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E-Commerce Recommendation By An Ensemble Of Purchase Matrices With Sequential Patterns, Mehdi Naseri
E-Commerce Recommendation By An Ensemble Of Purchase Matrices With Sequential Patterns, Mehdi Naseri
Electronic Theses and Dissertations
In E-commerce recommendation systems, integrating collaborative filtering (CF) and sequential pattern mining (SPM) of purchase history data will improve the accuracy of recommendations and mitigate limitations of using only explicit user ratings for recommendations. Existing E-commerce recommendation systems which have combined CF with some form of sequences from purchase history are those referred to as LiuRec09, ChioRec12, and HPCRec18. ChoiRec12 system, HOPE first derives implicit ratings from purchase frequency of users in transaction data which it uses to create user item rating matrix input to CF. Then, it computes the CFPP, the CF-based predicted preference of each target user_u on …
Discovering E-Commerce Sequential Data Sets And Sequential Patterns For Recommendation, Raj Bhatta
Discovering E-Commerce Sequential Data Sets And Sequential Patterns For Recommendation, Raj Bhatta
Electronic Theses and Dissertations
In E-commerce recommendation system accuracy will be improved if more complex sequential patterns of user purchase behavior are learned and included in its user-item matrix input, to make it more informative before collaborative filtering. Existing recommendation systems that use mining techniques with some sequences are those referred to as LiuRec09, ChoiRec12, SuChenRec15, and HPCRec18. LiuRec09 system clusters users with similar clickstream sequence data, then uses association rule mining and segmentation based collaborative filtering to select Top-N neighbors from the cluster to which a target user belongs. ChoiRec12 derives a user’s rating for an item as the percentage of the user’s …