Abstract
Existing multi-source E-Commerce recommendation algorithms, such as Multi-source Category Extension system (ECCF19), use item categories to improve the quality of the user-item rating matrix input to the collaborative filtering (CF) process for better recommendations. HPCRec18 model derives a consequential bond between clicks and purchases to predict preferences for users with no purchase history, whereas HSPRec19 uses sequential purchase patterns from historical data as well as consequential bond in mined patterns to improve the user-item matrix for CF. None of these systems use both the historical and item description data to address the CF limitations.
This paper proposes a Multi-source Category Extended Historical Sequential Pattern Recommendation System (MCE-HSPRec), an extension of the HSPRec19 system to increase recommendation coverage and alleviate new item problem by enriching item category information. MCE-HSPRec derives enriched category-based user profiles by analyzing item categories that are frequently purchased together. Results show that HSPRec achieves 36.64% more prediction coverage compared to HSPRec. MCE-HSPRec also obtains high precision and recall values (0.94716, 0.94781) in comparison to HSPRec19 (0.8985, 0.90002).
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References
Aggarwal, C.C.: An introduction to recommender systems. In: Aggarwal, C.C. (ed.) Recommender Systems, pp. 1–28. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29659-3_1
Anwar, T., Uma, V.: CD-SPM: cross-domain book recommendation using sequential pattern mining and rule mining. J. King Saud Univ.-Comput. Inf. Sci. 34(3), 793–800 (2022)
Bhatta, R., Ezeife, C.I., Butt, M.N.: Mining sequential patterns of historical purchases for e-commerce recommendation. In: Ordonez, C., Song, I.-Y., Anderst-Kotsis, G., Tjoa, A.M., Khalil, I. (eds.) DaWaK 2019. LNCS, vol. 11708, pp. 57–72. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27520-4_5
Brown, S.: Amazon sellers beg and bribe customers to delete negative reviews (2021). https://www.cnet.com/tech/services-and-software/
Ezeife, C.I., Aravindan, V., Chaturvedi, R.: Mining integrated sequential patterns from multiple databases. Int. J. Data Warehous. Min. (IJDWM) 16(1), 1–21 (2020)
Ghazanfar, M.A., Prugel-Bennett, A.: A scalable, accurate hybrid recommender system. In: 2010 Third International Conference on Knowledge Discovery and Data Mining, pp. 94–98. IEEE (2010)
Gupta, S., Dave, M.: An overview of recommendation system: methods and techniques. In: Sharma, H., Govindan, K., Poonia, R.C., Kumar, S., El-Medany, W.M. (eds.) Advances in Computing and Intelligent Systems. AIS, pp. 231–237. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-0222-4_20
Hug, N.: Surprise: a Python library for recommender systems. J. Open Source Softw. 5(52), 2174 (2020)
Kechinov, M.: Ecommerce events history in electronics store (2019). https://www.kaggle.com/mkechinov/ecommerce-events-history-in-electronics-store
Kumar, N.P., Fan, Z.: Hybrid user-item based collaborative filtering. Procedia Comput. Sci. 60, 1453–1461 (2015)
Mauro, N., Ardissono, L.: Extending a tag-based collaborative recommender with co-occurring information interests. In: Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization, pp. 181–190 (2019)
Nilashi, M., Ibrahim, O., Bagherifard, K.: A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Syst. Appl. 92, 507–520 (2018)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: Proceedings of the 2nd ACM Conference on Electronic Commerce, pp. 158–167 (2000)
Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_9
Schoinas, I., Tjortjis, C.: MuSIF: a product recommendation system based on multi-source implicit feedback. In: MacIntyre, J., Maglogiannis, I., Iliadis, L., Pimenidis, E. (eds.) Artificial Intelligence Applications and Innovations. IFIP Advances in Information and Communication Technology, vol. 559, pp. 660–672. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-19823-7_55
Shu, J., Shen, X., Liu, H., Yi, B., Zhang, Z.: A content-based recommendation algorithm for learning resources. Multimedia Syst. 24(2), 163–173 (2018)
Uddin, B.: Multi-data source recommendations with derived sequential pattern mining. Electronic Theses and Dissertations. 9046 (2023). https://scholar.uwindsor.ca/etd/9046
Xiao, Y., Ezeife, C.I.: E-commerce product recommendation using historical purchases and clickstream data. In: Ordonez, C., Bellatreche, L. (eds.) DaWaK 2018. LNCS, vol. 11031, pp. 70–82. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-98539-8_6
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Chaturvedi, R., Ezeife, C.I., Uddin, M.B. (2023). Using Derived Sequential Pattern Mining for E-Commerce Recommendations in Multiple Sources. In: Delir Haghighi, P., et al. Information Integration and Web Intelligence. iiWAS 2023. Lecture Notes in Computer Science, vol 14416. Springer, Cham. https://doi.org/10.1007/978-3-031-48316-5_35
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