Abstract :

In Recent Years, The Boundaries Between Ecommerce And Social Networking Became A Lot Of Blurs. Many E-commerce Websites Support The Mechanism Of Social Login Where Users Can Check In The Websites Mistreatment Their Social Network Identities Like Their Face Book Or Twitter Accounts. Users Can Also Post Their Freshly Purchased Product On Micro Blogs With Links To The E-commerce Product Websites. Throughout This Paper, We Have A Bent To Propose A Novel Resolution For The Cross-site Cold-start Product Recommendation That Aims To Advocate Product From E-commerce Websites To Users At Social Networking Sites In “cold- Start” Things, A Haul That Has Rarely Been Explored Before. A Significant Challenge May Be Thanks To Leverage Information Extracted From Social Networking Sites For A Cross-site Cold- Start Product Recommendation. We Have A Bent To Propose To Use The Joined Users Across Social Networking Sites And E-commerce Websites (users Administrative Unit Have Social Networking Accounts And Have Created Purchases On E-commerce Websites) As A Bridge To Map Users’ Social Networking Choices To A Special Feature Illustration For A Product Recommendation. In Specific, We Have A Bent To Propose Learning Every Users’ And Products’ Feature Representations (called User Embeddings And Product Embeddings, Respectively) From Data Collected From Ecommerce Websites Mistreatment Recurrent Neural Networks So Apply A Modified Gradient Boosting Trees Technique To Remodel Users’ Social Networking Choices Into User Embeddings. We Have A Bent To Then Develop A Feature-based Matrix Resolution Approach Which Could Leverage The Learned User Embeddings For The Cold-start Product Recommendation. Experimental Results On An Associate Degree Outsize Dataset Created From The Foremost Vital Chinese Micro Blogging Service SINA WEIBO And So The Biggest Chinese B2C E-commerce Website JINGDONG Have Shown The Effectiveness Of Our Planned Framework

Published:

25-8-2025

Issue:

Vol. 25 No. 8 (2025)

Page Nos:

423-431

Section:

Articles

License:

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

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