Combining feature importance and neighbor node interactions for cold start recommendation

Cold start recommendation usually views preference embedding as a missing problem because there is not any historical interaction. Existing approaches on graph neural networks for cold start users/items build the attribute embedding of each node through simply concatenating multiple features equally, and then reconstruct node preference embedding from its attribute embedding through a mapping function which is learned from warm users/items. However, these approaches do not consider the different contributions of features for building the attribute embedding. In addition, they assume the neighbors of a target node are independent and ignore interactions between the neighbor nodes when building the mapping function between the attribute embedding and the preference embedding. These two limitations reduce the effectiveness of their performance. To overcome these limitations, we propose a novel framework called Feature Importance and Neighbor node Interactions graph neural network (FINI) that exploits feature weights and interactions between neighbor nodes. The core ideas of the proposed method are as follows. First, it designs a global–local contexts attention mechanism in the attribute encoding layer, which can dynamically learn the importance of the attributes of each node and improve the expression of the feature embeddings. Second, it proposes a mixed interaction mechanism to augment the weighted information of neighbor node interactions in the neighbor interaction layer, which can strengthen the expressive capability of the user/item embeddings and further improve the quality of the mapping function for cold start users/items. Additionally, we also combine the rating prediction loss and mimic loss as the total loss to train the network in the prediction layer for model training. To assess the performance of the FINI, both cold start users and cold start items recommendation are considered. The results demonstrate FINI outperforms the state-of-the-art approaches for cold start recommendation and gains significant improvements in terms of metric evaluations.