INDIVIDUALITY OR CONFORMITY: RECOMMENDATION EXPLOITING COMMUNITY-LEVEL SOCIAL INFLUENCE

With the increasing prevalence of online businesses and social networking services, a huge volume of data about transaction records and social connections between users is accumulated at an unprecedented speed, which enables us to take advantage of electronic word-of-mouth effect embedded in social networks for precision marketing and social recommendations. Different from existing works on social recommendations, our research focuses on discriminating the community-level social influence of different friend groups to enhance the quality of recommendation. To this end, we propose a novel probabilistic topic model integrating community detection with topic discovery to model user behaviors. Based on this model, a recommendation method taking both individual interests and conformity influence into consideration is developed. To evaluate the performance of the proposed model and method, experiments are conducted on two real recommendation applications, and the results demonstrate that the proposed recommendation method exhibits superior performance compared with the state-of-art recommendation methods, and the proposed topic model exhibits good explainablibity of topic semantics and community interests. Furthermore, as some people are more individual interest oriented and some are more conformity oriented demonstrated by the experiments, we explore factors that influence each individual’s conformity tendency, and obtain some meaningful findings

Interaction-Aware Watching Duration Prediction on Live Streaming Platforms

Recently, live streaming, as a primary social media trend, has become more and more commonplace in businesses’ social media marketing. For live streaming platforms, how to keep viewers stay is always the central issue. To better predict viewer’s behaviors, we study a watching duration prediction problem in this paper. Different from prior literature, we take interactions between viewers and anchors into consideration and develop an integrated modeling framework for the prediction task. The proposed interaction-aware model combines probabilistic matrix factorization model with deep learning model to extract useful features from interactions for collaborative prediction. To the best of our knowledge, our study is the first one to analyze interactive behaviors for video watching prediction. Comprehensive experiments have been conducted on a real-world dataset to evaluate our predictive model. Experimental results demonstrate that our new model significantly outperforms baselines in watching duration prediction