Cluster Developing 1-Bit Matrix Completion
This work addresses grouping bias in 1-bit recommender systems, offering incremental improvements for applications like social behavior analysis.
This paper tackles the problem that existing 1-bit matrix completion methods for recommender systems ignore grouping bias among users and items, introducing Group-Specific 1-bit Matrix Completion (GS1MC) to incorporate group-specific effects and Cluster Developing Matrix Completion (CDMC) to handle cases without grouping information. Experiments show GS1MC outperforms current methods, and CDMC successfully captures items' genre features from sparse binary data.
Matrix completion has a long-time history of usage as the core technique of recommender systems. In particular, 1-bit matrix completion, which considers the prediction as a ``Recommended'' or ``Not Recommended'' question, has proved its significance and validity in the field. However, while customers and products aggregate into interacted clusters, state-of-the-art model-based 1-bit recommender systems do not take the consideration of grouping bias. To tackle the gap, this paper introduced Group-Specific 1-bit Matrix Completion (GS1MC) by first-time consolidating group-specific effects into 1-bit recommender systems under the low-rank latent variable framework. Additionally, to empower GS1MC even when grouping information is unobtainable, Cluster Developing Matrix Completion (CDMC) was proposed by integrating the sparse subspace clustering technique into GS1MC. Namely, CDMC allows clustering users/items and to leverage their group effects into matrix completion at the same time. Experiments on synthetic and real-world data show that GS1MC outperforms the current 1-bit matrix completion methods. Meanwhile, it is compelling that CDMC can successfully capture items' genre features only based on sparse binary user-item interactive data. Notably, GS1MC provides a new insight to incorporate and evaluate the efficacy of clustering methods while CDMC can be served as a new tool to explore unrevealed social behavior or market phenomenon.