Document Type

Article

Publication Date

1-1-2023

Publication Title

International Journal of Data Science and Analytics

Volume

15

Issue

1

First Page

67

Keywords

Collaborative filtering, E-commerce, Markov model, Recommendation systems, Semantics, Sequential recommendation

Last Page

91

Abstract

To model sequential relationships between items, Markov Models build a transition probability matrix P of size n× 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 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.

DOI

10.1007/s41060-022-00343-y

ISSN

2364415X

E-ISSN

23644168

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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