Heterogeneous information network-based music recommendation system in mobile networks

Wang, Ranran; Ma, Xiao; Jiang, Chi; Ye, Yi; Zhang, Yin

doi:10.1016/j.comcom.2019.12.002

Cited by 42 publications

(21 citation statements)

References 19 publications

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“…Linked Open Data has been used as an external knowledge graph adopted within a hybrid graph data model [16]. In [17], they propose a heterogeneous information network-based music recommendation system that uses a graph-based algorithm to generate recommendations. To improve social trust and influence, the authors [18] propose a graph-based model for the social recommendation.…”

Section: Graph-based Recommender Systemmentioning

confidence: 99%

Towards a folksonomy graph-based context-aware recommender system of annotated books

et al. 2021

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The emergence of collaborative interactions has empowered users by enabling their interactions through tagging practices that create a folksonomy, also called, classification of the shared resources, any identifiable thing or item on the system. In education, tagging is considered a powerful meta-cognitive strategy that successfully engages learners in the learning process. Besides, the collaborative tagging gathers learners’ opinions, thus, provides more comprehensible recommendations. Still, the abundant shared contents are mostly unorganized which makes it hard for users to select and discover the appropriate items of their interests. Thus, the use of recommender systems overcomes the distressing search problem by assisting users in their searching and exploring experience, and suggesting relevant items matching their preferences. In this regard, this article presents a folksonomy graphs based context-aware recommender system (CARS) of annotated books. The generated graphs express the semantic relatedness between these resources, i.e. books, by effectively modeling the folksonomy relationship between user-resource-tag and integrating contextual information within a multi-layer graph referring to a Knowledge Graph (KG). To put our proposal into shape, we model a real-world application of Goodbooks-10k dataset to recommend books. The proposed approach incorporates spectral clustering to deal with the graph partitioning problem. The experimental evaluation shows relevant performance results of graph-based book recommendations.

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Section: Graph-based Recommender Systemmentioning

confidence: 99%

Towards a folksonomy graph-based context-aware recommender system of annotated books

et al. 2021

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“…In order to overcome the problem that the meta path can only express simple information, Cheng et al ( 2017 ) proposed meta structure to measure the similarity between the objects. Until today, HINs have been widely used in other fields (Wang, 2019 ; Wang et al, 2020 ; Zhang et al, 2020 ).…”

Section: Related Workmentioning

confidence: 99%

Heterogeneous Information Network-Based Patient Similarity Search

Huang

Lü

Guo

et al. 2021

Front. Cell Dev. Biol.

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Patient similarity search is a fundamental and important task in artificial intelligence-assisted medicine service, which is beneficial to medical diagnosis, such as making accurate predictions for similar diseases and recommending personalized treatment plans. Existing patient similarity search methods retrieve medical events associated with patients from Electronic Health Record (EHR) data and map them to vectors. The similarity between patients is expressed by calculating the similarity or dissimilarity between the corresponding vectors of medical events, thereby completing the patient similarity measurement. However, the obtained vectors tend to be high dimensional and sparse, which makes it hard to calculate patient similarity accurately. In addition, most of existing methods cannot capture the time information in the EHR, which is not conducive to analyzing the influence of time factors on patient similarity search. To solve these problems, we propose a patient similarity search method based on a heterogeneous information network. On the one hand, the proposed method uses a heterogeneous information network to connect patients, diseases, and drugs, which solves the problem of vector representation of mixed information related to patients, diseases, and drugs. Meanwhile, our method measures the similarity between patients by calculating the similarity between nodes in the heterogeneous information network. In this way, the challenges caused by high-dimensional and sparse vectors can be addressed. On the other hand, the proposed method solves the problem of inaccurate patient similarity search caused by the lack of use of time information in the patient similarity measurement process by encoding time information into an annotated heterogeneous information network. Experiments show that our method is better than the compared baseline methods.

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“…In particular, some scholars have proposed the graph attention mechanism to improve the performance of node classification [50]. Some scholars have recommended graphs for recommendation systems [51], such as the music recommendation system in mobile networks [52,53] because of graphs' powerful information representation abilities and wide applications. Specifically, Zhang [54] used bipartite graph to perform context-sensitive web service discovery.…”

Section: Related Workmentioning

confidence: 99%

Data Mining of Students’ Consumption Behaviour Pattern Based on Self-Attention Graph Neural Network

2021

Applied Sciences

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Performance prediction is of significant importance. Previous mining of behaviour data was limited to machine learning models. Corresponding research has not made good use of the information of spatial location changes over time, in addition to discriminative students’ behavioural patterns and tendentious behaviour. Thus, we establish students’ behaviour networks, combine temporal and spatial information to mine behavioural patterns of academic performance discrimination, and predict student’s performance. Firstly, we put forward some principles to build graphs with a topological structure based on consumption data; secondly, we propose an improved self-attention mechanism model; thirdly, we perform classification tasks related to academic performance, and determine discriminative learning and life behaviour sequence patterns. Results showed that the accuracy of the two-category classification reached 84.86% and that of the three-category classification reached 79.43%. In addition, students with good academic performance were observed to study in the classroom or library after dinner and lunch. Apart from returning to the dormitory in the evening, they tended to stay focused in the library and other learning venues during the day. Lastly, different nodes have different contributions to the prediction, thereby providing an approach for feature selection. Our research findings provide a method to grasp students’ campus traces.

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Heterogeneous information network-based music recommendation system in mobile networks

Cited by 42 publications

References 19 publications

Towards a folksonomy graph-based context-aware recommender system of annotated books

Towards a folksonomy graph-based context-aware recommender system of annotated books

Heterogeneous Information Network-Based Patient Similarity Search

Data Mining of Students’ Consumption Behaviour Pattern Based on Self-Attention Graph Neural Network

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