Date of Award

8-27-2019

Publication Type

Master Thesis

Degree Name

M.Sc.

Department

Computer Science

Keywords

Collaborative Filtering, Data Mining, E-Commerce, Frequent Sequential Pattern Mining, Historical Purchase, Recommendation System

Supervisor

Ezeife, C.I.

Rights

info:eu-repo/semantics/openAccess

Abstract

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 an item_i as its output from the CF process. Similarly, it derives sequential patterns from the historical purchase database from which it obtains the second output matrix of SPAPP, sequential pattern analysis predicted preference of each user for each item. The final predicted preference of each user for each item FPP is obtained by integrating these two matrices by giving 90\% to SPAPP and 10\% to CFPP so it can recommend items with highest ratings to users. A limitation of HOPE system is that in user item matrix of CF, it does not distinguish between purchase frequency and ratings used for CF. Also in SPM, it recommends items, regardless of whether user has purchased that item before or not. This thesis proposes an E-commerce recommendation system, SEERs (Stacking Ensemble E-commerce Recommendation system), which improves on HOPE system to make better recommendations in the following two ways: i) Learning the best minimum support for SPA, best k similar users for CF and the best weights for integrating the four used matrices. ii) Separating their two intermediate matrices of CFPP and SPAPP into four intermediate matrices of CF not purchased, SPM purchased, SPM not purchased and purchase history matrix which are obtained and merged with the better-learned parameters from (i) above. Experimental results show that by using best weights discovered in training phase, and also separating purchased and not purchased items in CF and sequential pattern mining methods, SEERS provides better precision, recall, F1 score, and accuracy compared to tested systems.

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