Collaborative filtering (CF) usually suffers from limited performance in recommendation systems due to the sparsity of user–item interactions and cold start problems. To address these issues, auxiliary information from knowledge graphs, such as social networks and item properties, is typically used to boost performance. The current recommended algorithms based on knowledge graphs fail to utilize rich semantic associations. In this paper, we regard knowledge graphs as heterogeneous networks to add auxiliary information, propose a recommendation system with unified embeddings of behavior and knowledge features, and mine user preferences from their historical behavior and knowledge graphs to provide more accurate and diverse recommendations to the users. Our proposed ReBKC shows a significant improvement on three datasets compared to state-of-the-art methods. These results verify the effectiveness of learning short-term and long-term user preferences from their historical behavior and by integrating knowledge graphs to deeply identify user preferences.

Personalized recommendation system based on knowledge embedding and historical behavior

In the context of Internet technology, the integration of information technology and education is a powerful supplement to the traditional teaching model of higher education. Online learning has become the new development direction of the education industry in the network era. To address the problems of serious difficulty in completing online teaching tasks, difficulty in monitoring teaching effects, and fragmentation of course resources in universities, a multimodal music knowledge graph is constructed. A personalized learning strategy based on users' interest is proposed through the mining of online education data, and a music online education system has been developed on this basis. To improve the recommendation accuracy of the model, an embedding propagation knowledge graph recommendation method based on decay factors is proposed. The model considers the changes in the strength of user interest during the intra- and interlayer propagation of the knowledge graph interest map and focuses on higher-order user potential interest representations for enhancing the semantic relevance of multihop entities. The experimental results show that the proposed model brings a good prediction effect on several benchmark evaluation metrics and outperforms other comparative algorithms regarding recommendation accuracy.

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A Multimodal Fusion Online Music Education System for Universities

The transition from Industry 4.0 to Industry 5.0 started when personalization options became available to customers. This revolution aims to bring back the human touch with the convergence of advanced technology towards a degree of personalization to meet the demand of the customers. In the era of Industry 5.0, consumers want to differentiate themselves as unique, and personalized products allow them to express themselves as individuals. This has prompted personal recommendations to become more popular. Despite the increasing popularity of personalized recommendations, little research has been conducted on the impact of these recommendations on user satisfaction. As a result, an online survey was conducted to test the relationships between personalized product recommendations and user satisfaction and proposed a conceptual model. The findings of the study indicated a positive association between personalized product recommendations and consumer satisfaction and highlighted several managerial and practical implications that academics and retailers may find useful. 

Personalized Product Recommendation and User Satisfaction

Point-of-interest (POI) recommendation is a type of recommendation task, which generates a list of places that users may be interested in. There is a complex heterogeneous graph structure between users and points of interest. The current recommendation algorithms are generally based on Euclidean space data, and the algorithms based on graph structure also generally use homogeneous graph convolution. To solve these problems, the author proposes a heterogeneous graph convolution network algorithm based on hierarchical subgraphs (HGCNR). By constructing user-centered subgraph layers and interest point-cantered subgraph layers, respectively, the author performs heterogeneous graph convolution on different subgraphs to obtain more effective user node information and interest point node information and form recommendation result. Experiments on two public data sets show that HGCNR can effectively improve the recommendation performance of interest points and achieve better recommendation results.

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A POI Recommendation Algorithm Based on the Heterogeneous Graph Convolution Network

Knowledge graph is considered as a significant information structure for recommendation, therefore the graph based recommendation has attracted increasing attention in recent years. However, the existing methods face two major challenges. First, the users' preferences should be well considered in the algorithmic model and explicitly shown after model training. Second, there need a better solution to simultaneously learn and combine the information on multiple graphs from different aspects rather than the methods designed for single homogeneous graph. In this paper, we propose the preferences embeddings, which are able to learn the explicit representations for the preferences that influence the users' choices. Further, we innovatively design three channels in a new graph neural network that contains different graph convolutions specifically for the recommendation scenario. This framework can effectively excavate and combine heterogeneous information among user graph, item graph and interaction graph. Extensive experiments on real-world datasets demonstrate the effectiveness and good interpretability of the proposed framework.

Hong Zhao

Papers

Preference-aware Heterogeneous Graph Neural Networks for Recommendation