Knowledge-enhanced recommendation algorithms for multi-task learning with interactive attention

Abstract

Compared with the traditional knowledge graph-enhanced recommendation method, this paper introduces a multi-task learning module to alternately train knowledge graphs and recommendations to alleviate the data sparsity and cold start problems in traditional recommendation methods. Specifically, in the multi-task learning module, the item features and contextual content features are taken, and the features after feature interaction are obtained using the interactive attention network, as a way to learn finer-grained features, and then the gating mechanism processes the item features and entity features that fuse the contextual content, which can filter the unimportant features and obtain the important potential features, and can capture the implicit higher-order feature interaction more effectively. Optimized for multi-task learning tasks. The validity of our model was verified on three publicly available datasets.