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With the advent of cloud computing era and the dramatic increase in the amount of data applications, personalized recommendation technology is increasingly important. However, due to large scale and distributed processing architecture and other characteristics of cloud computing, the traditional recommendation techniques which are applied directly to the cloud computing environment will be faced with low recommendation precision, recommended delay, network overhead and other issues, leading to a sharp decline in performance recommendation. To solve these problems, the authors propose a personalized recommendation collaborative filtering mechanism RAC in the cloud computing environment. The first mechanism is to develop distributed score management strategy, by defining the candidate neighbors (CN) concept screening recommended greater impact on the results of the project set. And the authors build two stage index score based on distributed storage system, in order to ensure the recommended mechanism to locate the candidate neighbor. They propose collaborative filtering recommendation algorithm based on the candidate neighbor on this basis (CN-DCF). The target users are searched in candidate neighbors by the nearest neighbor k project score. And the target user's top-N recommendation sets are predicted. The results show that in the cloud computing environment RAC has a good recommendation accuracy and efficiency recommended.

In modern smart canteens, accurate personalized recommendations and robust security are essential for operational efficiency and user satisfaction. Traditional systems often face low accuracy, delayed response, and weak data protection. This study proposes an e-Cantong smart canteen system that integrates deep neural networks (DNNs) for feature extraction, reinforcement learning for adaptive path optimization, and a real-time feedback mechanism to dynamically adjust recommendations to changing user demands and environments. For security, a layered framework combining AES encryption, user authentication, and role-based access control is designed to ensure privacy and stability under high concurrency. Experiments on cafeteria operation records and user behavior datasets demonstrate 91.3% recommendation accuracy and 1.5-second inference latency, with stable performance in large-scale scenarios. The innovation lies in unifying adaptive recommendation and multi-level security, offering a practical path for intelligent canteen management that enhances efficiency, resilience, and user experience in complex environments.

With the continuous expansion of the scale of e-commerce, personalized recommendation technology has been widely used. However, the traditional recommendation system has been unable to meet the current needs of data processing, and good big data processing ability has become the basic requirement of the new personalized recommendation system. In addition, traditional recommendation systems are often limited to tangible goods recommendation, and pay less attention to e-commerce logistics service recommendation. In this paper, through the in-depth study of information personalized recommendation service in e-commerce environment, combined with the application background of big data: Taking the user dissimilarity matrix as the recommendation model, we propose IU usercf and UDB slope one recommendation algorithm. The two algorithms based on incremental update recommendation model have good scalability, can effectively deal with big data, and have high prediction accuracy. The proposed algorithm is applied to the actual system, taking e-commerce logistics service as the recommendation object and iu-usercf as the recommendation algorithm, the personalized recommendation system for e-commerce logistics service is constructed. The e-commerce logistics service recommendation system explores the application practice of recommendation algorithm under big data, and enriches the application scenarios of personalized recommendation technology.

This study explores the application of extensive data analysis to library information resources, focusing on enhancing the efficiency of resource utilization and user satisfaction through personalized recommendation services. We employ cluster analysis algorithms, and least squares support vector machines to model library users’ borrowing behaviors. Additionally, we use the Apriori algorithm and collaborative filtering to recommend books effectively. This study’s experimental outcomes show a notable 11.36% increase in the index of network resource demand satisfaction, indicating a significant enhancement in the library’s ability to meet resource update demands. The adoption of big data analytics has been instrumental in advancing library information management and expanding extensive data services. The findings underscore the pivotal role of big data in enhancing the efficiency of information resource utilization and elevating user satisfaction, mainly through personalized recommendations.

Traditional one-size-fits-all recommendations for student well-being and academic success may not be optimal. Personalized recommendations based on individual data hold promise. This study explores the potential of Large Language Models (LLMs) to generate personalized recommendations for 12 high school students to enhance their well-being and academic performance. We analyzed data from 12 students, including Fitbit data (activity levels, sleep and stress scores), PSQI surveys (sleep quality), and school reports (grades, teacher observations). An LLM model was used to analyze this data and create personalized recommendations for each student. Validator scoring assessed the clarity, actionability, and alignment of recommendations with student data. The LLM generated various recommendations based on different student data profiles (e.g., low activity levels, poor sleep quality). Validation results indicated that the recommendations were generally clear and actionable, with high ratings in both areas, though alignment with student data showed more variability, suggesting areas for improvement. This study demonstrates the potential of LLMs to generate personalized recommendations based on student data, acknowledging the need for further validation with initial validator feedback indicating their value. However, improvements are needed at every stage, including enhancing prompts, refining models, and incorporating advanced data analytics and continuous feedback. Future research, particularly with intervention groups and potentially RCT studies, is crucial to establish causal relationships and validate the recommendations’ impact. As this technology evolves, ensuring ethical considerations and data privacy remains essential.

With the rapid growth of Augmented Reality (AR) in e-commerce, it is necessary to conduct in-depth studies related to the integration of AR with Artificial Intelligence (AI) technology to improve personalization in product recommendations. AI and AR are expected to work together, where AI systems analyze consumer data to provide more suitable product recommendations, while AR helps consumers visualize these recommendations in the real world. This research explores the influence of AI and AR technology integration in personalized recommendations on customer usage intention, with a focus on the cosmetics industry on the e-commerce platform Shopee. Adopting the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), this research uses quantitative methods with the try-on feature of cosmetic products as the main focus. With a total number of 387 respondents, the results of the PLS-SEM analysis revealed factors impacting the intention to use, such as perceived ease of use, perceived usefulness, and users’ perceived trust, on the intention to use personalized recommendations. This research contributes to the understanding of AI–AR technology acceptance in the context of online shopping, particularly in the cosmetics sector.

Considering that the largest part of end-use energy consumption worldwide is associated with the buildings sector, there is an inherent need for the conceptualization, specification, implementation, and instantiation of novel solutions in smart buildings, able to achieve significant reductions in energy consumption through the adoption of energy efficient techniques and the active engagement of the occupants. Towards the design of such solutions, the identification of the main energy consuming factors, trends, and patterns, along with the appropriate modeling and understanding of the occupants’ behavior and the potential for the adoption of environmentally-friendly lifestyle changes have to be realized. In the current article, an innovative energy-aware information technology (IT) ecosystem is presented, aiming to support the design and development of novel personalized energy management and awareness services that can lead to occupants’ behavioral change towards actions that can have a positive impact on energy efficiency. Novel information and communication technologies (ICT) are exploited towards this direction, related mainly to the evolution of the Internet of Things (IoT), data modeling, management and fusion, big data analytics, and personalized recommendation mechanisms. The combination of such technologies has resulted in an open and extensible architectural approach able to exploit in a homogeneous, efficient and scalable way the vast amount of energy, environmental, and behavioral data collected in energy efficiency campaigns and lead to the design of energy management and awareness services targeted to the occupants’ lifestyles. The overall layered architectural approach is detailed, including design and instantiation aspects based on the selection of set of available technologies and tools. Initial results from the usage of the proposed energy aware IT ecosystem in a pilot site at the University of Murcia are presented along with a set of identified open issues for future research.

AI-personalized recommendation technology offers more accurate and diverse choices to consumers and increases click-through rates and sales on e-commerce platforms. Yet, data on consumers’ experiences of AI-personalized recommendations and their impact path on clicking intention are scarce. This article addressed these issues through three studies. In study 1, we adopted the Grounded Theory approach to conduct in-depth interviews with 30 Chinese consumers and constructed a scale to measure the impact of consumer experience on click intention. In study 2, we adopted the empirical research method to conduct reliability and validity tests on 347 valid questionnaires to finalize the scale officially. In study 3, based on the SOR theory, we constructed a model and formulated hypotheses and then conducted empirical analysis using 1097 valid questionnaires. We found that the relevance, inspiration, and insightful experiences of AI-personalized recommendations can significantly promote consumers’ clicking intention. Moreover, immersive experience mediates between the former three factors and clicking intention, and technology acceptance mediates between relevance, inspiration, and clicking intention. When consumers perceive a high degree of information privacy infringement, the immersive experience’s positive impact on clicking intention will be weakened. Meanwhile, the promoting effect of technology acceptance on clicking intention will also be inhibited. When information quality improves, the positive impact of technology acceptance on clicking intention will be enhanced. This research fills the gap in the literature on consumers’ experiences of AI-personalized recommendations and clarifies how these experiences affect the clicking intention. It offers valuable insights for e-commerce platforms to continuously optimize personalized recommendation algorithms and boost the click conversion rate of online shopping.

This paper is aimed to construct an intelligent teaching system supported by data mining, clustering, personalized recommendation and other technologies based on an in-depth study of the theoretical knowledge of new liberal arts teaching and the technology in the field of artificial intelligence. As a result, an intelligent interactive teaching mode is proposed. And the research results can be obtained by investigating the influence of intelligent interactive teaching modes on English listening, reading, and so on. The data of students’ online learning behaviors were characterized and clustered into 3 classes using the K-means method. More than 50% of the students think that the system’s exercise recommendation function can basically meet their needs. Before and after the intelligent interactive teaching, students’ language perception, language expression, language comprehension, information acquisition, affective attitudes and reading ability were significantly improved (Sig. = 0.001). After the teaching, the listening and speaking skills were all over level 6.

In this paper, we develop a novel hybrid recommender system for the tourism domain, which combines (a) a Bayesian preferences elicitation component which operates by asking the user to rate generic images (corresponding to generic types of POIs) in order to build a user model and (b) a novel content-based (CB) recommendations component. The second component can in fact itself be considered a hybrid among two different CB algorithms, each exploiting one of two semantic similarity measures: a hierarchy-based and a non-hierarchy based one. The latter is the recently introduced Weighted Extended Jaccard Similarity (WEJS). We note that WEJS is employed for the first time within a recommender algorithm. We incorporate our algorithm within a real, already available at Google Play, tour-planning mobile application for short-term visitors of the popular touristic destination of Agios Nikolaos, Crete, Greece, and evaluate our approach via extensive simulations conducted on a real-world dataset constructed for the needs of the aforementioned mobile application. Our experiments verify that our algorithms result in effective personalized recommendations of touristic points of interest, while our final hybrid algorithm outperforms our exclusively content-based recommender algorithms in terms of recommendations accuracy. Specifically, when comparing the performance of several hybrid recommender system variants, we are able to come up with a “winner”: the most preferable variant of our hybrid recommender algorithm is one using a ⟨four elicitation slates, six shown images per slate⟩ pair as input to its Bayesian elicitation component. This variant combines increased precision performance with a lightweight preferences elicitation process.