Hybrid recommendation system combining collaborative filtering and content-based recommendation with keyword extraction

Research Article
Open access

Hybrid recommendation system combining collaborative filtering and content-based recommendation with keyword extraction

Shuting Zhang 1* , Kechen Liu 2 , Zekai Yu 3 , Bowen Feng 4 , Zijie Ou 5
  • 1 Glasgow College, University of Electronic Science and Technology of China, Sichuan, 611700, China    
  • 2 College of Computer and Information Technology, China Three Gorges University, Yichang, 443002, China.    
  • 3 Ningbo Binhai International Cooperative School, Ningbo, 315800, China    
  • 4 Southland International High School Qingdao Campus, Qingdao, 266555, China    
  • 5 Guangdong Experimental High School, Guangzhou, 510000, China    
  • *corresponding author 2019190504007@std.uestc.edu.cn
Published on 22 March 2023 | https://doi.org/10.54254/2755-2721/2/20220579
ACE Vol.2
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-915371-19-5
ISBN (Online): 978-1-915371-20-1

Abstract

With the development of recommendation systems, large amount of information collected from e-commerce could help customers to find the potential interesting products. Collaborative filtering and content-based recommendation systems are two common recommendation systems. While collaborative filtering has the problem of cold-start, content-based recommendation system could not explore the potential interests of users. Hybrid system combining these two techniques could achieve better results. This paper applies hybrid recommendation methods to the Amazon food reviews and evaluate the results in the aspects of precision, recall, diversity and novelty. It is found that the weighted hybrid recommendation system combing 0.95 weight of collaborative filtering and 0.05 content-based recommendation system achieves a good precision and diversity.

Keywords:

recommendation systems, collaborative filtering, hybrid system.

Zhang,S.;Liu,K.;Yu,Z.;Feng,B.;Ou,Z. (2023). Hybrid recommendation system combining collaborative filtering and content-based recommendation with keyword extraction. Applied and Computational Engineering,2,149-161.
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References

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Cite this article

Zhang,S.;Liu,K.;Yu,Z.;Feng,B.;Ou,Z. (2023). Hybrid recommendation system combining collaborative filtering and content-based recommendation with keyword extraction. Applied and Computational Engineering,2,149-161.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume title: Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)

ISBN：978-1-915371-19-5(Print) / 978-1-915371-20-1(Online)
Editor：Alan Wang
Conference website: https://www.confcds.org/
Conference date: 16 July 2022
Series: Applied and Computational Engineering
Volume number: Vol.2
ISSN：2755-2721(Print) / 2755-273X(Online)

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References

[1]. J. Lu, D. Wu, M. Mao, W. Wang, and G. Zhang, “Recommender System Application Developments: A Survey,” p. 38.

[2]. Rathi, P. Keni, and J. Sisodia, “Project Topic Recommendation by Analyzing User’s Interest Using Intelligent Conversational System,” in Innovative Data Communication Technologies and Application, Singapore, 2022, pp. 277–287. doi: 10.1007/978-981-16-7167-821.

[3]. “Collaborative Filtering: A Simple Introduction | Built In.” https://builtin.com/data-science/collaborative-filtering-recommendersystem (accessed Mar. 19, 2022).

[4]. J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl, ‘An Algorithmic Framework for Performing Collaborative Filtering’, ACM SIGIR Forum, vol. 51, no. 2, pp. 227–234, Aug. 2017, doi: 10.1145/3130348.3130372.

[5]. B. Sarwar, G. Karypis, J. Konstan, and J. Reidl, ‘Item-based collaborative filtering recommendation algorithms’, in Proceedings of the tenth international conference on World Wide Web - WWW ’01, Hong Kong, Hong Kong, 2001, pp. 285–295. doi: 10.1145/371920.372071.

[6]. B. Smith and G. Linden, ‘Two Decades of Recommender Systems at Amazon.com’, IEEE Internet Computing, vol. 21, no. 3, pp. 12–18, 2017.

[7]. M. J. Pazzani and D. Billsus, ‘Content-Based Recommendation Systems’, in The Adaptive Web: Methods and Strategies of Web Personalization, P. Brusilovsky, A. Kobsa, and W. Nejdl, Eds. Berlin, Heidelberg: Springer, 2007, pp. 325–341. doi: 10.1007/978-3-54072079-910.

[8]. B. Barragáns-Martínez, E. Costa-Montenegro, J. C. Burguillo, M. Rey-López, F. A. Mikic-Fonte, and A. Peleteiro, ‘A hybrid contentbased and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition’, Inf. Sci., vol. 180, no. 22, pp. 4290–4311, 2010, doi: 10.1016/j.ins.2010.07.024.

[9]. M. Balabanovic and Y. Shoham, ‘Fab: content-based, collaborative´ recommendation’, Commun. ACM, vol. 40, no. 3, pp. 66–72, Mar. 1997, doi: 10.1145/245108.245124.

[10]. S. Rose, D. Engel, N. Cramer and W. Cowley, "Automatic keyword extraction from individual documents," applications and theory, p. 1, 20 1 2010.

[11]. J. Davidson, D. Liebald, J. Liu, P. Nandy and T. V. Vleet, "The YouTube Video Recommendation System," Proceedings of the fourth ACM conference on Recommender systems, pp. 293-294, 9 2010.

[12]. Liling, LIU. “Summary of Recommendation System Development.” Journal of Physics: Conference Series, vol. 1187, Apr. 2019, p. 052044, https://doi.org/10.1088/1742-6596/1187/5/052044.

[13]. Liling, LIU. “Summary of Recommendation System Development.” Journal of Physics: Conference Series, vol. 1187, Apr. 2019, p. 052044, https://doi.org/10.1088/1742-6596/1187/5/052044. [12] J. Bobadilla, F. Ortega, A. Hernando, A. Gutiérrez, Recommender systems survey, Knowledge-Based Systems, 46 (2013) 109-132.

[14]. Team, “Recommendation Systems: Benefits And Development Process Issues,” Azati: Uniting experts to fulfil important projects, Apr. 08, 2020. https://azati.ai/recommendation-systems-benefits-and-issues/ (accessed Mar. 19, 2022).

[15]. “1in2012ß2016| Inflation Calculator.ȷ https : //www.in2013dollars.com/us/inflation/2012?endY ear = 2016amount = 1(accessedMar.19,2022).

[16]. S. Gong, ‘A collaborative filtering recommendation algorithm based on user clustering and item clustering.’, J. Softw., vol. 5, no. 7, pp. 745–752, 2010.

[17]. X. He, Z. He, J. Song, Z. Liu, Y.-G. Jiang, and T.-S. Chua, ‘NAIS: Neural Attentive Item Similarity Model for Recommendation’, IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 12, pp. 2354–2366, 2018.

[18]. E. Christakopoulou and G. Karypis, ‘Local Item-Item Models For TopN Recommendation’, in Proceedings of the 10th ACM Conference on Recommender Systems, Boston Massachusetts USA, Sep. 2016, pp. 67–74.

[19]. C. B. Hensley, ‘Selective Dissemination of Information (SDI): State of the Art in May, 1963’, Proceedings of the May 21-23, 1963, Spring Joint Computer Conference, Detroit, Michigan, 1963, . 257–262.

[20]. M. Pazzani, J. Muramatsu, D. Billsus, ‘Syskill Webert: Identifying interesting web sites’, Proceedings of the Thirteenth National Conference on Artificial Intelligence, AAAI’96, 1996, . 54–61.

[21]. P. Lops, D. Jannach, C. Musto, T. Bogers, and M. Koolen, ‘Trends in content-based recommendation’, User Model. User-Adapt. Interact., vol. 29, no. 2, pp. 239–249, Apr. 2019, doi: 10.1007/s11257-01909231-w.

[22]. J. McAuley, C. Targett, Q. Shi, A. van den Hengel, ‘Image-Based Recommendations on Styles and Substitutes’, Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, Santiago, Chile, 2015, . 43–52.

[23]. S. Beliga, Keyword extraction: a review of methods and approaches, University of Rijeka, Department of Informatics, Rijeka, 2014.

[24]. L. Marujo, L. Wang, I. Trancoso, C. Dyer, A. W. Black, A. Gershman, D. M. d. Matos, J. P. Neto and J. Carbonell, "Automatic Keyword Extraction on Twitter," in Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Short Papers), Beijing, 2015.

[25]. Z. Li, D. Zhou, Y. Juan and J. Han, "Keyword Extraction for Social Snippets," in Proceedings of the 19th international conference on World wide web, 2010.

[26]. S. K. Bharti, K. S. Babu S. K. Jena, Automatic Keyword Extraction for Text Summarization: A Survey, arXiv preprint arXiv:1704.03242, 2017.

[27]. S. Rose, D. Engel, N. Cramer and W. Cowley, "Automatic keyword extraction from individual documents," Text Mining: Applications and Theory, p. 3, 2010.

[28]. Hulth, J. Karlgren, A. Jonsson, H. Boström and L. Asker, "Automatic Keyword Extraction Using Domain Knowledge," in International Conference on Intelligent Text Processing and Computational Linguistics, Springer, Berlin, Heidelberg, 2001.

[29]. Boy, "How to use RegEx in Python," Educative, Inc., [Online]. Available: https://www.educative.io/edpresso/how-to-use-regexin-python. [Accessed 19 3 2022].

[30]. csurfer, "rake-nltk 1.0.6," python Software Foundation, [Online]. Available: https://pypi.org/project/rake-nltk/. [Accessed 19 3 2022].

[31]. N. Saxena, "Extracting Keyphrases from Text: RAKE and Gensim in Python," Medium, [Online]. Available: https://towardsdatascience.com/extracting-keyphrases-from-textrake-and-gensim-in-python-eefd0fad582f. [Accessed 19 3 2022].

[32]. R. van Meteren, ‘Using Content-Based Filtering for Recommendation’, 2000.

[33]. G. Shani and A. Gunawardana, ‘Evaluating Recommendation Systems’, in Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Eds. Boston, MA: Springer US, 2011, pp. 257–297. doi: 10.1007/978-0-387-85820-38.

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