With the growing popularity of social media, the number of people using social media to communicate and interact with others has increased steadily. As a result, social commerce has become a new phenomenon. In the past, most of the product recommendations in microblogging only dealt with personal preferences and interests, and ignored other possible factors such as Crowd Interest, Popularity of Products, Reputation of Creators, Types of Preference and Recent. Nowadays, these variables used by Facebook to recommend posts to their users. Therefore, this research adapted those five aspects and analyzed their effectiveness to recommend products on social media. This study used the Plurk API to develop and implement recommended robots that recommend products at specific times of the day so that they can get product information and meet recommended tasks in the social circle. The empirical results showed that the Interest, Popularity and Type have significant impacts on recommendation effectiveness.