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Full-Text Articles in Physical Sciences and Mathematics
Semantic Enhanced Markov Model For Sequential E-Commerce Product Recommendation, Mahreen Nasir, C. I. Ezeife
Semantic Enhanced Markov Model For Sequential E-Commerce Product Recommendation, Mahreen Nasir, C. I. Ezeife
Computer Science Publications
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 …
Improving E-Commerce Product Recommendation Using Semantic Context And Sequential Historical Purchases, Mahreen Nasir, C. I. Ezeife, Abdulrauf Gidado
Improving E-Commerce Product Recommendation Using Semantic Context And Sequential Historical Purchases, Mahreen Nasir, C. I. Ezeife, Abdulrauf Gidado
Computer Science Publications
Collaborative Filtering (CF)-based recommendation methods suffer from (i) sparsity (have low user–item interactions) and (ii) cold start (an item cannot be recommended if no ratings exist). Systems using clustering and pattern mining (frequent and sequential) with similarity measures between clicks and purchases for next-item recommendation cannot perform well when the matrix is sparse, due to rapid increase in number of items. Additionally, they suffer from: (i) lack of personalization: patterns are not targeted for a specific customer and (ii) lack of semantics among recommended items: they can only recommend items that exist as a result of a matching rule generated …