@unpublished{mao_application_2021,
	title = {Application of {Knowledge} {Graphs} to {Provide} {Side} {Information} for {Improved} {Recommendation} {Accuracy}},
	url = {http://arxiv.org/abs/2101.03054},
	abstract = {Personalized recommendations are popular in these days of Internet driven
activities, specifically shopping. Recommendation methods can be grouped
into three major categories, content based filtering, collaborative
filtering and machine learning enhanced. Information about products and
preferences of different users are primarily used to infer preferences for
a specific user. Inadequate information can obviously cause these methods
to fail or perform poorly. The more information we provide to these
methods, the more likely it is that the methods perform better. Knowledge
graphs represent the current trend in recording information in the form of
relations between entities, and can provide additional (side) information
about products and users. Such information can be used to improve nearest
neighbour search, clustering users and products, or train the neural
network, when one is used. In this work, we present a new generic
recommendation systems framework, that integrates knowledge graphs into
the recommendation pipeline. We describe its software design and
implementation, and then show through experiments, how such a framework
can be specialized for a domain, say movie recommendations, and the
improvements in recommendation results possible due to side information
obtained from knowledge graphs representation of such information. Our
framework supports different knowledge graph representation formats, and
facilitates format conversion, merging and information extraction needed
for training recommendation methods.},
	author = {Mao, Yuhao and Mokhov, Serguei A and Mudur, Sudhir P},
	month = jan,
	year = {2021},
	doi = {10.1007/s11257-018-9213-x},
	note = {ISBN: 2101.03054
Publication Title: arXiv [cs.IR]},
}

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Knowledge graphs represent the current trend in recording information in the form of relations between entities, and can provide additional (side) information about products and users. Such information can be used to improve nearest neighbour search, clustering users and products, or train the neural network, when one is used. In this work, we present a new generic recommendation systems framework, that integrates knowledge graphs into the recommendation pipeline. We describe its software design and implementation, and then show through experiments, how such a framework can be specialized for a domain, say movie recommendations, and the improvements in recommendation results possible due to side information obtained from knowledge graphs representation of such information. Our framework supports different knowledge graph representation formats, and facilitates format conversion, merging and information extraction needed for training recommendation methods.","author":[{"propositions":[],"lastnames":["Mao"],"firstnames":["Yuhao"],"suffixes":[]},{"propositions":[],"lastnames":["Mokhov"],"firstnames":["Serguei","A"],"suffixes":[]},{"propositions":[],"lastnames":["Mudur"],"firstnames":["Sudhir","P"],"suffixes":[]}],"month":"January","year":"2021","doi":"10.1007/s11257-018-9213-x","note":"ISBN: 2101.03054 Publication Title: arXiv [cs.IR]","bibtex":"@unpublished{mao_application_2021,\n\ttitle = {Application of {Knowledge} {Graphs} to {Provide} {Side} {Information} for {Improved} {Recommendation} {Accuracy}},\n\turl = {http://arxiv.org/abs/2101.03054},\n\tabstract = {Personalized recommendations are popular in these days of Internet driven\nactivities, specifically shopping. Recommendation methods can be grouped\ninto three major categories, content based filtering, collaborative\nfiltering and machine learning enhanced. Information about products and\npreferences of different users are primarily used to infer preferences for\na specific user. Inadequate information can obviously cause these methods\nto fail or perform poorly. The more information we provide to these\nmethods, the more likely it is that the methods perform better. Knowledge\ngraphs represent the current trend in recording information in the form of\nrelations between entities, and can provide additional (side) information\nabout products and users. Such information can be used to improve nearest\nneighbour search, clustering users and products, or train the neural\nnetwork, when one is used. In this work, we present a new generic\nrecommendation systems framework, that integrates knowledge graphs into\nthe recommendation pipeline. We describe its software design and\nimplementation, and then show through experiments, how such a framework\ncan be specialized for a domain, say movie recommendations, and the\nimprovements in recommendation results possible due to side information\nobtained from knowledge graphs representation of such information. Our\nframework supports different knowledge graph representation formats, and\nfacilitates format conversion, merging and information extraction needed\nfor training recommendation methods.},\n\tauthor = {Mao, Yuhao and Mokhov, Serguei A and Mudur, Sudhir P},\n\tmonth = jan,\n\tyear = {2021},\n\tdoi = {10.1007/s11257-018-9213-x},\n\tnote = {ISBN: 2101.03054\nPublication Title: arXiv [cs.IR]},\n}\n\n","author_short":["Mao, Y.","Mokhov, S. A","Mudur, S. 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