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Comparative Analysis: Recommendation Techniques in E-Commerce

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Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23) (ACR 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 700))

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Abstract

Recommendation systems have become more vital in addressing the current state of information overload in e-commerce. It assists in filtering data according to customer’s personal interests. This research did comparative analysis on 30 papers that developed recommendation systems, and the techniques they utilized to generate customised and personalised data according to the customer needs. Then it proposed a new model considering the shortcoming of the analysed systems. It incorporates the nature of the data whether implicit and explicit, Recommendations techniques, and view of the data to provide recommendations that can assist e-commerce businesses to provide the products and services that better suits the customers’ customized and personalised preferences from enormous amount of collected data.

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References

  1. Kim, J., Choi, I., Li, Q.: Customer satisfaction of recommender system: examining accuracy and diversity in several types of recommendation approaches. Sustainability 13(11), 6165 (2021)

    Article  Google Scholar 

  2. Geuens, S., Coussement, K., De Bock, K.W.: A framework for configuring collaborative filtering-based recommendations derived from purchase data. Eur. J. Oper. Res. 265(1), 208–218 (2018). https://doi.org/10.1016/j.ejor.2017.07.005

    Article  MATH  Google Scholar 

  3. Song, H., Moon, N.: Eye-tracking and social behavior preference-based recommendation system. J. Supercomput. 75(4), 1990–2006 (2018). https://doi.org/10.1007/s11227-018-2447-x

    Article  Google Scholar 

  4. Putra, A.S., Waruwu, H., Asbari, M., Novitasari, D., Purwanto, A.: Leadership in the Innovation Era: transactional or transformational Style? Int. J. Soc. Manag. Stud. 1(1), 89–94 (2020)

    Google Scholar 

  5. Wang, C.-D., Deng, Z.-H., Lai, J.-H., Yu, P.S.: Serendipitous recommendation in e-commerce using innovator-based collaborative filtering. IEEE Trans. Cybern. 49(7), 2678–2692 (2019). https://doi.org/10.1109/TCYB.2018.2841924

    Article  Google Scholar 

  6. Balush, I., Vysotska, V., Albota, S.: Recommendation System Development Based on Intelligent Search, NLP and Machine Learning Methods. In: MoMLeT+ DS, pp. 584–617 (2021)

    Google Scholar 

  7. Putra, A.S., Aisyah, N.: Sistem pembelajaran online menggunakan virtual reality. In: Prosiding Seminar Nasional Pendidikan, vol. 3, pp. 295–303 (2021)

    Google Scholar 

  8. Hwangbo, H., Kim, Y.S., Cha, K.J.: Recommendation system development for fashion retail e-commerce. Electr. Commer. Res. Appl. 28, 94–101 (2018). https://doi.org/10.1016/j.elerap.2018.01.012

    Article  Google Scholar 

  9. Liu, C.-L., Wu, X.-W.: Fast recommendation on latent collaborative relations. Knowl.-Based Syst. 109, 25–34 (2016). https://doi.org/10.1016/j.knosys.2016.06.016

    Article  Google Scholar 

  10. Liu, C.-L., Wu, X.-W.: Large-scale recommender system with compact latent factor model. Expert Syst. Appl. 64, 467–475 (2016). https://doi.org/10.1016/j.eswa.2016.08.009

    Article  Google Scholar 

  11. Odić, A., Tkalčič, M., Tasič, J.F., Košir, A.: Predicting and detecting the relevant contextual information in a movie-recommender system. Interact. Comput. 25(1), 74–90 (2013). https://doi.org/10.1093/iwc/iws003

    Article  Google Scholar 

  12. Qiu, J., Liu, C., Li, Y., Lin, Z.: Leveraging sentiment analysis at the aspects level to predict ratings of reviews. Inf. Sci. 451–452, 295–309 (2018). https://doi.org/10.1016/j.ins.2018.04.009

    Article  Google Scholar 

  13. Weichselbraun, A., Gindl, S., Scharl, A.: Extracting and grounding contextualized sentiment lexicons. IEEE Intell. Syst. 28(2), 39–46 (2013). https://doi.org/10.1109/MIS.2013.41

    Article  Google Scholar 

  14. Adomavicius, G., Kwon, Y.: New recommendation techniques for multicriteria rating systems. IEEE Intell. Syst. 22(3), 48–55 (2007). https://doi.org/10.1109/MIS.2007.58

    Article  Google Scholar 

  15. Lu, J., Wu, D., Mao, M., Wang, W., Zhang, G.: Recommender system application developments: a survey. Decis. Support Syst. 74, 12–32 (2015). https://doi.org/10.1016/j.dss.2015.03.008

    Article  Google Scholar 

  16. Tarus, J.K., Niu, Z., Kalui, D.: A hybrid recommender system for e-learning based on context awareness and sequential pattern mining. Soft. Comput. 22(8), 2449–2461 (2017). https://doi.org/10.1007/s00500-017-2720-6

    Article  Google Scholar 

  17. Chen, W., Niu, Z., Zhao, X., Li, Y.: A hybrid recommendation algorithm adapted in e-learning environments. World Wide Web 17(2), 271–284 (2012). https://doi.org/10.1007/s11280-012-0187-z

    Article  Google Scholar 

  18. Adomavicius, G., Kwon, Y.: Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans. Knowl. Data Eng. 24(5), 896–911 (2012). https://doi.org/10.1109/TKDE.2011.15

    Article  Google Scholar 

  19. Kanavos, A., Iakovou, S.A., Sioutas, S., Tampakas, V.: Large scale product recommendation of supermarket ware based on customer behaviour analysis. Big Data Cogn. Comput. 2(2), 11 (2018)

    Article  Google Scholar 

  20. Victor, G.S., Antonia, P., Spyros, S.: Csmr: A scalable algorithm for text clustering with cosine similarity and mapreduce. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H., Sioutas, S., Makris, C. (eds.) AIAI 2014. IAICT, vol. 437, pp. 211–220. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44722-2_23

    Chapter  Google Scholar 

  21. Qian, X., Feng, H., Zhao, G., Mei, T.: Personalized recommendation combining user interest and social circle. IEEE Trans. Knowl. Data Eng. 26(7), 1763–1777 (2014). https://doi.org/10.1109/TKDE.2013.168

    Article  Google Scholar 

  22. Choi, I.Y., Oh, M.G., Kim, J.K., Ryu, Y.U.: Collaborative filtering with facial expressions for online video recommendation. Int. J. Inf. Manage. 36(3), 397–402 (2016). https://doi.org/10.1016/j.ijinfomgt.2016.01.005

    Article  Google Scholar 

  23. Bashyal, S., Venayagamoorthy, G.K.: Recognition of facial expressions using Gabor wavelets and learning vector quantization. Eng. Appl. Artif. Intell. 21(7), 1056–1064 (2008). https://doi.org/10.1016/j.engappai.2007.11.010

    Article  Google Scholar 

  24. Chen, B.-L., Li, F.-F., Zhang, Y.-J., Ma, J.-L.: Information filtering in evolving online networks. Phys. Lett. A 382(5), 265–271 (2018). https://doi.org/10.1016/j.physleta.2017.11.027

    Article  MathSciNet  Google Scholar 

  25. Wei, J., He, J., Chen, K., Zhou, Y., Tang, Z.: Collaborative filtering and deep learning based recommendation system for cold start items. Expert Syst. Appl. 69, 29–39 (2017). https://doi.org/10.1016/j.eswa.2016.09.040

    Article  Google Scholar 

  26. Chang, W.-L., Jung, C.-F.: A hybrid approach for personalized service staff recommendation. Inf. Syst. Front. 19(1), 149–163 (2015). https://doi.org/10.1007/s10796-015-9597-7

    Article  Google Scholar 

  27. Ha, T., Lee, S.: Item-network-based collaborative filtering: a personalized recommendation method based on a user’s item network. Inf. Process. Manage. 53(5), 1171–1184 (2017). https://doi.org/10.1016/j.ipm.2017.05.003

    Article  Google Scholar 

  28. Mezni, H., Abdeljaoued, T.: A cloud services recommendation system based on fuzzy formal concept analysis. Data Knowl. Eng. 116, 100–123 (2018). https://doi.org/10.1016/j.datak.2018.05.008

    Article  Google Scholar 

  29. Zhang, S., Zhang, S., Yen, N.Y., Zhu, G.: The recommendation system of micro-blog topic based on user clustering. Mobile Netw. Appl. 22(2), 228–239 (2016). https://doi.org/10.1007/s11036-016-0790-9

    Article  Google Scholar 

  30. Bagherifard, K., Rahmani, M., Nilashi, M., Rafe, V.: Performance improvement for recommender systems using ontology. Telematics Inform. 34(8), 1772–1792 (2017). https://doi.org/10.1016/j.tele.2017.08.008

    Article  Google Scholar 

  31. Hwangbo, H., Kim, Y.: An empirical study on the effect of data sparsity and data overlap on cross domain collaborative filtering performance. Expert Syst. Appl. 89, 254–265 (2017). https://doi.org/10.1016/j.eswa.2017.07.041

    Article  Google Scholar 

  32. Chai, Z., Li, Y.-L., Han, Y.-M., Zhu, S.-F.: Recommendation system based on singular value decomposition and multi-objective immune optimization. IEEE Access 7, 6060–6071 (2019). https://doi.org/10.1109/ACCESS.2018.2842257

    Article  Google Scholar 

  33. Yagci, A.M., Aytekin, T., Gurgen, F.S.: Scalable and adaptive collaborative filtering by mining frequent item co-occurrences in a user feedback stream. Eng. Appl. Artif. Intell. 58, 171–184 (2017). https://doi.org/10.1016/j.engappai.2016.10.011

    Article  Google Scholar 

  34. Yun, Y., Hooshyar, D., Jo, J., Lim, H.: Developing a hybrid collaborative filtering recommendation system with opinion mining on purchase review. J. Inf. Sci. 44(3), 331–344 (2018). https://doi.org/10.1177/0165551517692955

    Article  Google Scholar 

  35. Yang, Y., Xu, Y., Wang, E., Han, J., Yu, Z.: Improving existing collaborative filtering recommendations via serendipity-based algorithm. IEEE Trans. Multimed. 20(7), 1888–1900 (2017). https://doi.org/10.1109/TMM.2017.2779043

    Article  Google Scholar 

  36. Ochirbat, A., et al.: Hybrid occupation recommendation for adolescents on interest, profile, and behavior. Telematics Inform. 35(3), 534–550 (2018). https://doi.org/10.1016/j.tele.2017.02.002

    Article  Google Scholar 

  37. Fu, M., Qu, H., Moges, D., Lu, L.: Attention based collaborative filtering. Neurocomputing 311, 88–98 (2018). https://doi.org/10.1016/j.neucom.2018.05.049

    Article  Google Scholar 

  38. Hsieh, M.-Y., Weng, T.-H., Li, K.-C.: A keyword-aware recommender system using implicit feedback on Hadoop. J. Parallel Distrib. Comput. 116, 63–73 (2018). https://doi.org/10.1016/j.jpdc.2017.12.008

    Article  Google Scholar 

  39. Xu, W., Sun, J., Ma, J., Du, W.: A personalized information recommendation system for R&D project opportunity finding in big data contexts. J. Netw. Comput. Appl. 59, 362–369 (2016). https://doi.org/10.1016/j.jnca.2015.01.003

    Article  Google Scholar 

  40. Ma, W., Ren, C., Wu, Y., Wang, S., Feng, X.: Personalized recommendation via unbalance full-connectivity inference. Physica A 483, 273–279 (2017)

    Article  Google Scholar 

  41. Scholz, M., Dorner, V., Schryen, G., Benlian, A.: A configuration-based recommender system for supporting e-commerce decisions. Eur. J. Oper. Res. 259(1), 205–215 (2017). https://doi.org/10.1016/j.ejor.2016.09.057

    Article  MATH  Google Scholar 

  42. Wang, Y., Deng, J., Gao, J., Zhang, P.: A hybrid user similarity model for collaborative filtering. Inf. Sci. 418–419, 102–118 (2017). https://doi.org/10.1016/j.ins.2017.08.008

    Article  Google Scholar 

  43. Polatidis, N., Georgiadis, C.K.: A dynamic multi-level collaborative filtering method for improved recommendations. Comput. Stan. Interfaces 51, 14–21 (2017). https://doi.org/10.1016/j.csi.2016.10.014

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Central Queensland University, Norman Gardens, QLD, Australia

    Waleed Ibrahim

  2. The University of Sydney Business School, Darlington, NSW, Australia

    Waleed Ibrahim

  3. Study Group Australia, Darlinghurst, NSW, Australia

    Binaya Subedi

  4. Western Sydney University, Penrith, NSW, Australia

    Sabreena Zoha

  5. Asia Pacific International College (APIC), Parramatta, NSW, Australia

    Abdussalam Ali

  6. Crown Institute of Higher Education, North Sydney, NSW, Australia

    Emran Salahuddin

Authors
  1. Waleed Ibrahim
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  2. Binaya Subedi
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  3. Sabreena Zoha
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  4. Abdussalam Ali
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  5. Emran Salahuddin
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Corresponding author

Correspondence to Waleed Ibrahim .

Editor information

Editors and Affiliations

  1. University of Detroit Mercy, Detroit, MI, USA

    Kevin Daimi

  2. Charles Sturt University, Sydney, NSW, Australia

    Abeer Al Sadoon

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Ibrahim, W., Subedi, B., Zoha, S., Ali, A., Salahuddin, E. (2023). Comparative Analysis: Recommendation Techniques in E-Commerce. In: Daimi, K., Al Sadoon, A. (eds) Proceedings of the 2023 International Conference on Advances in Computing Research (ACR’23). ACR 2023. Lecture Notes in Networks and Systems, vol 700. Springer, Cham. https://doi.org/10.1007/978-3-031-33743-7_8

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