# Expert Recommendation for Answering Questions on Social Media
## 3 The Proposed Expert Recommendation Method
### User Interest  
In social media, each user writes a profile when joining. This profile is used to recommend similar users, including the interesting fields or hometown of the user when starting the SNS. However, most SNS users do not update their initial profiles even when their interest fields change. When an expert is recommended using the initial profile of an SNS user, a problem occurs whereby a user with low expertise or outdated interests is recommended as an expert. Hence, in this paper, the changing interest field of users is determined by analyzing the social activities of users. If the social activities of a user are analyzed, their changing interest fields can be kept up to date. Furthermore, even when a user has composed a false profile, if their trust is low compared with the user activities, that user is not determined to be an expert. Thus, the precision of expert recommendation increases. Figure 2 shows the procedure of determining user interest. User interest is based on data shared or writing posted by a user on social media. The user's writings or shared data are interpreted as the expression of user interest. First, the user's trust is calculated for the social activities of the user. Social activity trust is determined by using the time of activity of the user, number of likes, number of comments, and number of shares. Using the morphological analyzer, nouns are extracted, and stopwords are removed. From the extracted words, important keywords or keywords frequently used by the user on SNS are extracted using the term frequency-inverse document frequency (TF-IDF) method. The user interest is calculated by adding the social activity trust and TF-IDF weight. The TF-IDF weight used in the method of extracting keywords of interest field is a weight used in information search and text mining. When a document group composed of multiple documents exists, it is used as a statistical value exhibiting the importance of a certain word in a certain document. Particularly, it is used to extract keywords in a document. TF indicates the term frequency, i.e., how frequently a certain word appears in a document. IDF indicates the inverse document frequency and indicates the number of documents that contain a certain word in the document. The TF-IDF is obtained by multiplying the TF and IDF. For example, we assume that 100 documents exist that a user is engaged in. Among them, eight documents contain the word \"Hadoop\". We suppose that the word \"Hadoop\" appears five times in one of those documents. Subsequently, the TF value for that document is 5, and the IDF is 2.52573. Therefore, the TF-IDF value is 12.6286. To extract the user's interest, keywords are extracted using the TF-IDF method for the user activity details. The higher the TF-IDF value, the more important the keyword. It determines the trust of the document in which the extracted keywords appear. User interests are expressed by the weight of keywords extracted from documents created through user social activities. The keywords with a high weight mean a high  
Figure 1: Overall architecture of the expert recommendation method. Figure 2: Determination procedure of user interest. interest of the user. Equation (1) is the weight of the keyword \(i\) of user \(k\), where \(n\) is the number of documents left by user \(k\), \(TF_{ik}\) is the term frequency that represents the frequency appearing in the document, \(DT_{jk}\) is the trust of document \(j\) indicated by user \(k\), and \(IDF_{ik}\) is the inverse document frequency that represents the number of documents that contain a keyword in the total document. \[KW_{ik}=\left(\frac{\sum_{j=1}^{n}TF_{ik}\cdot DT_{jk}}{n}\right)\cdot IDF_{ik} \tag{1}\]  
The method of evaluating the trust of a document assigns a larger time weight to more recent writings by assigning a weight to the up-to-datedness of a document and determines the trust of a document by considering the document's number of likes, number of comments, and number of shares. Equation (2) calculates the document trust left by user \(k\).