## Appendix A  
This section provides clarification of the terms frequently used in this research. Definition 1. Demographic Data  
Refers to socioeconomic information people based on factors such as age, gender, residential location, social status, etc. This research is based on age and gender [49]. Example 1. Demographic Data for User A  
For instance, the demographic data for user A: Age: 25, Gender: Male. Definition 2. Big Five Personality  
The five-factor model or big five model is one of the most renowned models in the area of personality traits prediction [50]. The five different types of personality as illustrated by the five-factor model are as following:  
* Openness to Experience (a measure of Adventure seekers, Openness to new experience)
* Conscientiousness (a measure of the ability of any person to be organized)
* Extraversion (a measure of the tendency to seek stimulation in the external world)
* Agreeableness (a measure of tender tender-mindness)
* Neuroticism (a measure of the tendency for any user to be impulsive)  
Example 2. Big Five Personality for User A  
For instance, the personality data for user A: O = 3.14, C = 2.16, E = 4.16, A = 1.12, N = 3.36. Definition 3. Topics Modeling  
Topic modeling identifies keyword clusters that frequently appear together in user posts. Furthermore, the keywords in a topic that have the greatest likelihood of revealing what the topic is about [51]. Example 3. Topics Modeling for User A  
Suppose the user A has 100 tweets, it was extracted three topics as follows:  
Topic 1: Apple, snack, coffee, breakfast, bread, and break  
Topic 2: Library, university, lecture, conference, science and students  
Topic 3: Games, video, community of players, Snap and Facebook. Then the inferring process is to extract the main topics as follows:  
Topic 1: food, Topic 2: Academic, Topic 3: Games. Definition 4. Semantic Similarity  
Semantic similarity means a relationship in terms of the meaning and constituent semantics. In this research, the semantic similarity between the topics was used. Therefore, determined the correlation of two subjects based on the semantic concepts that make up [52]. Example 4. Semantic Similarity between Topics  
Suppose that the two topics, games and food, are similar in semantics. Definition 5. User Modeling  
The procedure represents all user characteristics and information related to a user in user context to gain user interests. This research required user information: personality, demographic data, and interest topics, and similarities between users [53]. Example 5. User Modeling for User A  
The user modeling for user A as follows:  
Age: 25, Gender: Male  
Interests' topics: food, Academic and Games  
Personality = O = 3.14, C = 2.16, E = 4.16, A = 1.12, N = 3.36  
Similarity: similar to user B, C and F.  
Definition 6. Explicit interest  
Explicit user interests can be viewed from user generated content explicitly. In other word, it can be detection by the user shares what in social media or by the user's own activities (such as social postings and social relationships) [54]. Example 6. Explicit interest  
User A writes the term Real Madrid or follows the official account of Real Madrid, one can infer that this person is interested in football, in Real Madrid. Definition 7. Implicit interest  
The user interests implicitly are potential interests' candidates which a user did not specifically discuss but which may have an interest in [54]. Example 7. Implicit interest  
User A interest in Topic A, Topic A and Topic B similar in semantic, then user A may be in interest in Topic B. Definition 8. Multi-faceted user interest profiles  
Various facets of users on the basis of multiple users modeling aspects which including data source, the user interest dynamics and level of representation [55]. Example 8. Multi-faceted user interest profiles  
User A has content, demographic, personality, location and social status  
Definition 9. Cold start problem  
Represent new users who do not have historical information or any content.