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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

PRIME: A Personalized Recommender System for Information Visualization Methods via Extended Matrix CompletionChen, Lau · 2021Adapting user interface designs for specific tasks performed by different users is a challenging yet important problem. Automatically adapting visualization designs to users and contexts (e.g., tasks, display devices, environments, etc.) can theoretically improve human–computer interaction to acquire insights from complex datasets. However, effectiveness of any specific visualization is moderated by individual differences in knowledge, skills, and abilities for different contexts. A modeling framework called Personalized Recommender System for Information visualization Methods via Extended matrix completion (PRIME) is proposed for recommending the optimal visualization designs for individual users in different contexts. PRIME quantitatively models covariates (e.g., psychological and behavioral measurements) to predict recommendation scores (e.g., perceived complexity, mental workload, etc.) for users to adapt the visualization specific to the context. An evaluation study was conducted and showed that PRIME can achieve satisfactory recommendation accuracy for adapting visualization, even when there are limited historical data. PRIME can make accurate recommendations even for new users or new tasks based on historical wearable sensor signals and recommendation scores. This capability contributes to designing a new generation of visualization systems that will adapt to users' states. PRIME can support researchers in reducing the sample size requirements to quantify individual differences, and practitioners in adapting visualizations according to user states and contexts.

UX of AI & PersonalizationResearch Papers

e-Learning Experience using Recommender SystemsJesus Bobadill, Antonio Hernando et al. · 2011This paper presents the results obtained using a real e-learning recommender system where the collaborative filtering core has been adapted with the aim of weighting the importance of the recommendations in accordance with the users’ knowledge. In this way, ratings from users with better knowledge of the given subject will have greater importance over ratings from users with less knowledge. In the same way, we validate the results obtained and we adjust, with just one parameter, the weight that should be awarded, in each specific e-learning recommender system, to the ratings of the users with the best reputation. The results obtained show a notable improvement regarding traditional collaborative filtering methods and suggest balanced weightings between the importance assigned to users with more or less knowledge. Categories and Subject Descriptors K.3.2 [Computer and Information Science Education] General Terms Experimentation.

UX of AI & PersonalizationResearch Papers