ABSTRACT
The query and selection of scientific literatures are knowledge driven. Researchers regard public literature resources as target knowledge sources and use their own domain knowledge to explore in them. However, existing knowledge-driven methods of literature recommendation mainly focus on morphological matching and cannot effectively resolve polysemous phenomenon brought by "knowledge overload". Based on this observation, this paper presents a knowledge-driven approach for personalized literature recommendation. Domain ontology, synonyms and knowledge labels are integrated into a multidimensional domain knowledge map for modeling user knowledge requirements and literature contents based on deep semantic discrimination. The personalized recommendation is achieved by calculating knowledge distances between users and literatures. Experimental results on a real data set of PubMed show that the recommended relevance of the current method is 67%, better than other methods.
- Jun Zhao, Qian-Li Jin, and Bo Xu. Semantic computation for text retrieval. Jisuanji Xuebao(Chinese Journal of Computers), 28(12):2068--2078, 2005.Google Scholar
- Justin Scott Giboney, Susan A Brown, Paul Benjamin Lowry, and Jay F Nunamaker. User acceptance of knowledge-based system recommendations: Explanations, arguments, and fit. Decision Support Systems, 72:1--10, 2015. Google ScholarDigital Library
- Wei Ding, Peng Liang, Antony Tang, and Hans Van Vliet. Knowledge-based approaches in software documentation: A systematic literature review. Information and Software Technology, 56(6):545--567, 2014. Google ScholarDigital Library
- Yuh-Jen Chen, Hui-Chuan Chu, Yuh-Min Chen, and Chung-Yueh Chao. Adapting domain ontology for personalized knowledge search and recommendation. Information & Management, 50(6):285--303, 2013. Google ScholarDigital Library
- Carmen Martinez-Cruz, Carlos Porcel, Juan Bernabé-Moreno, and Enrique Herrera-Viedma. A model to represent users trust in recommender systems using ontologies and fuzzy linguistic modeling. Information Sciences, 311:102--118, 2015. Google ScholarDigital Library
- Joeran Beel, Bela Gipp, Stefan Langer, and Corinna Breitinger. Research-paper recommender systems: a literature survey. International Journal on Digital Libraries, 17(4):305--338, 2016. Google ScholarDigital Library
- Weidong Zhao, Ran Wu, and Haitao Liu. Paper recommendation based on the knowledge gap between a researcher's background knowledge and research target. Information Processing & Management, 52(5):976--988, 2016. Google ScholarDigital Library
- Li-Ping Jing, Hou-Kuan Huang, and Hong-Bo Shi. Improved feature selection approach tfidf in text mining. In Proceedings. International Conference on Machine Learning and Cybernetics, volume 2, pages 944--946 vol.2, 2002.Google ScholarCross Ref
- Yuanyuan Zhang, James Z Wang, and Pradip K Srimani. Semantic graph based pseudo relevance feedback for biomedical information retrieval. In Proceedings of the 7th International Conference on Computational Systems-Biology and Bioinformatics, pages 48--52. ACM, 2016. Google ScholarDigital Library
- Gérald Kembellec, Imad Saleh, and Catherine Sauvaget. A model of cross language retrieval for it domain papers through a map of acm computing classification system. In Multimedia Computing and Systems, 2009. ICMCS'09. International Conference on, pages 162--168. ieee, 2009.Google ScholarCross Ref
- Adam Kilgarriff and Christiane Fellbaum. Wordnet: An electronic lexical database, 2000.Google Scholar
- Maribel Acosta, Amrapali Zaveri, Elena Simperl, Dimitris Kontokostas, Fabian Flöck, and Jens Lehmann. Detecting linked data quality issues via crowdsourcing: A dbpedia study. Semantic Web, (Preprint):1--33, 2016.Google Scholar
- Jerrold R Turner. Pubmed, pubmed central, and impact factor. Cellular and Molecular Gastroenterology and Hepatology, 2(5):537, 2016.Google ScholarCross Ref
- Lidong Bing, Shan Jiang, Wai Lam, Yan Zhang, and Shoaib Jameel. Adaptive concept resolution for document representation and its applications in text mining. Knowledge-Based Systems, 74:1--13, 2015. Google ScholarDigital Library
- Yu-Xiao Zhu and Lin-Yuan Lu. Evaluation metrics for recommender systems. Journal of University of Electronic Science and Technology of China, 41(2):163--175, 2012.Google Scholar
Index Terms
- A knowledge-driven approach for personalized literature recommendation based on deep semantic discrimination
Recommendations
Scholarly recommendation systems: a literature survey
AbstractA scholarly recommendation system is an important tool for identifying prior and related resources such as literature, datasets, grants, and collaborators. A well-designed scholarly recommender significantly saves the time of researchers and can ...
Attention-driven Factor Model for Explainable Personalized Recommendation
SIGIR '18: The 41st International ACM SIGIR Conference on Research & Development in Information RetrievalLatent Factor Models (LFMs) based on Collaborative Filtering (CF) have been widely applied in many recommendation systems, due to their good performance of prediction accuracy. In addition to users' ratings, auxiliary information such as item features ...
Personalized recommendation based on review topics
The traditional collaborative filtering algorithm is a successful recommendation technology. The core idea of this algorithm is to calculate user or item similarity based on user ratings and then to predict ratings and recommend items based on similar ...
Comments