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A Survey And Taxonomy Of Sequential Recommender Systems For E-Commerce Product Recommendation, Mahreen Nasir, C. I. Ezeife
A Survey And Taxonomy Of Sequential Recommender Systems For E-Commerce Product Recommendation, Mahreen Nasir, C. I. Ezeife
Computer Science Publications
E-commerce recommendation systems facilitate customers’ purchase decision by recommending products or services of interest (e.g., Amazon). Designing a recommender system tailored toward an individual customer’s need is crucial for retailers to increase revenue and retain customers’ loyalty. As users’ interests and preferences change with time, the time stamp of a user interaction (click, view or purchase event) is an important characteristic to learn sequential patterns from these user interactions and, hence, understand users’ long- and short-term preferences to predict the next item(s) for recommendation. This paper presents a taxonomy of sequential recommendation systems (SRecSys) with a focus on e-commerce product …
A Survey Of Sequential Pattern Based E-Commerce Recommendation Systems, Christie I. Ezeife, Hemni Karlapalepu
A Survey Of Sequential Pattern Based E-Commerce Recommendation Systems, Christie I. Ezeife, Hemni Karlapalepu
Computer Science Publications
E-commerce recommendation systems usually deal with massive customer sequential databases, such as historical purchase or click stream sequences. Recommendation systems’ accuracy can be improved if complex sequential patterns of user purchase behavior are learned by integrating sequential patterns of customer clicks and/or purchases into the user–item rating matrix input of collaborative filtering. This review focuses on algorithms of existing E-commerce recommendation systems that are sequential pattern-based. It provides a comprehensive and comparative performance analysis of these systems, exposing their methodologies, achievements, limitations, and potential for solving more important problems in this domain. The review shows that integrating sequential pattern mining …
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 …