Exploring Users' Internal Influence from Reviews for Social Recommendation

In recent years, we have witnessed a flourish of social review websites. Internet users can easily share their experiences on some products and services with their friends. Therefore, measuring interpersonal influence becomes a popular method for recommender systems. However, traditional works are all based on external tangible activities, such as following, retweeting, mentioning, etc. In this paper, we explore user internal factors to measure his/her influence on a specific domain, namely, the social network on local businesses. The proposed user internal factors include user sentimental deviations and the review's reliability. The internal factors are not from explicit behavior but could help us to understand users. In addition, we utilize an attention mechanism that could auto-learn the weights of different factors. Through a case study on the Yelp dataset, we found that the proposed user internal factors on influence, that is, the proposed user sentimental deviations and the review's reliability, are effective in improving the accuracy of rating predictions.