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Title:Recommendations in text-rich heterogeneous networks
Author(s):Raj, Jeetu
Advisor(s):Han, Jiawei
Department / Program:Computer Science
Discipline:Computer Science
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Network Mining
Deep Learning
Heterogeneous Networks
Text-Rich Networks
Abstract:In this work, we study the problem of performing recommendations in a Heterogeneous Network which has auxiliary text information present with the nodes. We cover the relevant background and illustrate heterogeneous networks along with related tasks through the help of examples. We choose the setting of Bibliographic Heterogeneous Network and devise a Citation Recommendation system that integrates the various sources of information present in the network. We utilize specific similarity matrices to compare the query paper with the set of candidate papers which enables us to capture the query-specific context of the candidate papers. Our proposed approach employs suitably transformed embeddings to create the similarity matrices and follows-up with convolution neural networks. We demonstrate the effectiveness of our solution over two popular datasets where our method outperforms several network and/or text-based methods. We also perform a thorough qualitative analysis based on sample queries to show the effectiveness of our model in holistically combining heterogeneous information.
Issue Date:2020-05-11
Rights Information:Copyright 2020 Jeetu Raj
Date Available in IDEALS:2020-08-27
Date Deposited:2020-05

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