Prediction of higher-order links using global vectors and Hasse diagrams

Anjan, K; Andreopoulos, Willam; Potika, Katerina

doi:10.1109/bigdata52589.2021.9671432

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…There is still significant scope to explore, like the different mechanisms for forming connections between items, calculating user and item similarities, exploring higher-order connections [31,32] instead of edges, and investigating graph reasoning algorithms to learn users' preferences better. In addition, we plan to leverage our previous work on influential users [2,23,33,34] to refine the user-user similarities and create neighborhoods of influence.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Using Graph Neural Networks for Social Recommendations

Tallapally,

Wang,

Potika

et al. 2023

Algorithms

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Recommender systems have revolutionized the way users discover and engage with content. Moving beyond the collaborative filtering approach, most modern recommender systems leverage additional sources of information, such as context and social network data. Such data can be modeled using graphs, and the recent advances in Graph Neural Networks have led to the prominence of a new family of graph-based recommender system algorithms. In this work, we propose the RelationalNet algorithm, which not only models user–item, and user–user relationships but also item–item relationships with graphs and uses them as input to the recommendation process. The rationale for utilizing item–item interactions is to enrich the item embeddings by leveraging the similarities between items. By using Graph Neural Networks (GNNs), RelationalNet incorporates social influence and similar item influence into the recommendation process and captures more accurate user interests, especially when traditional methods fall short due to data sparsity. Such models improve the accuracy and effectiveness of recommendation systems by leveraging social connections and item interactions. Results demonstrate that RelationalNet outperforms current state-of-the-art social recommendation algorithms.

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