Dynamic User Modeling Based On Personality Traits In Social Computing Systems

With the advance of social computing systems,many information and features have been used in the context of user modeling,such as profile information,location,behaviors and preferences.However,social media provides valuable resources to analyze the user's inner states,such as emotions and personality types.The user's personality characteristics could be a valuable resource that can reveal various inner features about the studied user,and this has led to the emerging to a new study field known as Personality computing.Most of the previous studies in this field have focused mainly on automatic personality recognition from the user's data,and to a lesser extent on incorporating the user's personality characteristics in the recommendation systems.However,none of the previous works studied the impact of incorporating the user's personality traits on user modeling and interest mining process,and the impact of that on the recommendation accuracy.In this thesis,we present a personality aware user modeling framework based on Big Five personality traits model and dynamic modeling of the users;interests.To prove the effectiveness of the proposed framework,we have proposed three application scenarios:(1)we propose a novel friend recommendation system based on the Big Five personality traits model and hybrid filtering,in which the friend recommendation process is based on personality traits and users;harmony rating.To validate the proposed system's accuracy,a personality based social network site that uses the proposed friend recommendation system named PersoNet is implemented.PresoNet achieved 0.812 in precision and 0.822 in recall and outperforms the state-of-the-art collaborative filtering system which scores 0.785 in precision and 0.79 in recall.(2)We propose a novel user interest mining system based on dynamic topic modeling and Big Five personality traits.To prove the effectiveness of incorporating the user's personality traits in the interest mining process,we have implemented a social network for news sharing and conducted different experiments on the collected data.The proposed system achieves 0.812 of precision and 8.22 of recall which is higher than compared state-of-the-art deep learning approaches.Moreover,it achieves 0.572 of precision and 0.579 of recall in cold start settings,which is higher than the legacy cold start mitigation schemes.(3)We propose Meta-Interest,a personality-aware product recommendation system based on user interest mining and meta-path discovery.Meta-Interest predicts the user's needs and the associated items,even if the user's history does not contain these items or similar ones.This is done by analyzing the user's topical interest,and eventually recommend the items associated with the user's interest.Meta-interest scores 0.81 in precision and 0.85 in recall,which is better than content filtering and collaborative filtering product recommendation systems.