The HSPRec E-Commerce System Open Source Code Implementation
Proceedings - 2021 IEEE/ACIS 21st International Fall Conference on Computer and Information Science, ICIS 2021-Fall
Application deployment, Collaborative Filtering, E-commerce recommendation, Sequential Pattern Mining, Source code, User-item matrix quality
To promote big data application access, usage and deployment, this paper presents a downloadable open source code implementation for an E-Commerce Recommendation system, HSPRec (Historical Sequential Pattern Recommendation System), in JAVA. The HSPRec system is composed of six different modules for generating purchase/click sequential databases, mining sequential patterns, computing click purchase similarities, generating purchase sequential rules, computing weights for frequent purchase patterns through Weighted Frequent Purchase Pattern Miner, and normalization of the user-item ratings to predict level of interest. The source code of each module and the main runner are discussed under four possible headings of running environment, input data files and format, minimum support format, output data files and format. The overall goal of the HSPRec system is to improve E-commerce Recommendation accuracy by incorporating more complex sequential patterns of user purchase and click stream behavior learned through frequent sequential purchase patterns. HSPRec provides more accurate recommendations than the tested comparative systems.
Ezeife, Christie I.; Nasir, Mahreen; Chaturvedi, Ritu; and Castro, Angel Veliz. (2021). The HSPRec E-Commerce System Open Source Code Implementation. Proceedings - 2021 IEEE/ACIS 21st International Fall Conference on Computer and Information Science, ICIS 2021-Fall, 42-48.