Abstract: As the volume of internet information grows exponentially,recommender systems play a crucial role in addressing information overload.In response to the deficiencies in entity and relation representations within existing recommendation systems,this paper proposes an enhanced model termed representation enhanced ripplenet(RE-RippleNet).On one hand,traditional models tend to overlook the semantic information inherent in relationships.By aggregating neighboring entities and relationships into the embedded representation of entities,the expressive power of entity embedding and the accuracy of user representation are improved.On the other hand,during the aggregation process of multi-hop ripple sets for propagating user preferences,a long short-term memory(LSTM) network is employed to capture the diverse influences and characteristics of user preference representations across different hops,facilitating a deeper exploration of user preferences and more precise recommendations.Click-through rate prediction experiments on two public datasets,MovieLens-1M and Book-Crossing,demonstrate that RE-RippleNet achieves significant improvements in accuracy(ACC) and AUC metrics,compared to the baseline RippleNet model.Specifically,ACC and AUC increases by 1.7% and 1.2% respectively on the MovieLens-1M dataset,and by 3.6% and 1.6% on the Book-Crossing dataset,validating the model’s effectiveness in enhancing recommender system performance.

Key words: Recommender systems, Knowledge graph, RippleNet, Representation enhancement, Long and short-term memory