials and Methods
### Recommendation Technic in Healthcare  
The recommendation system [16] obtains user information, calculates a similarity, and makes a cluster based on individuals' interest areas, logs, preferences, and bookmarks. It recommends the items of music, films, books, and others which users in a similar group seem to be interested in. The methods for recommendation are collaborative filtering, content-based filtering, and hybrid filtering. Collaborative filtering is the method of using the preferences of items and clustering users with similar patterns. It has the first-rater problem of excluding items from a recommendation list if there is no history of them [16; 17]. Content-based filtering is the method of analyzing the content of items and using the similarities and attributes of obtained items. In other words, based on a user profile, it discovers and uses similarities of attributes for recommendation [18]. The method recommends items with a high similarity only so that it has the problem of over-specialization. Hybrid filtering is the method of resolving the cold-start problem and the over-specialization problem and improving performance in a combination of each filtering method [19]. By considering item attributes and rating data, it assigns a weighting value differently to recommend a proper item. The recommendation system developed recently uses hybrid filtering to increase the precision and recall of the prediction. Figure 3 shows the process of hybrid collaboration filtering. In Figure 3, content-based filtering is based on item-item or user-user algorithms [20]. First, the item-item algorithm [21] calculates the distance between items based on the similarity between items. Based on this distance, close items, that is, similar items are recommended. For example, if an obese user has diabetes, the distance between obesity and diabetes is determined to be very close. Therefore, it is possible to warn about the correlation between the two by recommending diabetes and obesity to an obese person and a diabetic person, respectively. Most data have fewer users than items, so various relationship data are derived based on items. This means that the more productive the data, the more accurate the recommendation. On the other hand, the user-user algorithm [22] quantifies the distance of how much the two users' dispositions match based on common items between users. For example, if both user A and user B are diabetic, the distance between the two users is very close, 0 and the two users are determined to be very similar. However, if user A is diabetic, and user B is hyperlipidemia, the distance between the two users is far, and the similarity is also lowered. As shown above, the similarity between users is calculated to recommend items that one user and similar users like. However, the user-user algorithm  
Figure 3: Process of hybrid collaboration filtering. is difficult to recommend if there are no similar items among users enough to calculate similarity due to lack of data. Besides, if there are items preferred by all users, the results of similarity among all users are similar, and thus the recommendation result may also be the same. In other words, there is a disadvantage that the results meaningless to recommendation are derived for the universal preference. Recently, studies on recommendation tend to use deep learning, data mining, decision tree, and SVM. A recommendation system using deep learning assigns a weighting value to the item preferred by users for clustering and then predicts a missing value. Also, using the multi-modal deep learning model that maps a user with an item in the maximum similarity zone, a recommendation system learns the characteristics of items in a different range to expand its knowledge [23]. There is research on data mining and context-awareness technology to increase the precision of a recommendation system with the problem of user-to-item data scarcity.