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Leveraging online behaviors for interpretable knowledge-aware patent recommendation

Leveraging online behaviors for interpretable knowledge-aware patent recommendation Patent trade recommendations necessitate recommendation interpretability in addition to recommendation accuracy because of patent transaction risks and the technological complexity of patents. This study designs an interpretable knowledge-aware patent recommendation model (IKPRM) for patent trading. IKPRM first creates a patent knowledge graph (PKG) for patent trade recommendations and then leverages paths in the PKG to achieve recommendation interpretability.Design/methodology/approachFirst, we construct a PKG to integrate online company behaviors and patent information using natural language processing techniques. Second, a bidirectional long short-term memory network (BiLSTM) is utilized with an attention mechanism to establish the connecting paths of a company — patent pair in PKG. Finally, the prediction score of a company — patent pair is calculated by assigning different weights to their connecting paths. The semantic relationships in connecting paths help explain why a candidate patent is recommended.FindingsExperiments on a real dataset from a patent trading platform verify that IKPRM significantly outperforms baseline methods in terms of hit ratio and normalized discounted cumulative gain (nDCG). The analysis of an online user study verified the interpretability of our recommendations.Originality/valueA meta-path-based recommendation can achieve certain explainability but suffers from low flexibility when reasoning on heterogeneous information. To bridge this gap, we propose the IKPRM to explain the full paths in the knowledge graph. IKPRM demonstrates good performance and transparency and is a solid foundation for integrating interpretable artificial intelligence into complex tasks such as intelligent recommendations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Internet Research Emerald Publishing

Leveraging online behaviors for interpretable knowledge-aware patent recommendation

Du, Wei; Yan, Qiang; Zhang, Wenping; Ma, Jian
Internet Research , Volume 32 (2): 20 – Mar 15, 2022
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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1066-2243
DOI
10.1108/intr-08-2020-0473
Publisher site
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Abstract

Patent trade recommendations necessitate recommendation interpretability in addition to recommendation accuracy because of patent transaction risks and the technological complexity of patents. This study designs an interpretable knowledge-aware patent recommendation model (IKPRM) for patent trading. IKPRM first creates a patent knowledge graph (PKG) for patent trade recommendations and then leverages paths in the PKG to achieve recommendation interpretability.Design/methodology/approachFirst, we construct a PKG to integrate online company behaviors and patent information using natural language processing techniques. Second, a bidirectional long short-term memory network (BiLSTM) is utilized with an attention mechanism to establish the connecting paths of a company — patent pair in PKG. Finally, the prediction score of a company — patent pair is calculated by assigning different weights to their connecting paths. The semantic relationships in connecting paths help explain why a candidate patent is recommended.FindingsExperiments on a real dataset from a patent trading platform verify that IKPRM significantly outperforms baseline methods in terms of hit ratio and normalized discounted cumulative gain (nDCG). The analysis of an online user study verified the interpretability of our recommendations.Originality/valueA meta-path-based recommendation can achieve certain explainability but suffers from low flexibility when reasoning on heterogeneous information. To bridge this gap, we propose the IKPRM to explain the full paths in the knowledge graph. IKPRM demonstrates good performance and transparency and is a solid foundation for integrating interpretable artificial intelligence into complex tasks such as intelligent recommendations.

Journal

Internet Research – Emerald Publishing

Published: Mar 15, 2022

Keywords: Interpretable knowledge-aware recommendation; Patent recommendation; Online behaviors

References

  • Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions
    Adomavicius, G.; Tuzhilin, A.
  • Tensor decompositions for learning latent variable models
    Anandkumar, A.; Ge, R.; Hsu, D.; Kakade, S.M.; Telgarsky, M.
  • Innovation and competitive advantage creation: the role of organisational leadership in service firms from emerging markets
    Anning-Dorson, T.
  • Playing in between: patent brokers in markets for technology
    Benassi, M.; Di Minin, A.
  • Recommender systems survey
    Bobadilla, J.; Ortega, F.; Hernando, A.; Gutiérrez, A.
  • Translating embeddings for modeling multi-relational data
    Bordes, A.; Usunier, N.; Garcia-Duran, A.; Weston, J.; Yakhnenko, O.
  • Machine learning interpretability: a survey on methods and metrics
    Carvalho, D.V.; Pereira, E.M.; Cardoso, J.S.
  • Named entity recognition with bidirectional LSTM-CNNs
    Chiu, J.P.C.; Nichols, E.
  • Prediction of emerging technologies based on analysis of the US patent citation network
    Érdi, P.; Makovi, K.; Somogyvári, Z.; Strandburg, K.; Tobochnik, J.; Volf, P.; Zalányi, L.
  • Patent portfolio analysis as a useful tool for identifying R&D and business opportunities—an empirical application in the nutrition and health industry
    Fabry, B.; Ernst, H.; Langholz, J.; Köster, M.
  • Automated categorization in the international patent classification
    Fall, C.J.; Törcsvári, A.; Benzineb, K.; Karetka, G.
  • Patent citation recommendation for examiners
    Fu, T.; Lei, Z.; Lee, W.-C.
  • Weighted meta paths and networking embedding for patent technology trade recommendations among subjects
    He, X.; Dong, Y.; Zhen, Z.; Wu, Y.; Jiang, G.; Meng, X.; Ma, S.
  • Metaresearch recommendations using knowledge graph embeddings
    Henk, V.; Vahdati, S.; Nayyeri, M.; Ali, M.; Yazdi, H.S.; Lehmann, J.
  • Long short-term memory
    Hochreiter, S.; Schmidhuber, J.
  • Latent semantic models for collaborative filtering
    Hofmann, T.
  • SimRank: a measure of structural-context similarity
    Jeh, G.; Widom, J.
  • Patent collaborative filtering recommendation approach based on patent similarity
    Ji, X.; Gu, X.; Dai, F.; Chen, J.; Le, C.
  • Knowledge graph embedding via dynamic mapping matrix
    Ji, G.; He, S.; Xu, L.; Liu, K.; Zhao, J.
  • Patent citation analysis: a policy analysis tool
    Karki, M.M.S.
  • The effect of patent acquisition on subsequent patenting activity
    Kim, S.Y.; Lee, H.J.
  • Technology clusters exploration for patent portfolio through patent abstract analysis
    Kim, G.; Lee, J.; Jang, D.; Park, S.
  • Multi-co-training for document classification using various document representations: TF–IDF, LDA, and Doc2Vec
    Kim, D.; Seo, D.; Cho, S.; Kang, P.
  • E-Learning personalization based on hybrid recommendation strategy and learning style identification
    Klašnja-Milićević, A.; Vesin, B.; Ivanović, M.; Budimac, Z.
  • Interactive overlay maps for US patent (USPTO) data based on International Patent Classification (IPC)
    Leydesdorff, L.; Kushnir, D.; Rafols, I.
  • Disambiguation and co-authorship networks of the US patent inventor database (1975–2010)
    Li, G.-C.; Lai, R.; D'Amour, A.; Doolin, D.M.; Sun, Y.; Torvik, V.I.; Amy, Z.Y.; Fleming, L.
  • A capability‐based framework for open innovation: complementing absorptive capacity
    Lichtenthaler, U.; Lichtenthaler, E.
  • Learning entity and relation embeddings for knowledge graph completion
    Lin, Y.; Liu, Z.; Sun, M.; Liu, Y.; Zhu, X.
  • Meta-path-based ranking with pseudo relevance feedback on heterogeneous graph for citation recommendation
    Liu, X.; Yu, Y.; Guo, C.; Sun, Y.
  • A new function-based patent knowledge retrieval tool for conceptual design of innovative products
    Liu, L.; Li, Y.; Xiong, Y.; Cavallucci, D.
  • How retailers can keep up with consumers
    MacKenzie, I.; Meyer, C.; Noble, S.
  • Query-driven mining of citation networks for patent citation retrieval and recommendation
    Mahdabi, P.; Crestani, F.
  • Leveraging conceptual lexicon: query disambiguation using proximity information for patent retrieval
    Mahdabi, P.; Gerani, S.; Huang, J.X.; Crestani, F.
  • Word2Vec tutorial - the skip-gram model
    McCormick, C.
  • CV-PCR: a context-guided value-driven framework for patent citation recommendation
    Oh, S.; Lei, Z.; Lee, W.-C.; Mitra, P.; Yen, J.
  • Technology transfer from universities and public research institutes to firms in Brazil: what is transferred and how the transfer is carried out
    Póvoa, L.M.C.; Rapini, M.S.
  • Feed-forward networks with attention can solve some long-term memory problems
    Raffel, C.; Ellis, D.P.W.
  • Automatic keyword extraction from individual documents
    Rose, S.; Engel, D.; Cramer, N.; Cowley, W.
  • Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
    Rudin, C.
  • Toward an interactive patent retrieval framework based on distributed representations
    Shalaby, W.; Zadrozny, W.
  • Recurrent knowledge graph embedding for effective recommendation
    Sun, Z.; Yang, J.; Zhang, J.; Bozzon, A.; Huang, L.-K.; Xu, C.
  • Intelligent patent recommendation system for innovative design collaboration
    Trappey, A.J.C.; Trappey, C.V.; Wu, C.-Y.; Fan, C.Y.; Lin, Y.-L.
  • Exploiting semantic knowledge base for patent retrieval
    Wang, F.; Lin, L.
  • A semantic query expansion-based patent retrieval approach
    Wang, F.; Lin, L.; Yang, S.; Zhu, X.
  • Knowledge graph embedding by translating on hyperplanes
    Wang, Z.; Zhang, J.; Feng, J.; Chen, Z.
  • Ripplenet: propagating user preferences on the knowledge graph for recommender systems
    Wang, H.; Zhang, F.; Wang, J.; Zhao, M.; Li, W.; Xie, X.; Guo, M.
  • DKN: deep knowledge-aware network for news recommendation
    Wang, H.; Zhang, F.; Xie, X.; Guo, M.
  • Multi-task feature learning for knowledge graph enhanced recommendation
    Wang, H.; Zhang, F.; Zhao, M.; Li, W.; Xie, X.; Guo, M.
  • Recommendation mechanism for patent trading empowered by heterogeneous information networks
    Wang, Q.; Du, W.; Ma, J.; Liao, X.
  • Incorporating graph attention mechanism into knowledge graph reasoning based on deep reinforcement learning
    Wang, H.; Li, S.; Pan, R.; Mao, M.
  • Patent partner recommendation in enterprise social networks
    Wu, S.; Sun, J.; Tang, J.
  • Reinforcement knowledge graph reasoning for explainable recommendation
    Xian, Y.; Fu, Z.; Muthukrishnan, S.; De Melo, G.; Zhang, Y.
  • Personalized entity recommendation: a heterogeneous information network approach
    Yu, X.; Ren, X.; Sun, Y.; Gu, Q.; Sturt, B.; Khandelwal, U.; Norick, B.; Han, J.
  • Harnessing the value of open innovation: change in the moderating role of absorptive capability
    Yun, J.J.; Zhao, X.; Hahm, S.D.
  • Explainable recommendation: a survey and new perspectives
    Zhang, Y.; Chen, X.