Context-Aware Network Embedding via Variation Autoencoders for Link Prediction

Tian, Long; Zhang, Dejun; Han, Fei; Hong, Mingbo; Huang, Xiang; Chen, Yilin; Wu, Yan

doi:10.1007/978-981-13-2203-7_24

Cited by 1 publication

(1 citation statement)

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“…The mutual attention mechanism is used to obtain contextaware text embedding. Based on this work, the studies in [Tian et al 2018;Shen et al 2018;Chen et al 2019b] ) depends on the attention between v and n i , where v is the node to be recommended. Therefore, choosing different v for recommendation would result in different embeddings of u.…”

Section: Learning Variable Node Embeddings In Recommendation Tasksmentioning

confidence: 99%

RTANet: Recommendation Target-Aware Network Embedding

Cao

Yin

Song

et al. 2023

ICWSM

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Network embedding is a process of encoding nodes into latent vectors by preserving network structure and content information. It is used in various applications, especially in recommender systems. In a social network setting, when recommending new friends to a user, the similarity between the user's embedding and the target friend will be examined. Traditional methods generate user node embedding without considering the recommendation target. No matter which target is to be recommended, the same embedding vector is generated for that particular user. This approach has its limitations. For example, a user can be both a computer scientist and a musician. When recommending music friends with potentially the same taste to him, we are interested in getting his representation that is useful in recommending music friends rather than computer scientists. His corresponding embedding should consider the user's musical features rather than those associated with computer science with the awareness that the recommendation targets are music friends. In order to address this issue, we propose a new framework which we name it as Recommendation Target-Aware Network embedding method (RTANet). Herein, the embedding of each user is no longer fixed to a constant vector, but it can vary according to their specific recommendation target. Concretely, RTANet assigns different attention weights to each neighbour node, allowing us to obtain the user's context information aggregated from its neighbours before transforming this context into its embedding. Different from other graph attention approaches, the attention weights in our work measure the similarity between each user's neighbour node and the target node, which in return generates the target-aware embedding. To demonstrate the effectiveness of our method, we compared RTANet with several state-of-the-art network embedding methods on four real-world datasets and showed that RTANet outperforms other comparative methods in the recommendation tasks.

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Section: Learning Variable Node Embeddings In Recommendation Tasksmentioning

confidence: 99%