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JIT2R: A Joint Framework for Item Tagging and Tag-based Recommendation

Predicting tags for a given item and leveraging tags to assist item recommendation are two popular research topics in the field of recommender system. Previous studies mostly focus only one of them to make contributions. However, we believe that these tasks are inherently correlated with each other: tags can provide additional information to profile items for more accurate recommendation; user behaviors can help to infer item relationships to benefit the item tagging process. In order to take the advantages of such mutually influential signals, we propose to integrate item tagging and tag-based recommendation into a unified model. We firstly design a basic framework, where the user-item interaction signals are leveraged to supervise the item tagging process. Then we extend the basic model with a bootstrapping technique to circulate such mutual improvements between different tasks. We conduct extensive experiments based on real-word datasets to demonstrate our model's superiorities.