Volume 8, Issue 2 (9-2021)
Human Information Interaction 2021, 8(2): 67-78 |
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Mohammadian S, Naghshineh N, Nakhoda M. Cross-Domain Recommendations: Foundations, Applications, and Challenges. Human Information Interaction 2021; 8 (2)
URL: http://hii.khu.ac.ir/article-1-2960-en.html
URL: http://hii.khu.ac.ir/article-1-2960-en.html
University of Tehran
Abstract: (2655 Views)
Background and Aim: The meaning of cross-domain recommendation is that instead of dealing with each domain independently, transfer knowledge gained in one domain (source) to another domain (target) and use it. The present article systematically reviews the research in this field in terms of foundations, applications and challenges.
Method: The Prisma guidelines had been used. Search in Persian and English scientific information sources with related keywords were conducted and 98 English language sources were found in the period 2007 to 2021. Applying the initial refinement, inclusion and exclusion criteria by experts, 28 English documents were selected to enter in the systematic review.
Findings: There are four levels of cross-domain recommendations: Attributes, types, items and systems. Machine learning algorithms are used to predict user rating in cross-domain recommendations, and three categories of: Prediction, ranking, and classification criteria are used to evaluate predictions based on confusion matrix. Cross-domain recommendations can be used to increase the accuracy of recommendations, resolve cold start problems, cross-sell, and improve personalization by transferring knowledge between domains. The most challengeable recommendations of cross-domain is the differences between domains. These differences include the mismatch between the properties of the domains and/or unclear relationships between the domains. In addition, differences in domain size and poor performance of basic algorithms in predicting user rating are other challenges in cross-domain recommendations.
Conclusion: While this subject has been shaped in the last decade, but the keen attention of computer science and information researchers shows its importance. Items level are the main category of cross-domain recommendations. Due to the formation of e-business groups, in the future, cross-domain recommendations at the system level will be given more consideration. Cross-domain recommendations could be used to improve the performance of recommender systems, user modeling in human-computer interaction, and e-commerce.
Method: The Prisma guidelines had been used. Search in Persian and English scientific information sources with related keywords were conducted and 98 English language sources were found in the period 2007 to 2021. Applying the initial refinement, inclusion and exclusion criteria by experts, 28 English documents were selected to enter in the systematic review.
Findings: There are four levels of cross-domain recommendations: Attributes, types, items and systems. Machine learning algorithms are used to predict user rating in cross-domain recommendations, and three categories of: Prediction, ranking, and classification criteria are used to evaluate predictions based on confusion matrix. Cross-domain recommendations can be used to increase the accuracy of recommendations, resolve cold start problems, cross-sell, and improve personalization by transferring knowledge between domains. The most challengeable recommendations of cross-domain is the differences between domains. These differences include the mismatch between the properties of the domains and/or unclear relationships between the domains. In addition, differences in domain size and poor performance of basic algorithms in predicting user rating are other challenges in cross-domain recommendations.
Conclusion: While this subject has been shaped in the last decade, but the keen attention of computer science and information researchers shows its importance. Items level are the main category of cross-domain recommendations. Due to the formation of e-business groups, in the future, cross-domain recommendations at the system level will be given more consideration. Cross-domain recommendations could be used to improve the performance of recommender systems, user modeling in human-computer interaction, and e-commerce.
References
1. Azak, M (2010) Crossing: A Framework to Develop Knowledge-based Recommenders in Cross Domains. MSc thesis, Middle East Technical University
2. Batmaz, Z., Yurekli, A., Bilge, A., & Kaleli, C. (2018). A review on deep learning for recommender systems: challenges and remedies. Artificial Intelligence Review, 52(1), 1-37. doi: 10.1007/s10462-018-9654-y [DOI:10.1007/s10462-018-9654-y]
3. Chang, W., Zhang, Q., Fu, C., Liu, W., Zhang, G., & Lu, J. (2021). A cross-domain recommender system through information transfer for medical diagnosis. Decision Support Systems, 143, 113489. doi: 10.1016/j.dss.2020.113489 [DOI:10.1016/j.dss.2020.113489]
4. Cremonesi, P., Tripodi, A., Turrin, R.: Cross-domain Recommender Systems. 11th IEEE International Conference on Data Mining Workshops, pp. 496-503 (2011) [DOI:10.1109/ICDMW.2011.57]
5. Fernández Tobías, I. (2016). Matrix factorization models for cross-domain recommendation: addressing the cold start in collaborative filtering (PH.D). Universidad Autonoma.
6. Ferreras Fernández, T., Martín-Rodero, H., García-Peñalvo, F. J., & Merlo Vega, J. A. (2016). The systematic review of literature in LIS: an approach. In F. J. García-Peñalvo (Ed.), Proceedings of the Fourth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM'16) (Salamanca, Spain, November 2-4, 2016) (pp. 291-298). New York, NY, USA: ACM . [DOI:10.1145/3012430.3012531]
7. Guo, Y., & Chen, X. (2013). A Framework for Cross-domain Recommendation in Folksonomies. Journal Of Automation And Control Engineering, 1(4), 326-331. doi: 10.12720/joace.1.4.326-331 [DOI:10.12720/joace.1.4.326-331]
8. Hao, P. (2019). Cross-domain RecommenderSystem Through Tag-basedModels (Ph.D). University of Technology Sydney.
9. Hashemi, S., & Rahmati, M. (2020). Cross-domain recommender system using generalized canonical correlation analysis. Knowledge And Information Systems, 62(12), 4625-4651. doi: 10.1007/s10115-020-01499-4 [DOI:10.1007/s10115-020-01499-4]
10. Jhaveri, M., & Pareek, J. (2012). Cross Domain Framework for Implementing Recommendation Systems Based on Context Based Implicit Negative Feedback. International Journal Of Information Systems And Social Change, 3(1), 22-36. doi: 10.4018/jissc.2012010103 [DOI:10.4018/jissc.2012010103]
11. Khanam, N. (2017). Cross Domain Collaborative Filtering Recommender Using Probabilistic Matrix Factorization. International Journal Of Advanced Research In Computer Science, 8(9), 234-249. doi: 10.26483/ijarcs.v8i9.4897 [DOI:10.26483/ijarcs.v8i9.4897]
12. Kumar, V., Shrivastva, K., & Singh, S. (2016). Cross Domain Recommendation Using Semantic Similarity and Tensor Decomposition. Procedia Computer Science, 85, 317-324. doi: 10.1016/j.procs.2016.05.239 [DOI:10.1016/j.procs.2016.05.239]
13. Loni, B, Shi, Y, Larson, M. A., Hanjalic, A.: Cross-Domain Collaborative Filtering with Factorization Machines. 36th European Conference on Information Retrieval (2014) [DOI:10.1145/2645710.2645771]
14. Melville, P., & Sindhwani, V. 2010. recommender systems. In Encyclopedia of Machine Learning. Springer US. [DOI:10.1007/978-0-387-30164-8_705]
15. Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. Plos Medicine, 6(7), e1000097. doi: 10.1371/journal.pmed.1000097 [DOI:10.1371/journal.pmed.1000097] [PMID] []
16. Pourheidari, V. (2019). Cross Domain Recommender Systems Using Matrix and Tensor Factorizations (MS). University of Saskatchewan.
17. Q. Zhang, J. Lu and G. Zhang, "Cross-Domain Recommendation with Multiple Sources," 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020, pp. 1-7, doi: 10.1109/IJCNN48605.2020.9207014. [DOI:10.1109/IJCNN48605.2020.9207014]
18. Richa, & Bedi, P. (2018). Parallel proactive cross domain context aware recommender system. Journal Of Intelligent & Fuzzy Systems, 34(3), 1521-1533. doi: 10.3233/jifs-169447 [DOI:10.3233/JIFS-169447]
19. Richa, & Bedi, P. (2021). Trust and Distrust based Cross-domain Recommender System. Applied Artificial Intelligence, 35(4), 326-351. doi: 10.1080/08839514.2021.1881297 [DOI:10.1080/08839514.2021.1881297]
20. S. E. Thendral and C. Valliyammai, "Clustering based transfer learning in cross domain recommender system," 2016 Eighth International Conference on Advanced Computing (ICoAC), Chennai, India, 2017, pp. 51-54, doi: 10.1109/ICoAC.2017.7951744. [DOI:10.1109/ICoAC.2017.7951744]
21. sahabi, S. (2016). CANONICAL CORRELATION ANALYSIS IN CROSS-DOMAIN RECOMMENDATION (Ph.D). University of Pittsburgh.
22. Sahu, A., & Dwivedi, P. (2020). Knowledge transfer by domain-independent user latent factor for cross-domain recommender systems. Future Generation Computer Systems, 108, 320-333. doi: 10.1016/j.future.2020.02.024 [DOI:10.1016/j.future.2020.02.024]
23. Sahu, A., Dwivedi, P., & Kant, V. (2018). Tags and Item Features as a Bridge for Cross-Domain Recommender Systems. Procedia Computer Science, 125, 624-631. doi: 10.1016/j.procs.2017.12.080 [DOI:10.1016/j.procs.2017.12.080]
24. Shapira, B., Rokach, L., Freilikhman, S.: Facebook Single and Cross Domain Data for Rec-ommendation Systems. User Modeling and User-Adapted Interaction 23(2-3), pp. 211-247 (2013) [DOI:10.1007/s11257-012-9128-x]
25. Sharma, S., & Sharma, D. (2018). Systematic Study and Application of Machine Learning Algorithms in Recommender System Design. International Journal Of Computer Sciences And Engineering, 6(6), 1021-1026. doi: 10.26438/ijcse/v6i6.10211026 [DOI:10.26438/ijcse/v6i6.10211026]
26. Véras, D., Prudêncio, R., & Ferraz, C. (2019). CD-CARS: Cross-domain context-aware recommender systems. Expert Systems With Applications, 135, 388-409. doi: 10.1016/j.eswa.2019.06.020 [DOI:10.1016/j.eswa.2019.06.020]
27. Wang, H., Zuo, Y., Li, H., & Wu, J. (2021). Cross-domain recommendation with user personality. Knowledge-Based Systems, 213, 106664. doi: 10.1016/j.knosys.2020.106664 [DOI:10.1016/j.knosys.2020.106664]
28. Y. Tsai, C. Wuy, H. Hsuy, T. Liuy, P. Cheny and W. C. K. Liao, "A Cross-Domain Recommender System Based on Common-Sense Knowledge Bases," 2017 Conference on Technologies and Applications of Artificial Intelligence (TAAI), Taipei, Taiwan, 2017, pp. 80-83, doi: 10.1109/TAAI.2017.48. [DOI:10.1109/TAAI.2017.48]
29. Yu, X., Lin, J., Jiang, F., Du, J., & Han, J. (2018). A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression. Computational Intelligence And Neuroscience, 2018, 1-12. doi: 10.1155/2018/1425365 [DOI:10.1155/2018/1425365] [PMID] []
30. Zhang, Q., Lu, J., Wu, D., & Zhang, G. (2019). A Cross-Domain Recommender System With Kernel-Induced Knowledge Transfer for Overlapping Entities. IEEE Transactions On Neural Networks And Learning Systems, 30(7), 1998-2012. doi: 10.1109/tnnls.2018.2875144 [DOI:10.1109/TNNLS.2018.2875144] [PMID]
31. Zhang, Q., Wu, D., Lu, J., Liu, F., & Zhang, G. (2017). A cross-domain recommender system with consistent information transfer. Decision Support Systems, 104, 49-63. doi: 10.1016/j.dss.2017.10.002 [DOI:10.1016/j.dss.2017.10.002]
32. Zhang, Y., Cao, B., Yeung, D.-Y.: Multi-Domain Collaborative Filtering. 26th Conference on Uncertainty in Artificial Intelligence, pp. 725-732 (2010)
33. Zhang, Y., Ma, X., Wan, S., Abbas, H., & Guizani, M. (2018). CrossRec: Cross-Domain Recommendations Based on Social Big Data and Cognitive Computing. Mobile Networks And Applications, 23(6), 1610-1623. doi: 10.1007/s11036-018-1112-1 [DOI:10.1007/s11036-018-1112-1]
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