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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 …
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 …