Mining User Facebook Post Likes for Cross Domain Product Recommendations across E- commerce platforms

Ecommerce recommendation accuracy can be improved by mining patterns from other domains such as social media (Facebook), to predict purchase behaviours. The "cross-site cold start problem" arises when traditional recommender systems, relying on e-commerce purchase history, face platforms with no user history. Existing systems such as the GaoLinRec23 (2023) system that employs (CMF) Collective Matrix Factorization to jointly factorize user-item interaction matrices from both domains, WangZhaoRec21 (2021), GaoRec20 (2021), WangHeNeiChuaRec17 (2017), which also incorporate user attribute and social connections from the social media domain have attempted to bridge the gap, however the assumption that specific item embeddings which are the specific product details are shared between these domains does not align with the real-world scenario. Major e-commerce and social media platforms, including Amazon and Facebook, typically do not exchange granular product information. This disconnect poses a critical obstacle for existing recommendation systems in providing accurate suggestions for users starting with no observable e-commerce activity. This thesis proposes Facebook Data Cross Recommendation ‘2023 (FD-CDR ’23) system, which uses the proposed MLTU (Mine Likes and Transactions per User) algorithm to extract likes and purchase history of users from both domains, transforming then into itemsets. A modified association rule mining is applied uncover patterns of frequent co-occurrence between user Facebook post likes and e-commerce transactions as rules. It then uses the proposed HARR (Hybrid Association Rule Recommendation) algorithm to match new user facebook likes to, generated rules such as “Users who typically like cooking posts, buy cooking recipes” without needing to share item embeddings across platforms and still solve the cross-site cold start problem. Experimental results with precision and recall show that the proposed FD-CDR’23 system provides more accurate recommendations than the mentioned existing systems.

Name thesis:degree_name

M.Sc.

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Windsor

Year dc:date.issued

2024

Author dc:creator

• Ainoo, Emmanuel Jojoe

Advisor dc:contributor.advisor

• Ezeife, Christie I.

Contributors dc:contributor

• scholarship@uwindsor.ca

Licence

• http://creativecommons.org/licenses/by-nc-nd/4.0/

Language dc:language.iso

en_CA

Handle dc:identifier.uri

https://hdl.handle.net/20.500.14776/8570

OAI identifier oai:identifier

oai:uwindsor.scholaris.ca:20.500.14776/8570