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Article citations


Palumbo, E., Rizzo, G. and Troncy, R. (2017) Entity2rec: Learning User-Item Relatedness from Knowledge Graphs for Top-N Item Recommendation. Proceedings of the Eleventh ACM Conference on Recommender Systems, Como, 27-31 August 2017, 32-36.

has been cited by the following article:

  • TITLE: Knowledge Driven Paper Recommendation Using Heterogeneous Network Embedding Method

    AUTHORS: Irfan Ahmed, Zubair Ahmed Kalhoro

    KEYWORDS: Network Embedding, Heterogeneous Representation Learning, Paper-Citation Relations, Recommender System, Learning Latent Representations

    JOURNAL NAME: Journal of Computer and Communications, Vol.6 No.12, December 28, 2018

    ABSTRACT: We search a variety of things over the Internet in our daily lives, and numerous search engines are available to get us more relevant results. With the rapid technological advancement, the internet has become a major source of obtaining information. Further, the advent of the Web2.0 era has led to an increased interaction between the user and the website. It has become challenging to provide information to users as per their interests. Because of copyright restrictions, most of existing research studies are confronting the lack of availability of the content of candidates recommending articles. The content of such articles is not always available freely and hence leads to inadequate recommendation results. Moreover, various research studies base recommendation on user profiles. Therefore, their recommendation needs a significant number of registered users in the system. In recent years, research work proves that Knowledge graphs have yielded better in generating quality recommendation results and alleviating sparsity and cold start issues. Network embedding techniques try to learn high quality feature vectors automatically from network structures, enabling vector-based measurers of node relatedness. Keeping the strength of Network embedding techniques, the proposed citation-based recommendation approach makes use of heterogeneous network embedding in generating recommendation results. The novelty of this paper is in exploiting the performance of a network embedding approach i.e., matapath2vec to generate paper recommendations. Unlike existing approaches, the proposed method has the capability of learning low-dimensional latent representation of nodes (i.e., research papers) in a network. We apply metapath2vec on a knowledge network built by the ACL Anthology Network (all about NLP) and use the node relatedness to generate item (research article) recommendations.