Enhancing Knowledge of Propagation-Perception-Based Attention Recommender Systems

Abstract

Researchers have introduced side information such as social networks or knowledge graphs to alleviate the problems of data sparsity and cold starts in recommendation systems. However, most of the methods ignore the exploration of feature differentiation aspects in the knowledge propagation process. To solve the above problem, we propose a new attention recommendation method based on an enhanced knowledge propagation perception. Specifically, to capture user preferences in a fine-grained manner in a knowledge graph, an asymmetric semantic attention mechanism is adopted. It identifies the influence of propagation neighbors on user preferences through a more precise representation of the preference semantics for head and tail entities. Furthermore, in consideration of the memory and generalization of different propagation depth features and adaptively adjusting the propagation weights, a new propagation feature exploration framework is designed. The performance of the proposed model is validated by two real-world datasets. The baseline model averagely increases by 9.65% and 9.15% for the Area Under Curve (AUC) and Accuracy (ACC) indicators, which proves the effectiveness of the model.

Recommendation of Online Business English Learning Resources Integrating Attention Mechanism and Collaborative Filtering Model

“…In interaction rate prediction, the top-K learning resources were recommended for learners in the test set for the top-K recommendation task, and the model performance was evaluated by using Precision@K and Recall@K indicators. e algorithm in this paper was compared with recent literature [17][18][19][20]. Figures 7 and 8 show the comparison between Precision@K and Recall@K in top-K services.…”

“…Its deficiency lies in that it does not use the attention mechanism to improve the information quality at the learning resource end. Similar to literature [17], literature [18] focuses on the learning resource end, integrates the neighbour nodes of learning resources to obtain its embedded representation, and enriches the learner embedded representation without utilizing the information of the knowledge graph. e advantage of literature [19] is that both the learner end and the learning resource end are taken into account.…”

“…is results in the lack of knowledge characteristic information on the learner side, which leads to the lack of semantic richness of the learner's embedded Literature [20] Literature [17] Literature [18] Literature [19] Proposed representation. e model proposed in this paper makes full use of the heterogeneous information of attention mechanism at both the learner side and the learning resource side and integrates the entity information between the learner's interactive learning resource and learning target and its neighbour information into the vector embedded representation of the learner, thus resulting in a significant improvement in performance.…”

“…In future work, we try to add more contextual information, social network information, and other auxiliary information to enhance the expressive force of the model. Literature [20] Literature [17] Literature [18] Literature [19] Proposed…”