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SCIENTIA SINICA Informationis, Volume 50 , Issue 7 : 937-956(2020) https://doi.org/10.1360/SSI-2019-0274

A survey on knowledge graph-based recommender systems

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  • ReceivedDec 5, 2019
  • AcceptedFeb 19, 2020
  • PublishedJul 14, 2020

Abstract

Recommender system (RS) targets at providing accurate item recommendations to users with respect to their preferences; it has been widely employed in various online applications for addressing the problem of information explosion and improving user experience. In the past decades, while tremendous efforts have been made in enhancing the performance of RSs, some long-standing challenges, such as data sparsity, cold start, and result diversity, are unaddressed. Along this line, an emerging research trend is to exploit the rich semantic information contained in the knowledge graph (KG); it has been proven to be an effective way to enhance the capability of RSs. To this end, we provide a focused survey on KG-based RS via a holistic perspective of both technologies and applications. Specifically, firstly, we briefly review the core concepts and classical algorithms of the RSs and KGs. Secondly, we comprehensively introduce the representative and state-of-the-art works in this field based on different strategies of exploiting KGs for RSs. Meanwhile, we also summarize some typical application scenarios of KG-based RSs, for facilitating the hands-on practices of corresponding algorithms. Finally, we present our opinions on the prospects of KG-based RS and suggest some future research directions in this area.


Funded by

国家自然科学基金(91746301,71531001,61836013,U1836206,61773361)


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  • Figure 1

    (Color online) An illustration of explainable knowledge graph-based recommendation

  • Figure 2

    (Color online) An illustration of leveraging knowledge graph based heterogeneous information network (HIN) in recommender system

  • Figure 3

    (Color online) The motivations of four traditional knowledge graph embedding approaches. (a) TransE; protectłinebreak (b) TransR; (c) TransH; (d) TransD

  • Figure 4

    (Color online) An illustration of entity linking for news data

  • Figure 5

    (Color online) An illustration of two explainable knowledge graph-based recommender system

  • Table 1   A summary of common knowledge graphs
    Name Organization Data source Domain Is open-source
    YAGO KG [37] Max Planck Institute WordNet, Wikipedia General Yes
    DBpedia KG [38] DBpedia Association Wikipedia, Expert knowledge General Yes
    Freebase KG [39] Google Wikipedia General Yes
    NELL KG [40] Carnegie Mellon University Web data General Yes
    Wikidata [41] Wikimedia Deutschland Wikipedia, Freebase General Yes
    Google's Knowledge Graph Google Freebase, Web data General Yes
    Microsoft Satori Microsoft Web data General No
    Baidu's Knowledge Graph Baidu Web data General No
    OwnThink KG OwnThink Web data General Yes
    CN-DBpedia [42] Fudan University Chinese encyclopedia website General Yes
    WordNet [43] Princeton University Expert knowledge Linguistics Yes
    UMLS KG National Library of Medicine Medical literature Medical Yes
    Douban's movie KG Zhejiang University Douban data Movie Yes
    MusicBrainz MetaBrainz Foundation Web data Music Yes
  • Table 2   A lookup table for relevant publications
    Category Year Ref.
    Embedding-based methods Before 2018 [10,62,63]
    2018 [2,11-67]
    2019 [44,47-73]
    Path-based methods Before 2018 [16,21,53]
    2018 [18]
    2019 [14,19,20,45,74]
  • Table 3   A lookup table for relevant datasets
    Category Data Ref. Category Data Ref.
    Movie MovieLens-100K [16,45,90] Product Amazon Electronics [21]
    MovieLens-1M [10,12,13,18,19,44,46,47,50,63,74,91] Amazon e-commerce [66,67]
    MovieLens-20M [12,20,20,48,61,72,91] POI Yelp challenge [14,16,18,21,45,46,53,90]
    DoubanMovie [52,53,71] Dianping-Food [72]
    Book BDbook2014 [19,47] CEM [51]
    Book-Crossing [13,44,50,61,72,74,91] Music Last.FM [12,14,19,45,46,48,50,72]
    Amazon-Book [12,14,91] NetEase Cloud Music [64]
    Intent Book [10] Medicine TCM [68]
    News Bing-News [2,13,50,61] MIMIC-III [62]

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