HIM-PRS: A Patent Recommendation System Based on Hierarchical Index-Based MapReduce Framework

Rui, Xuhua; Dai, Min

doi:10.1007/978-981-10-3023-9_130

Cited by 3 publications

(3 citation statements)

References 11 publications

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“…To facilitate the reuse of cross-domain design knowledge, Liu et al [31] adopted functional basis mapping from the design problem space to the patent knowledge space for the purpose of cross-domain patent retrieval. To alleviate the searching slowness problem, Rui and Min proposed a hierarchical index-based MapReduce framework for high-speed patent searching [8].…”

Section: Related Workmentioning

confidence: 99%

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Sequential patent trading recommendation using knowledge-aware attentional bidirectional long short-term memory network (KBiLSTM)

Jiang

et al. 2021

Journal of Information Science

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With the rapid development of the patent marketplace, patent trading recommendation is required to mitigate the technology searching cost of patent buyers. Current research focuses on the recommendation based on existing patents of a company; a few studies take into account the sequential pattern of patent acquisition activities and the possible diversity of a company’s business interests. Moreover, the profiling of patents based on solely patent documents fails to capture the high-order information of patents. To bridge the gap, we propose a knowledge-aware attentional bidirectional long short-term memory network (KBiLSTM) method for patent trading recommendation. KBiLSTM uses knowledge graph embeddings to profile patents with rich patent information. It introduces bidirectional long short-term memory network (BiLSTM) to capture the sequential pattern in a company’s historical records. In addition, to address a company’s diverse technology interests, we design an attention mechanism to aggregate the company’s historical patents given a candidate patent. Experimental results on the United States Patent and Trademark Office (USPTO) data set show that KBiLSTM outperforms state-of-the-art baselines for patent trading recommendation in terms of F1 and normalised discounted cumulative gain (nDCG). The attention visualisation of randomly selected company intuitively demonstrates the recommendation effectiveness.

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Section: Related Workmentioning

confidence: 99%

“…Automatic patent recommendation has been investigated in many contexts such as personalised patent retrieval, patent citation recommendation and patent trading recommendation [7][8][9]. Previous studies mainly used topic models [10,11] and collaborative filtering (CF) methods [12,13] for patent recommendation.…”

Section: Introductionmentioning

confidence: 99%

Sequential patent trading recommendation using knowledge-aware attentional bidirectional long short-term memory network (KBiLSTM)

Jiang

et al. 2021

Journal of Information Science

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“…Moreover, a large proportion of high-quality patents are held by research institutions and have not been transferred. For solving these problems, state-of-the-art patent recommendation methods leverage rich information in patent documents to identify matched patents in different contexts, such as filtering patents for personalized retrieval, suggesting patent citations and recommending patents for potential buyers (Ji et al, 2011;Oh et al, 2013;Rui and Min, 2016;Wang et al, 2019b;Wu et al, 2013). Most of these existing methods focus on improving the recommendation accuracy while ignoring the interpretability of recommendation results.…”

Section: Introductionmentioning

confidence: 99%

Leveraging online behaviors for interpretable knowledge-aware patent recommendation

Yan

Zhang

et al. 2021

INTR

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PurposePatent 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.

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Interpretable patent recommendation with knowledge graph and deep learning

Chen

Deng

2023

Sci Rep

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Patent transfer is a common practice for companies to obtain competitive advantages. However, they encounter the difficulty of selecting suitable patents because the number of patents is increasingly large. Many patent recommendation methods have been proposed to ease the difficulty, but they ignore patent quality and cannot explain why certain patents are recommended. Patent quality and recommendation explanations affect companies’ decision-making in the patent transfer context. Failing to consider them in the recommendation process leads to less effective recommendation results. To fill these gaps, this paper proposes an interpretable patent recommendation method based on knowledge graph and deep learning. The proposed method organizes heterogeneous patent information as a knowledge graph. Then it extracts connectivity and quality features from the knowledge graph for pairs of patents and companies. The former features indicate the relevance of the pairs while the latter features reflect the quality of the patents. Based on the features, we design an interpretable recommendation model by combining a deep neural network with a relevance propagation technique. We conduct experiments with real-world data to evaluate the proposed method. Recommendation lists with varying lengths show that the average precision, recall, and mean average precision of the proposed method are 0.596, 0.636, and 0.584, which improve corresponding performance of best baselines by 7.28%, 18.35%, and 8.60%, respectively. Besides, our method interprets recommendation results by identifying important features leading to the results.

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HIM-PRS: A Patent Recommendation System Based on Hierarchical Index-Based MapReduce Framework

Cited by 3 publications

References 11 publications

Sequential patent trading recommendation using knowledge-aware attentional bidirectional long short-term memory network (KBiLSTM)

Sequential patent trading recommendation using knowledge-aware attentional bidirectional long short-term memory network (KBiLSTM)

Leveraging online behaviors for interpretable knowledge-aware patent recommendation

Interpretable patent recommendation with knowledge graph and deep learning

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