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Collaborative Filtering and Recommendation Algorithm for Artificial Intelligence Live Streaming E-Commerce Platforms Based on Big DataChen · 2024With the development of the Internet and mobile Internet, live streaming e-commerce has become an emerging e-commerce force. However, traditional recommendation algorithms have shortcomings in terms of accuracy and personalization of recommendation results, and more intelligent and personalized recommendation algorithms need to be applied. This article aimed to achieve personalized product recommendations and enhance the shopping experience of users by analyzing their historical behavioral data, real-time interests and needs, combined with big data and artificial intelligence technology. The collaborative filtering recommendation algorithm based on live streaming had an average recommendation accuracy of over 80% for user groups 1, 2, and 3. The research results of this article had important practical significance for promoting the healthy development of live streaming e-commerce platforms, improving user experience, and enhancing platform competitiveness. © 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0](https://creativecommons.org/licenses/by-nc-nd/4.0)) Peer-review under responsibility of the scientific committee of the 11th International Conference on Applications and Techniques in Cyber Intelligence Yu’e Chen et al. / Procedia Computer Science 247 (2024) 826–833 827 and loyalty to the platform, but also promotes user purchasing behavior, increases purchase conversion rates, and enhances order value. This article analyzes the current application status of traditional recommendation algorithms and big data artificial intelligence in the e-commerce field, points out the shortcomings and areas for improvement of existing methods, and proposes methods such as user collaborative filtering and live streaming collaborative filtering to improve recommendation accuracy. On this basis, an in-depth analysis and exploration of collaborative filtering and recommendation algorithms for intelligent live streaming e-commerce platforms based on big data is conducted. 2. Related Works A large number of customers use traditional e-commerce portal websites, which lack product quality assurance. Rashidin Md Salamun used the status quo bias theory to study customer retention behavior on e-commerce platforms. The research findings can help managers and policy makers develop new policies to better serve customers [1]. Nichifor Eliza aimed to study the impact of using artificial intelligence through chatbots on the con

UX of AI & PersonalizationResearch Papers

ScalAR: Authoring Semantically Adaptive Augmented Reality Experiences in Virtual RealityXun Qian · 2022Augmented Reality (AR) experiences tightly associate virtual con- tents with environmental entities. However, the dissimilarity of different environments limits the adaptive AR content behaviors under large-scale deployment. We propose ScalAR, an integrated workflow enabling designers to author semantically adaptive AR ex- periences in Virtual Reality (VR). First, potential AR consumers col- lect local scenes with a semantic understanding technique. ScalAR then synthesizes numerous similar scenes. In VR, a designer au- thors the AR contents’ semantic associations and validates the design while being immersed in the provided scenes. We adopt a decision-tree-based algorithm to fit the designer’s demonstrations as a semantic adaptation model to deploy the authored AR expe- rience in a physical scene. We further showcase two application scenarios authored by ScalAR and conduct a two-session user study where the quantitative results prove the accuracy of the AR content rendering and the qualitative results show the usability of ScalAR. This work is licensed under a Creative Commons Attribution International 4.0 License. CHI ’22, April 29-May 5, 2022, New Orleans, LA, USA © 2022 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-9157-3/22/04. https://doi.org/10.1145/3491102.3517665 CCS CONCEPTS • Human-centered computing →Mixed / augmented reality; Virtual reality; Interactive systems and tools.

UX of AI & PersonalizationResearch Papers

Deep Neural Networks for YouTube RecommendationsPaul Covington, Jay Adams et al. · 2016YouTube represents one of the largest scale and most sophis- ticated industrial recommendation systems in existence. In this paper, we describe the system at a high level and fo- cus on the dramatic performance improvements brought by deep learning. The paper is split according to the classic two-stage information retrieval dichotomy: first, we detail a deep candidate generation model and then describe a sepa- rate deep ranking model. We also provide practical lessons and insights derived from designing, iterating and maintain- ing a massive recommendation system with enormous user- facing impact.

UX of AI & PersonalizationResearch Papers