Construction of Intelligent Recommendation Retrieval Model of FuJian Intangible Cultural Heritage Digital Archives Resources

In order to improve the consistency between the recommended retrieval results and user needs,improve the recommendation efficiency,and reduce the average absolute deviation of resource retrieval,a design method of intelligent recommendation retrieval model for Fujian intangible cultural heritage digital archive resources based on knowledge atlas is proposed.The TG-LDA(Tag-granularity LDA)model is proposed on the basis of the standard LDA(Linear Discriminant Analysis)model.The model is used to mine archive resource topics.The Pearson correlation coefficient is used to measure the relevance between topics.Based on the measurement results,the FastText deep learning model is used to achieve archive resource classification.According to the classification results,TF-IDF(term frequency–inverse document frequency)algorithm is used to calculate the weight of resource retrieval keywords to achieve resource retrieval,and a recommendation model of intangible cultural heritage digital archives resources is built through the knowledge map to achieve comprehensive and personalized recommendation of resources.The experimental results show that the recommendation and retrieval results of the proposed method are more in line with users’needs,can provide users with personalized digital archive resources,and the average absolute deviation of resource retrieval is low,the recommendation efficiency is high,and the utilization effect of archive resources is effectively improved.

Intelligent outdoor video advertisement recommendation system based on analysis of audiences' characteristics

An integrated implementation framework of an intelligent recommendation system for outdoor video advertising is proposed, which is based on the analysis of audiences＇ characteristics. Firstly, the images of the scene and the people who view the video advertisements are captured by the net- work camera deployed on the video advertising terminal side. Then audiences＇ characteristics can be obtained by applying computer vision technologies ： face detection, face tracking, gender recogni- tion and age estimation. Finally, an intelligent recommendation algorithm is designed to decide the most fitting video ads for each terminal according to multi-dimensional statistical information of its reover, a novel face detection method and a new face tracking method have been proposed to meet the practical requirements of the system, of which the average Fl-score is O. 988 and 0. 951 respec- tively.

Intelligent Recommendation and Matching Method for Agricultural Knowledge Based on Context-Aware Models

The personalized recommendation of the cloud platform for agricultural knowledge and agricultural intelligent service is one of the core technologies for the development of smart agriculture.Revealing the implicit laws and dynamic characteristics of agricultural knowledge demand is a key problem to be solved urgently.In order to enhance the matching ability of knowledge recommendation and service in human-computer interaction of cloud platform,the mechanism of agricultural knowledge intelligent recommendation service integrated with context-aware model was analyzed.By combining context data acquisition,data analysis and matching,and personalized knowledge recommendation,a framework for agricultural knowledge recommendation service is constructed to improve the ability to extract multidimensional information features and predict sequence data.Using the cloud platform for agricultural knowledge and agricultural intelligent service,this research aims to deliver interesting video service content to users in order to solve key problems faced by farmers,including planting technology,disease control,expert advice,etc.Then the knowledge needs of different users can be met and user satisfaction can be improved.

The Application of Book Intelligent Recommendation Based on the Association Rule Mining of Clementine

The traditional library can’t provide the service of personalized recommendation for users. This paper used Clementine to solve this problem. Firstly, model of K-means clustering analyze the initial data to delete the redundant data. It can avoid scanning the database repeatedly and producing a large number of false rules. Secondly, the paper used clustering results to perform association rule mining. It can obtain valuable information and achieve the service of intelligent recommendation.

Research and Modelling on the E-commerce Consumer Behavior based on Intelligent Recommendation System and Machine Learning

In this paper, we conduct research on the E-commerce consumer behavior based on the intelligent recommendation system andmachine learning. Closely associated with consumer network information search of a problem is that the consumer’s information demand ascan be thought of consumer’s information demand is leading to trigger the power of consumer network information search behavior, whenconsumer is willing to buy goods, in a certain task under the infl uence of factors, environmental factors, individual factors, consumers and thetask object interaction to form the demand of consumer cognition. Under this basis, this paper proposes the new idea on the related issues thatwill solve the related challenges.

Video Recommendation System Using Machine-Learning Techniques

In the realm of contemporary artificial intelligence,machine learning enables automation,allowing systems to naturally acquire and enhance their capabilities through learning.In this cycle,Video recommendation is finished by utilizing machine learning strategies.A suggestion framework is an interaction of data sifting framework,which is utilized to foresee the“rating”or“inclination”given by the different clients.The expectation depends on past evaluations,history,interest,IMDB rating,and so on.This can be carried out by utilizing collective and substance-based separating approaches which utilize the data given by the different clients,examine them,and afterward suggest the video that suits the client at that specific time.The required datasets for the video are taken from Grouplens.This recommender framework is executed by utilizing Python Programming Language.For building this video recommender framework,two calculations are utilized,for example,K-implies Clustering and KNN grouping.K-implies is one of the unaided AI calculations and the fundamental goal is to bunch comparable sort of information focuses together and discover the examples.For that K-implies searches for a steady‘k'of bunches in a dataset.A group is an assortment of information focuses collected due to specific similitudes.K-Nearest Neighbor is an administered learning calculation utilized for characterization,with the given information;KNN can group new information by examination of the‘k'number of the closest information focuses.The last qualities acquired are through bunching qualities and root mean squared mistake,by using this algorithm we can recommend videos more appropriately based on user previous records and ratings.

Design of a Student Recommendation Platform Based on Learning Behavior and Habit Training

This study innovatively built an intelligent analysis platform for learning behavior,which deeply integrated the cutting-edge technology of big data and Artificial Intelligence(AI),\mined and analyzed students’learning data,and realized the personalized customization of learning resources and the accurate matching of intelligent learning partners.With the help of advanced algorithms and multi-dimensional data fusion strategies,the platform not only promotes positive interaction and collaboration in the learning environment but also provides teachers with comprehensive and in-depth students’learning portraits,which provides solid support for the implementation of precision education and the personalized adjustment of teaching strategies.In this study,a recommender system based on user similarity evaluation and a collaborative filtering mechanism is carefully designed,and its technical architecture and implementation process are described in detail.

On recommendation-aware content caching for 6G:An artificial intelligence and optimization empowered paradigm

Recommendation-aware Content Caching(RCC)at the edge enables a significant reduction of the network latency and the backhaul load,thereby invigorating ubiquitous latency-sensitive innovative services.However,the effectiveness of RCC strategies is highly dependent on explicit information as regards subscribers’content request patterns,the sophisticated caching placement policy,and the personalized recommendation tactics.In this article,we investigate how the potentials of Artificial Intelligence(AI)and optimization techniques can be harnessed to address those core issues and facilitate the full implementation of RCC for the upcoming intelligent 6G era.Towards this end,we first elaborate on the hierarchical RCC network architecture.Then,the devised AI and optimization empowered paradigm is introduced,whereas AI and optimization techniques are leveraged to predict the users’content preferences in real-time situations with the assistance of their historical behavior data and determine the cache pushing and recommendation decision,respectively.Through extensive case studies,we validate the effectiveness of AI-based predictors in estimating users’content preference and the superiority of optimized RCC policies over the conventional benchmarks.At last,we shed light on the opportunities and challenges in the future.

Reservation Based Optimal Parking Lot Recommendation Model in Internet of Vehicle Environment

In order to solve the problem that the drivers can't find the optimal parking lot timely,a reservation based optimal parking lot recommendation model in Internet of Vehicle(IoV) environment is designed.Based on the users oriented parking information recommendation system,the model considers subjective demands of drivers comprehensively,makes a deeply analysis of the evaluation indicators.This recommendation model uses a phased selection method to calculate the optimal objective parking lot.The first stage is screening which based on the users' subjective parking demands;the second stage is processing the candidate parking lots through multiple attribute decision making.Simulation experiments show that this model can effectively solve the problems encountered in the process of finding optimal parking lot,save the driver's parking time and parking costs and also improve the overall utilization of parking facilities to ease the traffic congestion caused by vehicles parked patrol.

Explainable Rules and Heuristics in AI Algorithm Recommendation Approaches——A Systematic Literature Review and Mapping Study

The exponential use of artificial intelligence(AI)to solve and automated complex tasks has catapulted its popularity generating some challenges that need to be addressed.While AI is a powerfulmeans to discover interesting patterns and obtain predictive models,the use of these algorithms comes with a great responsibility,as an incomplete or unbalanced set of training data or an unproper interpretation of the models’outcomes could result in misleading conclusions that ultimately could become very dangerous.For these reasons,it is important to rely on expert knowledge when applying these methods.However,not every user can count on this specific expertise;non-AIexpert users could also benefit from applying these powerful algorithms to their domain problems,but they need basic guidelines to obtain themost out of AI models.The goal of this work is to present a systematic review of the literature to analyze studies whose outcomes are explainable rules and heuristics to select suitable AI algorithms given a set of input features.The systematic review follows the methodology proposed by Kitchenham and other authors in the field of software engineering.As a result,9 papers that tackle AI algorithmrecommendation through tangible and traceable rules and heuristics were collected.The reduced number of retrieved papers suggests a lack of reporting explicit rules and heuristics when testing the suitability and performance of AI algorithms.

Multimodal Machine Learning Based Crop Recommendation and Yield Prediction Model

Agriculture plays a vital role in the Indian economy.Crop recommen-dation for a specific region is a tedious process as it can be affected by various variables such as soil type and climatic parameters.At the same time,crop yield prediction was based on several features like area,irrigation type,temperature,etc.The recent advancements of artificial intelligence(AI)and machine learning(ML)models pave the way to design effective crop recommendation and crop pre-diction models.In this view,this paper presents a novel Multimodal Machine Learning Based Crop Recommendation and Yield Prediction(MMML-CRYP)technique.The proposed MMML-CRYP model mainly focuses on two processes namely crop recommendation and crop prediction.At the initial stage,equilibrium optimizer(EO)with kernel extreme learning machine(KELM)technique is employed for effectual recommendation of crops.Next,random forest(RF)tech-nique was executed for predicting the crop yield accurately.For reporting the improved performance of the MMML-CRYP system,a wide range of simulations were carried out and the results are investigated using benchmark dataset.Experi-mentation outcomes highlighted the significant performance of the MMML-CRYP approach on the compared approaches with maximum accuracy of 97.91%.

Recommendation system with minimized transaction data

This paper deals with the recommendation system in the so-called user-centric payment environment where users,i.e.,the payers,can make payments without providing self-information to merchants.This service maintains only the minimum purchase information such as the purchased product names,the time of purchase,the place of purchase for possible refunds or cancellations of purchases.This study aims to develop AI-based recommendation system by utilizing the minimum transaction data generated by the user-centric payment service.First,we developed a matrix-based extrapolative collaborative filtering algorithm based on open transaction data.The recommendation methodology was verified with the real transaction data.Based on the experimental results,we confirmed that the recommendation performance is satisfactory only with the minimum purchase information.

The Mobile Personalized Recommendation Model Containing Implicit Intention

Because mobile e-commerce is limited by the mobile terminal,network environment and other factors,accurate personalized recommendations become more and more important.We establish a large data intelligence platform,aiming at the characteristics of mobile e-commerce;we put forward a personalized recommendation model with implicit intention further.Firstly,create an intelligence unit with the virtual individual association set,virtual demand association set and virtual behavior associated set;Secondly,calculate the complex buying behavior prediction engine;Finally,give the predictive value of complex buying behavior.This method takes full account of factors such as hidden wishes perturbations that affect the predict of complex buying behavior,which to some extent solve a long-span composite purchasing behavior prediction.It shows that this method improves the purchasing behavior prediction accuracy effectively through experiments.

Adaptive Music Recommendation: Applying Machine Learning Algorithms Using Low Computing Device

In the digital music landscape, the accuracy and response speed of music recommendation systems (MRS) are crucial for user experience optimization. Traditional MRS often relies on the use of high-performance servers for large-scale training to produce recommendation results, which may result in the inability to achieve music recommendation in some areas due to substandard hardware conditions. This study evaluates the adaptability of four popular machine learning algorithms (K-means clustering, fuzzy C-means (FCM) clustering, hierarchical clustering, and self-organizing map (SOM)) on low-computing servers. Our comparative analysis highlights that while K-means and FCM are robust in high-performance settings, they underperform in low-power scenarios where SOM excels, delivering fast and reliable recommendations with minimal computational overhead. This research addresses a gap in the literature by providing a detailed comparative analysis of MRS algorithms, offering practical insights for implementing adaptive MRS in technologically diverse environments. We conclude with strategic recommendations for emerging streaming services in resource-constrained settings, emphasizing the need for scalable solutions that balance cost and performance. This study advocates an adaptive selection of recommendation algorithms to manage operational costs effectively and accommodate growth.

基于画像技术的教师研修路径智能推荐研究

教师是教育的第一资源,研修是促进教师专业发展的重要方式。在大数据、数字画像等新技术赋能下,优化教师研修路径以提升教师发展质量变得尤为重要。文章构建了多模态数据和画像技术支持的教师研修路径智能推荐模型,包括数据伴随式采集分类与预处理、教师画像生成、研修路径算法三个模块,实现教师研修特征与优质研修资源的智能匹配。在教师研修路径动态优化方面,模型通过提供基于画像的个性化导研服务、基于知识图谱的资源关联推荐、基于群体智能的群体路径发现、基于目标导向的过程评价和基于研修行为的智能预警,满足教师的个性化研修需求,为发掘研修数据潜能、促进教师智能研修模式创新提供参考。

服装个性化定制中信息技术的应用与展望

随着人们个性化需求的不断增加,服装个性化定制已成为时尚发展趋势之一。信息技术在推动服装个性化定制发展中扮演着重要的角色,可以收集和处理用户的个性化信息,并将其转化为具体的设计和生产方案。文章首先对服装产业信息技术的研究进展进行总结,阐述新一代信息技术在服装产业的应用情况;其次分析了信息技术在服装个性化定制领域的应用现状,按照定制流程分别总结信息技术在提高生产效率、降低成本和满足用户需求方面的优势及不足;最后根据目前服装个性化定制在数据共享、协同设计和柔性生产等方面的需求,从建模技术和系统平台构建两个方面对服装个性化定制发展进行展望。

基于集成改进蚁群算法的作战环推荐方法

作战环推荐是依靠优化算法从作战网络中为指挥员推荐最优的作战环,以对目标形成高质量打击。未来作战中的作战环推荐面临体系规模大、决策节奏快的特点。对此,提出了一种集成改进的蚁群算法,能够实现高效、高质的作战环推荐优化求解。首先,将作战环推荐问题转换为一种基于多仓库路径规划的数学模型。然后,针对原始蚁群算法前期收敛速度慢、算法参数对结果影响大和容易陷入局部最优的问题分别提出了3种改进策略:基于边权重信息的信息素初始化、基于差分进化的蚁群算法参数自适应优化和基于遗传算子的全局搜索能力提升,并进行了集成改进。最后,在案例分析中对集成改进蚁群算法进行了分析和对比,验证了所提算法在不需要大幅提高耗时的情况下,优化结果要优于未集成改进的蚁群算法,且相比于原始蚁群算法提升效果显著。

基于人工智能的船舶故障检测结果智能推荐系统

为全面整合船舶故障相关的各种知识,为检修人员智能推荐便于理解的故障检测结果,设计基于人工智能的船舶故障检测结果智能推荐系统。知识图谱模块依据船舶故障维修日志建立船舶故障知识图谱;实体抽取模块利用人工智能的长短时记忆网络,在船舶故障描述文本内抽取故障实体;实体识别匹配模块,利用基于实体识别的文本匹配技术,计算抽取的故障实体与知识图谱内故障实体间的匹配得分,以最高匹配得分对应的故障实体为船舶故障检测智能推荐结果。实验证明,该系统可有效构建检查故障知识图谱;该系统可有效抽取船舶故障实体,完成船舶故障检测结果智能推荐。

解构与建构:算法推荐视域下主流意识形态建设理路

在智能时代,算法推荐成为人们获取信息、构建认知的重要途径,给主流意识形态建设带来新的机遇。然而,算法推荐技术并非单纯的工具技术,在实际应用过程中还扮演着意识形态的角色,产生技术异化,引发主流意识形态认同危机、安全隐患等方面的解构风险。规避算法推荐技术的意识形态风险,应从主体、价值、技术、载体、制度等层面,构建以公众素养为核心、以价值引领为根本、以技术变革为抓手、以载体创新为驱动、以制度建设为保障的立体化主流意识形态安全防线。

情感计算在服装智能研发中的应用

将人工智能、3D等技术应用于服装,有助于精准把握客户需求,但目前设计师依旧无法获取用户的隐性需求。在此背景下,情感计算成为推动服装智能研发的重要力量。通过介绍情感计算在服装智能研发中的应用场景和相关技术,分析其未来研究前景。研究认为,尽管情感计算在服装智能研发中已经展现出潜力,但相关技术仍需进一步完善以满足不断增长的个性化需求,为用户带来更优质丰富的体验。