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Minaei2 B, Parvin H, Mirzarezaee M, Keshavarz A. A New WordNet Enriched Content-Collaborative Recommender System. JSDP 2022; 18 (4) : 7
URL: http://jsdp.rcisp.ac.ir/article-1-1185-en.html
Department of Computer Engineering, Islamic Azad University of Noorabad Mamasani, Fars, Iran
Abstract:   (1828 Views)
The recommender systems are models that are to predict the potential interests of users among a number of items. These systems are widespread and they have many applications in real-world. These systems are generally based on one of two structural types: collaborative filtering and content filtering. There are some systems which are based on both of them. These systems are named hybrid recommender systems. Recently, many researchers have proved that using content models along with these systems can improve the efficacy of hybrid recommender systems. In this paper, we propose to use a new hybrid recommender system where we use a WordNet to improve its performance. This WordNet is also automatically generated and improved during its generation. Our ontology creates a knowledge base of concepts and their relations. This WordNet is used in the content collaborator section in our hybrid recommender system. We improve our ontological structure via a content filtering technique. Our method also benefits from a clustering task in its collaborative section. Indeed, we use a passive clustering task to improve the time complexity of our hybrid recommender system. Although this is a hybrid method, it consists of two separate sections. These two sections work together during learning.
Our hybrid recommender system incorporates a basic memory-based approach and a basic model-based approach in such a way that it is as accurate as a memory-based approach and as scalable as a model-based approach. Our hybrid recommender system is assessed by a well-known data set. The empirical results indicate that our hybrid recommender system is superior to the state of the art methods. Also, our hybrid recommender system is more accurate and scalable compared to the recommender systems, which are simply memory-based (KNN) or basic model-based. The empirical results also confirm that our hybrid recommender system is superior to the state of the art methods in terms of the consumed time.
While this method is more accurate than model-based methods, it is also faster than memory-based methods. However, this method is not much weaker in terms of accuracy than memory-based methods, and not much weaker in terms of speed than model-based methods.
Article number: 7
Full-Text [PDF 3065 kb]   (625 Downloads)    
Type of Study: Research | Subject: Paper
Received: 2020/10/25 | Accepted: 2021/03/8 | Published: 2022/03/21 | ePublished: 2022/03/21

References
1. [1] E. Rich, "User modeling via stereotypes," Cognitive science, vol. 3, pp. 329-354, 1979. [DOI:10.1207/s15516709cog0304_3]
2. [2] G. Salton, "Automatic text processing: The transformation, analysis, and retrieval of," Reading: Addison-Wesley, 1989.
3. [3] B. Murthi and S. Sarkar, "The role of the management sciences in research on personalization," Management Science, vol. 49, pp. 1344-1362, 2003. [DOI:10.1287/mnsc.49.10.1344.17313]
4. [4] M. J. D. Powell, Approximation theory and methods: Cambridge university press, 1981. [DOI:10.1017/CBO9781139171502]
5. [5] G. Lilien, P. Kotler, and K. Moorthy, "Marketing models prentice-hall," Englewood Cliffs, NJ, 1992.
6. [6] J. S. Armstrong, Principles of forecasting: A handbook for researchers and practitioners vol. 30: Springer Science & Business Media, 2001. [DOI:10.1007/978-0-306-47630-3]
7. [7] F. McSherry and I. Mironov, "Differentially private recommender systems: Building privacy into the netflix prize contenders," in Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 2009, pp. 627-636. [DOI:10.1145/1557019.1557090]
8. [8] S. S. Anand and B. Mobasher, "Intelligent techniques for web personalization," in Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization, 2003, pp. 1-36. [DOI:10.1007/11577935_1]
9. [9] P. Lops, M. De Gemmis, and G. Semeraro, "Content-based recommender systems: State of the art and trends," in Recommender systems handbook, ed: Springer, 2011, pp. 73-105. [DOI:10.1007/978-0-387-85820-3_3]
10. [10] J. S. Breese, D. Heckerman, and C. Kadie, "Empirical analysis of predictive algorithms for collaborative filtering," in Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, 1998, pp. 43-52.
11. [11] L. Iaquinta, M. De Gemmis, P. Lops, G. Semeraro, M. Filannino, and P. Molino, "Introducing serendipity in a content-based recommender system," in Hybrid Intelligent Systems, 2008. HIS'08. Eighth International Conference on, 2008, pp. 168-173. [DOI:10.1109/HIS.2008.25]
12. [12] B. M. Sarwar, G. Karypis, J. Konstan, and J. Riedl, "Recommender systems for large-scale e-commerce: Scalable neighborhood formation using clustering," in Proceedings of the fifth international conference on computer and information technology, 2002, pp. 291-324.
13. [13] K. Goldberg, T. Roeder, D. Gupta, and C. Perkins, "Eigentaste: A constant time collaborative filtering algorithm," information retrieval, vol. 4, pp. 133-151, 2001. [DOI:10.1023/A:1011419012209]
14. [14] K. Goldberg, T. Roeder, D. Gupta, and C. Perkins, "Eigentaste: A constant time collaborative filtering algorithm," information retrieval, vol. 4, pp. 133-151, 2001. [DOI:10.1023/A:1011419012209]
15. [15] M. Nilashi, O. bin Ibrahim, and N. Ithnin, "Hybrid recommendation approaches for multi-criteria collaborative filtering," Expert Systems with Applications, vol. 41, pp. 3879-3900, 2014. [DOI:10.1016/j.eswa.2013.12.023]
16. [16] G. Linden, B. Smith, and J. York, "Amazon. Com recommendations: Item-to-item collaborative filtering," IEEE Internet computing, vol. 7, pp. 76-80, 2003. [DOI:10.1109/MIC.2003.1167344]
17. [17] M. C. Pham, Y. Cao, R. Klamma, and M. Jarke, "A clustering approach for collaborative filtering recommendation using social network analysis," J. UCS, vol. 17, pp. 583-604, 2011.
18. [18] S. Gong, "A collaborative filtering recommendation algorithm based on user clustering and item clustering," JSW, vol. 5, pp. 745-752, 2010. [DOI:10.4304/jsw.5.7.745-752]
19. [19] Y. He, S. Yang, and C. Jiao, "A hybrid collaborative filtering recommendation algorithm for solving the data sparsity," in Computer Science and Society (ISCCS), 2011 International Symposium on, 2011, pp. 118-121. [DOI:10.1109/ISCCS.2011.40]
20. [20] S. K. Shinde and U. Kulkarni, "Hybrid personalized recommender system using centering-bunching based clustering algorithm," Expert Systems with Applications, vol. 39, pp. 1381-1387, 2012. [DOI:10.1016/j.eswa.2011.08.020]
21. [21] K. Truong, F. Ishikawa, and S. Honiden, "Improving accuracy of recommender system by item clustering," IEICE TRANSACTIONS on Information and Systems, vol. 90, pp. 1363-1373, 2007. [DOI:10.1093/ietisy/e90-d.9.1363]
22. [22] P. Wang, "A personalized collaborative recommendation approach based on clustering of customers," Physics Procedia, vol. 24, pp. 812-816, 2012. [DOI:10.1016/j.phpro.2012.02.121]
23. [23] Z. K. Zhang, T. Zhou, and Y.-C. Zhang, "Tag-aware recommender systems: A state-of-the-art survey," Journal of computer science and technology, vol. 26, p. 767, 2011. [DOI:10.1007/s11390-011-0176-1]
24. [24] M. C. Pham, Y. Cao, R. Klamma, and M. Jarke, "A clustering approach for collaborative filtering recommendation using social network analysis," J. UCS, vol. 17, pp. 583-604, 2011.
25. [25] J. Breese, D. Heckerman, and C. Kadie,. "An experimental comparison of collaborative filtering methods", Technical Report MSR-TR 98-12, Microsoft Research, Redmond, WA, 1998.
26. [26] Y. He, S. Yang, and C. Jiao, "A hybrid collaborative filtering recommendation algorithm for solving the data sparsity," in Computer Science and Society (ISCCS), 2011 International Symposium on, 2011, pp. 118-121. [DOI:10.1109/ISCCS.2011.40]
27. [27] S. K. Shinde and U. Kulkarni, "Hybrid personalized recommender system using centering-bunching based clustering algorithm," Expert Systems with Applications, vol. 39, pp. 1381-1387, 2012. [DOI:10.1016/j.eswa.2011.08.020]
28. [28] R. Burke, "Hybrid recommender systems: Survey and experiments," User modeling and user-adapted interaction, vol. 12, pp. 331-370, 2002. [DOI:10.1023/A:1021240730564]
29. [29] J. C, Flinn, & G. L. Denning, "Interdisciplinary challenges and opportunities in international agricultural research", IRRI research paper series-International Rice Research Institute, 1982.
30. [30] Chen, S., Peng, Y, "Matrix factorization for recommendation with explicit and implicit feedback. Knowl", Based Syst. 158: 109-117, 2018. [DOI:10.1016/j.knosys.2018.05.040]
31. [31] G. Adomavicius, & Y. Kwon, "New recommendation techniques for multicriteria rating systems", IEEE Intelligent Systems, 22(3), 2007. [DOI:10.1109/MIS.2007.58]
32. [32] G. Adomavicius, & A. Tuzhilin, "Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions", Knowledge and Data Engineering, IEEE Transactions on, 17(6), 734-749, 2005. [DOI:10.1109/TKDE.2005.99]
33. [33] D. H. Park, H. K. Kim, I. Y. Choi, & J. K. Kim, "A literature review and classification of recommender systems research", Expert Systems with Applications, 39(11), 10059-10072, 2012. [DOI:10.1016/j.eswa.2012.02.038]
34. [34] M. Montaner, B. López, and J. L. De La Rosa, "A taxonomy of recommender agents on the internet", Artificial intelligence review, vol. 19, pp. 285-330, 2003. [DOI:10.1023/A:1022850703159]
35. [35] J.-S. Lee and S. Olafsson, "Two-way cooperative prediction for collaborative filtering recommendations," Expert Systems with Applications, vol. 36, pp. 5353-5361, 2009. [DOI:10.1016/j.eswa.2008.06.106]
36. [36] J. B. Schafer, D. Frankowski, J. Herlocker, and S. Sen, "Collaborative filtering recommender systems," in The adaptive web, ed: Springer, 2007, pp. 291-324. [DOI:10.1007/978-3-540-72079-9_9]
37. [37] R. Van Meteren and M. Van Someren, "Using content-based filtering for recommendation," in Proceedings of the Machine Learning in the New Information Age: MLnet/ECML2000 Workshop, 2000, pp. 47-56.
38. [38] K. Verbert, N. Manouselis, X. Ochoa, M.Wolpers, H. Drachsler, I. Bosnic & Duval, E, "Context-aware recommender systems for learning: a survey and future challenges", IEEE Transactions on Learning Technologies, vol. 5(4), pp.318-335, 2012. [DOI:10.1109/TLT.2012.11]
39. [39] Y. Koren, R. Bell & C. Volinsky, "Matrix factorization techniques for recommender systems", Computer, vol. 42(8), pp. 30-37, 2009. [DOI:10.1109/MC.2009.263]
40. [40] Y. Koren, "Factorization meets the neighborhood: a multifaceted collaborative filtering model", KDD 2008, 426-434, 2008. [DOI:10.1145/1401890.1401944] [PMID]
41. [41] L. C. Cheng, , & H. A. Wang, "A fuzzy recommender system based on the integration of subjective preferences and objective information", Applied Soft Computing, vol. 18, .290-301, 2014. [DOI:10.1016/j.asoc.2013.09.004]
42. [42] S. Chen, Peng, Y. "Matrix factorization for recommendation with explicit and implicit feedback. Knowl", Based Syst. Vol. 158, pp. 109-117, 2018. [DOI:10.1016/j.knosys.2018.05.040]
43. [43] M. Y. H. Al-Shamri, "User profiling approaches for demographic recommender systems. Knowl", Based Syst. Vol. 100, pp. 175-187, 2016. [DOI:10.1016/j.knosys.2016.03.006]
44. [44] G. Jawaheer, M. Szomszor, and P. Kostkova, "Comparison of implicit and explicit feedback from an online music recommendation service," in proceedings of the 1st international workshop on information heterogeneity and fusion in recommender systems, 2010, pp. 47-51. [DOI:10.1145/1869446.1869453]
45. [45] X. Su and T. M. Khoshgoftaar, "A survey of collaborative filtering techniques," Advances in artificial intelligence, vol. 2009, pp. 4, 2009. [DOI:10.1155/2009/421425]
46. [46] J. Bobadilla, A. Hernando, F. Ortega, and J. Bernal, "A framework for collaborative filtering recommender systems," Expert Systems with Applications, vol. 38, pp. 14609-14623, 2011. [DOI:10.1016/j.eswa.2011.05.021]
47. [47] A. M. Acilar and A. Arslan, "A collaborative filtering method based on artificial immune network," Expert Systems with Applications, vol. 36, pp. 8324-8332, 2009. [DOI:10.1016/j.eswa.2008.10.029]
48. [48] J. Borràs, A. Moreno, and A. Valls, "Intelligent tourism recommender systems: A survey," Expert Systems with Applications, vol. 41, pp. 7370-7389, 2014. [DOI:10.1016/j.eswa.2014.06.007]
49. [49] S. Gong and H. Ye, "An item based collaborative filtering using bp neural networks prediction," in Industrial and Information Systems, 2009. IIS'09. International Conference on, 2009, pp. 146-148. [DOI:10.1109/IIS.2009.69] [PMID]
50. [50] A. Abdelwahab, H. Sekiya, I. Matsuba, Y. Horiuchi, S. Kuroiwa, and M. Nishida, "An efficient collaborative filtering algorithm using svd-free latent semantic indexing and particle swarm optimization," in Natural Language Processing and Knowledge Engineering, 2009. NLP-KE 2009. International Conference on, 2009, pp. 1-4. [DOI:10.1109/NLPKE.2009.5313754]
51. [51] X. Zhou, J. He, G. Huang, Y. Zhang, "SVD-based incremental approaches for recommender systems", J. Comput. Syst. Sci. 81(4): 717-733, 2015. [DOI:10.1016/j.jcss.2014.11.016]
52. [52] R. Baeza-Yates and B. Ribeiro-Neto, Modern information retrieval vol. 463: ACM press New York, 1999.
53. [53] C. Basu, H. Hirsh, and W. Cohen, "Recommendation as classification: Using social and content-based information in recommendation," in Aaai/iaai, 1998, pp. 714-720.
54. [54] K. Lang, "Newsweeder: Learning to filter netnews," in Proceedings of the 12th international conference on machine learning, 1995, pp. 331-339. [DOI:10.1016/B978-1-55860-377-6.50048-7] [PMID]
55. [55] K. Bagherifard, M. Rahmani, M. Nilashi, V. Rafe. "Performance improvement for recommender systems using ontology", Telematics Informatics, vol. 34(8), pp. 1772-1792, 2017. [DOI:10.1016/j.tele.2017.08.008]
56. [56] K. Bagherifard, M. Rahmani, M. Nilashi, V. Rafe, M. Nilashi, "A Recommendation Method Based on Semantic Similarity and Complementarity Using Weighted Taxonomy: A Case on Construction Materials Dataset", J. Inf. Knowl. Manag,vol. 17(1), pp. 1-26, 2018. [DOI:10.1142/S0219649218500107]
57. [57] M. Nilashi, O. Ibrahim, K. Bagherifard, "A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques", Expert Syst. Appl. 92: 507-520, 2018. [DOI:10.1016/j.eswa.2017.09.058]
58. [58] J. Alspector, A. Kolcz, and N. Karunanithi, "Comparing feature-based and clique-based user models for movie selection," in Proceedings of the third ACM conference on Digital libraries, 1998, pp. 11-18. [DOI:10.1145/276675.276677]
59. [59] N. Guarino, D. Oberle, and S. Staab, "What is an ontology?," in Handbook on ontologies, ed: Springer, 2009, pp. 1-17. [DOI:10.1007/978-3-540-92673-3_0] [PMID]
60. [60] T. R. Gruber, "Toward principles for the design of ontologies used for knowledge sharing?," International journal of human-computer studies, vol. 43, pp. 907-928, 1995. [DOI:10.1006/ijhc.1995.1081]
61. [61] W. Borst, "Construction of engineering," ed: Ontologies, University of Tweenty, Enschede, NL-Center for Telematica and Information Technology, 1997.
62. [62] A. Flahive, B. O. Apduhan, J. W. Rahayu, and D. Taniar, "Large scale ontology tailoring and simulation in the semantic grid environment," International Journal of Metadata, Semantics and Ontologies, vol. 1, pp. 265-281, 2006. [DOI:10.1504/IJMSO.2006.012951]
63. [63] G. Antoniou and F. Van Harmelen, A semantic web primer: MIT press, 2004.
64. [64] S. E. Middleton, N. R. Shadbolt, and D. C. De Roure, "Ontological user profiling in recommender systems," ACM Transactions on Information Systems (TOIS), vol. 22, pp. 54-88, 2004. [DOI:10.1145/963770.963773]
65. [65] N. Guarino, C. Masolo, and G. Vetere, "Ontoseek: Content-based access to the web," IEEE Intelligent Systems and their Applications, vol. 14, pp. 70-80, 1999. [DOI:10.1109/5254.769887]
66. [66] M. Craven, A. McCallum, D. PiPasquo, T. Mitchell, and D. Freitag, "Learning to extract symbolic knowledge from the world wide web," Carnegie-mellon univ pittsburgh pa school of computer Science1998.
67. [67] D. Godoy and A. Amandi, "User profiling for web page filtering," IEEE Internet computing, vol. 9, pp. 56-64, 2005. [DOI:10.1109/MIC.2005.90]
68. [68] S. Gauch, J. Chaffee, and A. Pretschner, "Ontology-based personalized search and browsing," Web Intelligence and Agent Systems: An international Journal, vol. 1, pp. 219-234, 2003.
69. [69] M. I. Martín-Vicente, A. Gil-Solla, M. Ramos-Cabrer, J. J. Pazos-Arias, Y. Blanco-Fernández, and M. López-Nores, "A semantic approach to improve neighborhood formation in collaborative recommender systems," Expert Systems with Applications, vol. 41, pp. 7776-7788, 2014. [DOI:10.1016/j.eswa.2014.06.038]
70. [70] A. Sieg, B. Mobasher, and R. Burke, "Improving the effectiveness of collaborative recommendation with ontology-based user profiles," in proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems, 2010, pp. 39-46. [DOI:10.1145/1869446.1869452]
71. [71] I. Cantador, A. Bellogín, and P. Castells, "A multilayer ontology-based hybrid recommendation model," Ai Communications, vol. 21, pp. 203-210, 2008. [DOI:10.3233/AIC-2008-0437]
72. [72] Y. Deng, Z. Wu, C. Tang, H. Si, H. Xiong, and Z. Chen, "A hybrid movie recommender based on ontology and neural networks," in Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing, 2010, pp. 846-851. [DOI:10.1109/GreenCom-CPSCom.2010.144] [PMID]
73. [73] L. Zhuhadar, O. Nasraoui, R. Wyatt, and E. Romero, "Multi-model ontology-based hybrid recommender system in e-learning domain," in Web Intelligence and Intelligent Agent Technologies, 2009. WI-IAT'09. IEEE/WIC/ACM International Joint Conferences on, 2009, pp. 91-95. [DOI:10.1109/WI-IAT.2009.238]
74. [74] A. Moreno, A. Valls, D. Isern, L. Marin, and J. Borràs, "Sigtur/e-destination: Ontology-based personalized recommendation of tourism and leisure activities," Engineering Applications of Artificial Intelligence, vol. 26, pp. 633-651, 2013. [DOI:10.1016/j.engappai.2012.02.014]
75. [75] J. Lu, Q. Shambour, Y. Xu, Q. Lin, and G. Zhang, "Bizseeker: A hybrid semantic recommendation system for personalized government-to-business e-services," Internet Research, vol. 20, pp. 342-365, 2010. [DOI:10.1108/10662241011050740]
76. [76] O. Daramola, M. Adigun, and C. Ayo, "Building an ontology-based framework for tourism recommendation services," Information and communication technologies in tourism 2009, pp. 135-147, 2009. [DOI:10.1007/978-3-211-93971-0_12]
77. [77] R. Q. Wang and F.-S. Kong, "Semantic-enhanced personalized recommender system," in Machine Learning and Cybernetics, 2007 International Conference on, 2007, pp. 4069-4074. [DOI:10.1109/ICMLC.2007.4370858]
78. [78] S. Trewin, "Knowledge-based recommender systems," Encyclopedia of library and information science, vol. 69, p. 180, 2000.
79. [79] C.-N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen, "Improving recommendation lists through topic diversification," in Proceedings of the 14th international conference on World Wide Web, 2005, pp. 22-32. [DOI:10.1145/1060745.1060754]
80. [80] T. N. Pham, T.-H. Vuong, T.-H. Thai, M.-V. Tran, and Q.-T. Ha, "Sentiment analysis and user similarity for social recommender system: An experimental study," in Information science and applications (icisa) 2016, ed: Springer, 2016, pp. 1147-1156. [DOI:10.1007/978-981-10-0557-2_109]
81. [81] P. Buitelaar, P. Cimiano, and B. Magnini, Ontology learning from text: Methods, evaluation and applications vol. 123: IOS press, 2005.
82. [82] B. Sarwar, G. Karypis, J. Konstan, & J. Riedl, "Analysis of recommendation algorithms for e-commerce", Paper presented at the Proceedings of the 2nd ACM conference on Electronic commerce, 2000. [DOI:10.1145/352871.352887]
83. [83] P. Cremonesi, Y. Koren, and R. Turrin, "Performance of recommender algorithms on top-n recommendation tasks," in Proceedings of the fourth ACM conference on Recommender systems, 2010, pp. 39-46. [DOI:10.1145/1864708.1864721]
84. [84] R. Bambini, P. Cremonesi, R. Turrin, "A Recommender System for an IPTV Service Provider: a Real Large-Scale Production Environment", In book: Recommender Systems Handbook (pp.299-331), DOI: 10.1007/978-0-387-85820-3_9. [DOI:10.1007/978-0-387-85820-3_9]
85. [85] M. Nilashi, O. Ibrahim, K. Bagherifard, "A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques", Expert Systems with Applications, vol. 92, pp. 507-520, 2015. [DOI:10.1016/j.eswa.2017.09.058]
86. [86] Wang H, Wang N (2015) Collaborative deep learning for recommender systems. Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 1235-1244. https://doi.org/10.1145/2783258.2783273 [DOI:10.1145/2783258.2783273.]
87. [87] Zhang L, Luo T, Zhang F, Wu Y (2018) A recommendation model based on deep neural network. IEEE Access 6:9454-9463. https://doi.org/10.1109/ACCESS.2018.2789866 [DOI:10.1109/ACCESS.2018.2789866.]
88. [88] Zhang S, Yao L, Sun A, Tay Y (2019) Deep Learning Based Recommender System: A Survey and New Perspectives. ACM Comput. Surv.52:5:1-5:38. https://doi.org/10.1145/3285029 [DOI:10.1145/3285029.]
89. [89] H. Cui, M. Zhu, and S. Yao, "Ontology-based Top-N Recommendations on new items with matrix factorization," Journal of Software, vol. 9, pp. 2026-2032, 2014. [DOI:10.4304/jsw.9.8.2026-2032]

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