MNI: An enhanced multi-task neighborhood interaction model for recommendation on knowledge graph

Abstract

To alleviate the data sparsity and cold start problems for collaborative filtering in recommendation systems, side information is usually leveraged by researchers to improve the recommendation performance. The utility of knowledge graph regards the side information as part of the graph structure and gives an explanation for recommendation results. In this paper, we propose an enhanced multi-task neighborhood interaction (MNI) model for recommendation on knowledge graphs. MNI explores not only the user-item interaction but also the neighbor-neighbor interactions, capturing a more sophisticated local structure. Besides, the entities and relations are also semantically embedded. And with the cross&compress unit, items in the recommendation system and entities in the knowledge graph can share latent features, and thus high-order interactions can be investigated. Through extensive experiments on real-world datasets, we demonstrate that MNI outperforms some of the state-of-the-art baselines both for CTR prediction and top-N recommendation.

Knowledge distillation for multi-depth-model-fusion recommendation algorithm

“…In order to alleviate the data sparsity and cold start problems in recommender systems, Ma et al [ 30 ] constructed a knowledge graph and made recommendations through various information such as user-item, neighbor-neighbor, etc. Ye et al [ 31 ] obtained low-dimensional representations of various entities by constructing a knowledge graph, and then input them into a neural decomposition machine for recommendation.…”