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HyperTeNet: Hypergraph and Transformer-based Neural Network for Personalized List Continuation

Vijaikumar, M and Hada, D and Shevade, S (2021) HyperTeNet: Hypergraph and Transformer-based Neural Network for Personalized List Continuation. In: 21st IEEE International Conference on Data Mining, ICDM 2021, 7-10 Dec 2021, Virtual, Online, pp. 1210-1215.

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Official URL: https://doi.org/10.1109/ICDM51629.2021.00146

Abstract

The personalized list continuation (PLC) task is to curate the next items to user-generated lists (ordered sequence of items) in a personalized way. The main challenge in this task is understanding the ternary relationships among the interacting entities (users, items, and lists) that the existing works do not consider. Further, they do not take into account the multi-hop relationships among entities of the same type. In addition, capturing the sequential information amongst the items already present in the list also plays a vital role in determining the next relevant items that get curated.In this work, we propose HyperTeNet - a self-attention hypergraph and Transformer-based neural network architecture for the personalized list continuation task to address the challenges mentioned above. We use graph convolutions to learn the multi-hop relationship among the entities of the same type and leverage a self-attention-based hypergraph neural network to learn the ternary relationships among the interacting entities via hyperlink prediction in a 3-uniform hypergraph. Further, the entity embeddings are shared with a Transformer-based architecture and are learned through an alternating optimization procedure. As a result, this network also learns the sequential information needed to curate the next items to be added to the list. Experimental results demonstrate that HyperTeNet significantly outperforms the other state-of-the-art models on real-world datasets. Our implementation is available online1.1https://github.com/mvijaikumar/HyperTeNet © 2021 IEEE.

Item Type: Conference Paper
Publication: Proceedings - IEEE International Conference on Data Mining, ICDM
Publisher: Institute of Electrical and Electronics Engineers Inc.
Additional Information: The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords: Graph neural networks; Hypertext systems; Network architecture, Graph neural networks; Hyper graph; Hypergraph neural network; Interacting entities; Learn+; Multi-hops; Neural-networks; Personalized list continuation; Sequential information; Sequential recommende system, Recommender systems
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 09 Mar 2022 10:28
Last Modified: 09 Mar 2022 10:28
URI: http://eprints.iisc.ac.in/id/eprint/71528

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