Social Recommendations for Facebook Brand Pages

The objective of this research is to bridge the gap by proposing a content-based framework that is specifically designed to operate in social media environment. This study proposed a recommendation framework that integrated the features of brand pages and information on user behavior on brand pages. Data were obtained from 2,076 official brand pages in Taiwan, and a total of 500,000 interaction data were obtained and processed. Decision trees were used to classify brand pages and detect features that facilitate distinguishing among brand pages. Our method involved a simple training procedure with no restriction to a particular classifier learning algorithm and yields improved results on data sets extracted from a considerable number of Facebook brand pages. The results represented the diversity of social media user criteria in evaluating whether an activity is interesting to users. Moreover, these prevalent features indicate that brand pages distinguish themselves from others through their willingness to engage and interact with users. These findings not only enabled using brand page recommendation models for facilitating the selection of the most suitable brand page, but also useful for researchers seeking to develop a recommendation system for social media.