An Incremental Group-Specific Framework Based on Community Detection for Cold Start Recommendation

To address cold start problem by utilizing only rating information, this paper proposes an incremental group-specific framework for recommender systems. Firstly, a decoupled normalization method is introduced to extract preference patterns from ratings. Then, two incremental community detection methods are proposed to detect evolving communities in dynamic networks for capturing interest shifts, and new users/items corresponding communities according to the missing mechanism of ratings. Finally, an incremental group-specific model is proposed to incorporate evolving communities for recommender systems. A series of empirical analysis on three datasets is conducted to validate the rationality of grouping new users/items with missingness-related information. Experimental results show that the proposed framework can achieve better performance compared with other competitive methods, and is capable of handling the cold start problem and highly scalable with incremental data.

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