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24-07-2022 | Regular Paper

Semantic enhanced Markov model for sequential E-commerce product recommendation

Authors: Mahreen Nasir, C. I. Ezeife

Published in: International Journal of Data Science and Analytics

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Abstract

To model sequential relationships between items, Markov Models build a transition probability matrix \(\mathbf {P}\) of size \(n \times n\), where n represents number of states (items) and each matrix entry \(p_{(i,j)}\) represents transition probabilities from state i to state j. Existing systems such as factorized personalized Markov chains (FPMC) and fossil either combine sequential information with user preference information or add the high-order Markov chains concept. However, they suffer from (i) model complexity: an increase in Markov Model’s order (number of states) and separation of sequential pattern and user preference matrices, (ii) sparse transition probability matrix: few product purchases from thousands of available products, (iii) ambiguous prediction: multiple states (items) having same transition probability from current state and (iv) lack of semantic knowledge: transition to next state (item) depends on probabilities of items’ purchase frequency. To alleviate sparsity and ambiguous prediction problems, this paper proposes semantic-enabled Markov model recommendation (SEMMRec) system which inputs customers’ purchase history and products’ metadata (e.g., title, description and brand) and extract products’ sequential and semantic knowledge according to their (i) usage (e.g., products co-purchased or co-reviewed) and (ii) textual features by finding similarity between products based on their characteristics using distributional hypothesis methods (Doc2vec and TF-IDF) which consider the context of items’ usage. Next, this extracted knowledge is integrated into the transition probability matrix \(\mathbf {P}\) to generate personalized sequential and semantically rich next item recommendations. Experimental results on various E-commerce data sets exhibit an improved performance by the proposed model
Literature
4.
go back to reference Asher, R.E., Simpson, J.M.Y.: The Encyclopedia of Language and Linguistics. Pergamon (1993) Asher, R.E., Simpson, J.M.Y.: The Encyclopedia of Language and Linguistics. Pergamon (1993)
5.
go back to reference Ayres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential Pattern mining using a bitmap representation. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’02, pp. 429–435. Association for Computing Machinery, New York, NY, USA (2002). https://​doi.​org/​10.​1145/​775047.​775109 Ayres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential Pattern mining using a bitmap representation. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’02, pp. 429–435. Association for Computing Machinery, New York, NY, USA (2002). https://​doi.​org/​10.​1145/​775047.​775109
6.
go back to reference Bernhard, S.D., Leung, C.K., Reimer, V.J., Westlake, J.: Clickstream Prediction Using Sequential Stream Mining Techniques with Markov Chains. In: Proceedings of the 20th International Database Engineering & Applications Symposium, IDEAS ’16, pp. 24–33. Association for Computing Machinery, New York, NY, USA (2016). https://​doi.​org/​10.​1145/​2938503.​2938535 Bernhard, S.D., Leung, C.K., Reimer, V.J., Westlake, J.: Clickstream Prediction Using Sequential Stream Mining Techniques with Markov Chains. In: Proceedings of the 20th International Database Engineering & Applications Symposium, IDEAS ’16, pp. 24–33. Association for Computing Machinery, New York, NY, USA (2016). https://​doi.​org/​10.​1145/​2938503.​2938535
7.
10.
go back to reference Brafman, R.I., Heckerman, D., Shani, G.: Recommendation as a Stochastic Sequential Decision Problem p. 10 Brafman, R.I., Heckerman, D., Shani, G.: Recommendation as a Stochastic Sequential Decision Problem p. 10
11.
go back to reference Cao, L.: Actionable knowledge discovery and delivery. Wiley Interdiscipl. Rev. Data Min. Knowl. Discov. 2(2), 149–163 (2012) CrossRef Cao, L.: Actionable knowledge discovery and delivery. Wiley Interdiscipl. Rev. Data Min. Knowl. Discov. 2(2), 149–163 (2012) CrossRef
12.
go back to reference Cao, L.: Combined mining: analyzing object and pattern relations for discovering and constructing complex yet actionable patterns. Wiley Interdiscipl. Rev. Data Min. Knowl. Discov. 3(2), 140–155 (2013) CrossRef Cao, L.: Combined mining: analyzing object and pattern relations for discovering and constructing complex yet actionable patterns. Wiley Interdiscipl. Rev. Data Min. Knowl. Discov. 3(2), 140–155 (2013) CrossRef
13.
go back to reference Cao, L., Zhang, C.: Domain-driven data mining: a practical methodology. Int. J. Data Warehous. Min. (IJDWM) 2(4), 49–65 (2006) CrossRef Cao, L., Zhang, C.: Domain-driven data mining: a practical methodology. Int. J. Data Warehous. Min. (IJDWM) 2(4), 49–65 (2006) CrossRef
15.
go back to reference Cao, L., Zhao, Y., Zhang, H., Luo, D., Zhang, C., Park, E.K.: Flexible frameworks for actionable knowledge discovery. IEEE Trans. Knowl. Data Eng. 22(9), 1299–1312 (2009) Cao, L., Zhao, Y., Zhang, H., Luo, D., Zhang, C., Park, E.K.: Flexible frameworks for actionable knowledge discovery. IEEE Trans. Knowl. Data Eng. 22(9), 1299–1312 (2009)
16.
17.
go back to reference Deshpande, M., Karypis, G.: Selective Markov models for predicting web page accesses. ACM Trans. Internet Technol. 4(2), 163–184 (2004) CrossRef Deshpande, M., Karypis, G.: Selective Markov models for predicting web page accesses. ACM Trans. Internet Technol. 4(2), 163–184 (2004) CrossRef
19.
go back to reference Ekstrand, M.D., Riedl, J.T., Konstan, J.A.: Collaborative Filtering Recommender Systems. Now Publishers Inc, Norwell (2011) CrossRef Ekstrand, M.D., Riedl, J.T., Konstan, J.A.: Collaborative Filtering Recommender Systems. Now Publishers Inc, Norwell (2011) CrossRef
21.
go back to reference Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17(3), 37 (1996) Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AI Mag. 17(3), 37 (1996)
22.
go back to reference Fayyad, U.M., Piatetsky-Shapiro, G., Uthurusamy, R.: Summary from the kdd-03 panel: data mining: the next 10 years. ACM Sigkdd Explor. Newslett. 5(2), 191–196 (2003) CrossRef Fayyad, U.M., Piatetsky-Shapiro, G., Uthurusamy, R.: Summary from the kdd-03 panel: data mining: the next 10 years. ACM Sigkdd Explor. Newslett. 5(2), 191–196 (2003) CrossRef
24.
go back to reference Grbovic, M., Radosavljevic, V., Djuric, N., Bhamidipati, N., Savla, J., Bhagwan, V., Sharp, D.: E-commerce in Your Inbox: Product Recommendations at Scale. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’15, pp. 1809–1818. Association for Computing Machinery, New York, NY, USA (2015). https://​doi.​org/​10.​1145/​2783258.​2788627 Grbovic, M., Radosavljevic, V., Djuric, N., Bhamidipati, N., Savla, J., Bhagwan, V., Sharp, D.: E-commerce in Your Inbox: Product Recommendations at Scale. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’15, pp. 1809–1818. Association for Computing Machinery, New York, NY, USA (2015). https://​doi.​org/​10.​1145/​2783258.​2788627
25.
go back to reference Guo, W., Wang, S., Lu, W., Wu, H., Zhang, Q., Shao, Z.: Sequential dependency enhanced graph neural networks for session-based recommendations. In: 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), pp. 1–10. IEEE (2021) Guo, W., Wang, S., Lu, W., Wu, H., Zhang, Q., Shao, Z.: Sequential dependency enhanced graph neural networks for session-based recommendations. In: 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), pp. 1–10. IEEE (2021)
28.
29.
go back to reference Hidasi, B., Quadrana, M., Karatzoglou, A., Tikk, D.: Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, RecSys ’16, pp. 241–248. Association for Computing Machinery, New York, NY, USA (2016). https://​doi.​org/​10.​1145/​2959100.​2959167 Hidasi, B., Quadrana, M., Karatzoglou, A., Tikk, D.: Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, RecSys ’16, pp. 241–248. Association for Computing Machinery, New York, NY, USA (2016). https://​doi.​org/​10.​1145/​2959100.​2959167
31.
go back to reference Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction. Cambridge University Press (2010). Google-Books-ID: eygTJBd_U2cC Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction. Cambridge University Press (2010). Google-Books-ID: eygTJBd_U2cC
37.
go back to reference Kiran, R., Kumar, P., Bhasker, B.: DNNRec: a novel deep learning based hybrid recommender system. Expert Syst. Appl. 144, 113054 (2020) CrossRef Kiran, R., Kumar, P., Bhasker, B.: DNNRec: a novel deep learning based hybrid recommender system. Expert Syst. Appl. 144, 113054 (2020) CrossRef
38.
go back to reference Li, J., Li, L., Wu, Y., Chen, S.: An improved recommender based on hidden Markov model p. 5 Li, J., Li, L., Wu, Y., Chen, S.: An improved recommender based on hidden Markov model p. 5
40.
go back to reference Liu, T., Wang, Z., Tang, J., Yang, S., Huang, G.Y., Liu, Z.: Recommender systems with heterogeneous side information. arXiv:​1907.​08679 [cs, stat] (2019) Liu, T., Wang, Z., Tang, J., Yang, S., Huang, G.Y., Liu, Z.: Recommender systems with heterogeneous side information. arXiv:​1907.​08679 [cs, stat] (2019)
45.
go back to reference Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543. Pennington, J., Socher, R., Manning, C.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543.
46.
go back to reference Polyzou, A., Karypis, G.: Scholars walk: a Markov chain framework for course recommendation p. 6 (2019) Polyzou, A., Karypis, G.: Scholars walk: a Markov chain framework for course recommendation p. 6 (2019)
48.
go back to reference Quadrana, M., Karatzoglou, A., Hidasi, B., Cremonesi, P.: Personalizing session-based recommendations with hierarchical recurrent neural networks. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys ’17, pp. 130–137. Association for Computing Machinery, New York, NY, USA (2017). https://​doi.​org/​10.​1145/​3109859.​3109896 Quadrana, M., Karatzoglou, A., Hidasi, B., Cremonesi, P.: Personalizing session-based recommendations with hierarchical recurrent neural networks. In: Proceedings of the Eleventh ACM Conference on Recommender Systems, RecSys ’17, pp. 130–137. Association for Computing Machinery, New York, NY, USA (2017). https://​doi.​org/​10.​1145/​3109859.​3109896
50.
go back to reference Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized Markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web, WWW ’10, pp. 811–820. Association for Computing Machinery, New York, NY, USA (2010). https://​doi.​org/​10.​1145/​1772690.​1772773 Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized Markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web, WWW ’10, pp. 811–820. Association for Computing Machinery, New York, NY, USA (2010). https://​doi.​org/​10.​1145/​1772690.​1772773
53.
go back to reference Salton, G.: Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley Series in Computer Science. Addison-Wesley, Reading (1988) Salton, G.: Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley Series in Computer Science. Addison-Wesley, Reading (1988)
57.
go back to reference Semeraro, G., Degemmis, M., Lops, P., Basile, P.: Combining learning and word sense disambiguation for intelligent user profiling. In: Proceedings of the 20th International Joint Conference on Artifical Intelligence, IJCAI’07, pp. 2856–2861. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2007) Semeraro, G., Degemmis, M., Lops, P., Basile, P.: Combining learning and word sense disambiguation for intelligent user profiling. In: Proceedings of the 20th International Joint Conference on Artifical Intelligence, IJCAI’07, pp. 2856–2861. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2007)
58.
61.
go back to reference Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., Jiang, P.: BERT4Rec: sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1441–1450. ACM, Beijing China (2019). https://​doi.​org/​10.​1145/​3357384.​3357895 Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., Jiang, P.: BERT4Rec: sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1441–1450. ACM, Beijing China (2019). https://​doi.​org/​10.​1145/​3357384.​3357895
64.
go back to reference Wang, J., Huang, P., Zhao, H., Zhang, Z., Zhao, B., Lee, D.L.: Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 839–848. ACM, London United Kingdom (2018). https://​doi.​org/​10.​1145/​3219819.​3219869 Wang, J., Huang, P., Zhao, H., Zhang, Z., Zhao, B., Lee, D.L.: Billion-scale Commodity Embedding for E-commerce Recommendation in Alibaba. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 839–848. ACM, London United Kingdom (2018). https://​doi.​org/​10.​1145/​3219819.​3219869
67.
go back to reference Xiao, Y., Yao, L., Pei, Q., Wang, X., Yang, J., Sheng, Q.Z.: Mgnn: mutualistic graph neural network for joint friend and item recommendation. IEEE Intell. Syst. 35(5), 7–17 (2020) CrossRef Xiao, Y., Yao, L., Pei, Q., Wang, X., Yang, J., Sheng, Q.Z.: Mgnn: mutualistic graph neural network for joint friend and item recommendation. IEEE Intell. Syst. 35(5), 7–17 (2020) CrossRef
68.
go back to reference Zhang, C., Philip, S.Y., Bell, D.: Introduction to the domain-drive data mining special section. IEEE Trans. Knowl. Data Eng. 22(6), 753–754 (2010) CrossRef Zhang, C., Philip, S.Y., Bell, D.: Introduction to the domain-drive data mining special section. IEEE Trans. Knowl. Data Eng. 22(6), 753–754 (2010) CrossRef
69.
go back to reference Zhao, H., Yao, Q., Song, Y., Kwok, J., Lee, D.L.: Side information fusion for recommender systems over heterogeneous information network. arXiv:​1801.​02411 [cs] (2020) Zhao, H., Yao, Q., Song, Y., Kwok, J., Lee, D.L.: Side information fusion for recommender systems over heterogeneous information network. arXiv:​1801.​02411 [cs] (2020)
70.
go back to reference Zhong, M., Li, C., Wen, J., Liu, L., Ma, J., Zhang, G., Yang, Y.: Hignet: hierarchical and interactive gate networks for item recommendation. IEEE Intell. Syst. 35(5), 50–61 (2020) CrossRef Zhong, M., Li, C., Wen, J., Liu, L., Ma, J., Zhang, G., Yang, Y.: Hignet: hierarchical and interactive gate networks for item recommendation. IEEE Intell. Syst. 35(5), 50–61 (2020) CrossRef
Metadata
Title
Semantic enhanced Markov model for sequential E-commerce product recommendation
Authors
Mahreen Nasir
C. I. Ezeife
Publication date
24-07-2022
Publisher
Springer International Publishing
Published in
International Journal of Data Science and Analytics
Print ISSN: 2364-415X
Electronic ISSN: 2364-4168
DOI
https://doi.org/10.1007/s41060-022-00343-y

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