Personalizing recommendation diversity based on user personality

In recent years, diversity has attracted increasing attention in the field of recommender systems because of its ability of catching users' various interests by providing a set of dissimilar items. There are few endeavors to personalize the recommendation diversity being tailored to individual users' diversity needs. However, they mainly depend on users' behavior history such as ratings to customize diversity, which has two limitations: (1) They neglect taking into account a user's needs that are inherently caused by some personal factors such as personality; (2) they fail to work well for new users who have little behavior history. In order to address these issues, this paper proposes a generalized, dynamic personality-based greedy re-ranking approach to generating the recommendation list. On one hand, personality is used to estimate each user's diversity preference. On the other hand, personality is leveraged to alleviate the cold-start problem of collaborative filtering recommendations. The experimental results demonstrate that our approach significantly outperforms related methods (including both non-diversity-oriented and diversity-oriented methods) in terms of metrics measuring recommendation accuracy and personalized diversity degree, especially in the cold-start setting.