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Erschienen in: International Journal of Data Science and Analytics 1/2023

24.07.2022 | Regular Paper

Semantic enhanced Markov model for sequential E-commerce product recommendation

verfasst von: Mahreen Nasir, C. I. Ezeife

Erschienen in: International Journal of Data Science and Analytics | Ausgabe 1/2023

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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

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Metadaten
Titel
Semantic enhanced Markov model for sequential E-commerce product recommendation
verfasst von
Mahreen Nasir
C. I. Ezeife
Publikationsdatum
24.07.2022
Verlag
Springer International Publishing
Erschienen in
International Journal of Data Science and Analytics / Ausgabe 1/2023
Print ISSN: 2364-415X
Elektronische ISSN: 2364-4168
DOI
https://doi.org/10.1007/s41060-022-00343-y

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