Date of Award

2023

Publication Type

Thesis

Degree Name

M.Sc.

Department

Computer Science

Keywords

Category co-occurrence, Multi-source, Recommendation system, Sequential pattern mining

Supervisor

C.Ezeife

Supervisor

Y.H.Tsin

Rights

info:eu-repo/semantics/closedAccess

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Existing multi-source RS algorithms, such as ECCF19, use item categories to extend the CF model and enhance the preference matrix. The CD-SPM19 model uses item ontology to derive semantic similarity information to enrich the collaborative filtering (CF) step. The HPCRec18 model derives a consequential bond between click to purchase to predict preferences for users with no purchase history. Finally, the HSPRec19 model using the historical sequential purchase database (SHOD) algorithm, derives sequential purchase patterns from historical purchase data. It then uses the consequential bond in mined patterns to improve the user-item matrix for CF. None of these systems use historical data and item description data to address the CF system's limitation. This thesis proposes the Multi-source Category Extended Historical Sequential Pattern Recommendation System (MCE-HSPRec), an extension of the HSPRec19 system to increase recommendation coverage and alleviate new item problem by enriched item category information. The proposed algorithm derives enriched category-based user profiles by analyzing purchase behavior and item categories that are frequently purchased together. MCE-HSPRec first generates a rich user-item matrix (UI) using sequential relation derived from sequential pattern mining in the HSPRec module. Then it derives a Category Co-occurrence Graph (CCG) and a user category preference matrix (UC) from the combination of historical e-commerce and item category information. The thesis computes a category-based user profile using the UI, CCG, and UC before applying CF. Experimental results show that the proposed MCE-HSPRec achieves 36.64% more prediction coverage compared to HSPRec. MCE-HSPRec also obtains high precision and recall value of 0.94716, 0.94781 respectively compared to 0.8985, 0.90002 of HSPRec19.

Download

Share

COinS