Weighted Forwarding in Graph Convolution Networks for Recommendation Information Systems

Recommendation Information Systems(RIS)are pivotal in helping users in swiftly locating desired content from the vast amount of information available on the Internet.Graph Convolution Network(GCN)algorithms have been employed to implement the RIS efficiently.However,the GCN algorithm faces limitations in terms of performance enhancement owing to the due to the embedding value-vanishing problem that occurs during the learning process.To address this issue,we propose a Weighted Forwarding method using the GCN(WF-GCN)algorithm.The proposed method involves multiplying the embedding results with different weights for each hop layer during graph learning.By applying the WF-GCN algorithm,which adjusts weights for each hop layer before forwarding to the next,nodes with many neighbors achieve higher embedding values.This approach facilitates the learning of more hop layers within the GCN framework.The efficacy of the WF-GCN was demonstrated through its application to various datasets.In the MovieLens dataset,the implementation of WF-GCN in LightGCN resulted in significant performance improvements,with recall and NDCG increasing by up to+163.64%and+132.04%,respectively.Similarly,in the Last.FM dataset,LightGCN using WF-GCN enhanced with WF-GCN showed substantial improvements,with the recall and NDCG metrics rising by up to+174.40%and+169.95%,respectively.Furthermore,the application of WF-GCN to Self-supervised Graph Learning(SGL)and Simple Graph Contrastive Learning(SimGCL)also demonstrated notable enhancements in both recall and NDCG across these datasets.

Intelligent 6G Wireless Network with Multi-Dimensional Information Perception

Intelligence and perception are two operative technologies in 6G scenarios.The intelligent wireless network and information perception require a deep fusion of artificial intelligence(AI)and wireless communications in 6G systems.Therefore,fusion is becoming a typical feature and key challenge of 6G wireless communication systems.In this paper,we focus on the critical issues and propose three application scenarios in 6G wireless systems.Specifically,we first discuss the fusion of AI and 6G networks for the enhancement of 5G-advanced technology and future wireless communication systems.Then,we introduce the wireless AI technology architecture with 6G multidimensional information perception,which includes the physical layer technology of multi-dimensional feature information perception,full spectrum fusion technology,and intelligent wireless resource management.The discussion of key technologies for intelligent 6G wireless network networks is expected to provide a guideline for future research.

Short Video Recommendation Algorithm Incorporating Temporal Contextual Information and User Context

With the popularity of 5G and the rapid development of mobile terminals,an endless stream of short video software exists.Browsing short-form mobile video in fragmented time has become the mainstream of user’s life.Hence,designing an efficient short video recommendation method has become important for major network platforms to attract users and satisfy their requirements.Nevertheless,the explosive growth of data leads to the low efficiency of the algorithm,which fails to distill users’points of interest on one hand effectively.On the other hand,integrating user preferences and the content of items urgently intensify the requirements for platform recommendation.In this paper,we propose a collaborative filtering algorithm,integrating time context information and user context,which pours attention into expanding and discovering user interest.In the first place,we introduce the temporal context information into the typical collaborative filtering algorithm,and leverage the popularity penalty function to weight the similarity between recommended short videos and the historical short videos.There remains one more point.We also introduce the user situation into the traditional collaborative filtering recommendation algorithm,considering the context information of users in the generation recommendation stage,and weight the recommended short-formvideos of candidates.At last,a diverse approach is used to generate a Top-K recommendation list for users.And through a case study,we illustrate the accuracy and diversity of the proposed method.

MINDTL： Multiple Incomplete Domains Transfer Learning for Information Recommendation

Collaborative filtering is the most popular and successful information recommendation technique. However, it can suffer from data sparsity issue in cases where the systems do not have sufficient domain information. Transfer learning, which enables information to be transferred from source domains to target domain, presents an unprecedented opportunity to alleviate this issue. A few recent works focus on transferring user-item rating information from a dense domain to a sparse target domain, while almost all methods need that each rating matrix in source domain to be extracted should be complete. To address this issue, in this paper we propose a novel multiple incomplete domains transfer learning model for cross-domain collaborative filtering. The transfer learning process consists of two steps. First, the user-item ratings information in incomplete source domains are compressed into multiple informative compact cluster-level matrixes, which are referred as codebooks. Second, we reconstruct the target matrix based on the codebooks. Specifically, for the purpose of maximizing the knowledge transfer, we design a new algorithm to learn the rating knowledge efficiently from multiple incomplete domains. Extensive experiments on real datasets demonstrate that our proposed approach significantly outperforms existing methods.

A GIS-Based Database Management Package for Fertilizer Recommendations in Paddy Fields

Over-use of fertilizer in paddy fields could lead to agro-environmental pollution. Therefore, the Paddy Fertilizer Recommendation System (PFRS) application package was designed to aid in the dissemination of fertilizer recommendations for paddy fields. PFRS utilized geographical information system (GIS) ActiveX Controls, enabling the user to select a location of interest linked to a spatial database of paddy field soil characteristics. The application package also incorporated different soil fertilizer recommendation methods, forming a relational database. The application's structure consisted primarily of building database queries using Standard Query Language (SQL) constructed during run-time, based on user provided spatial parameters of a selected location, the type of soil desired and paddy production criteria. PFRS, which was comprised of five modules including: File, View, Edit, Layer and Fertilizer/Model, provided the user with map-based fertilizer recommendations based on selected soil nutrient P and K map layers as well as N characteristics and land use maps.

Advantages and Disadvantages of Fragmented Learning and Recommendations

The development of information technology has changed people's learning methods. Fragmented learning,as an informal learning method,has become an important way to accept new knowledge and learn new technologies. Through analyzing the connotation,characteristics,and advantages and disadvantages of fragmented learning,this paper came up with reasonable recommendations for fragmented learning. To truly become systematic and holistic knowledge,fragmented knowledge must be explored,understood,integrated and internalized. This paper is expected to play an important guiding role in building a lifelong learning society.

Design and Implementation of Book Recommendation Management System Based on Improved Apriori Algorithm

The traditional Apriori applied in books management system causes slow system operation due to frequent scanning of database and excessive quantity of candidate item-sets, so an information recommendation book management system based on improved Apriori data mining algorithm is designed, in which the C/S (client/server) architecture and B/S (browser/server) architecture are integrated, so as to open the book information to library staff and borrowers. The related information data of the borrowers and books can be extracted from books lending database by the data preprocessing sub-module in the system function module. After the data is cleaned, converted and integrated, the association rule mining sub-module is used to mine the strong association rules with support degree greater than minimum support degree threshold and confidence coefficient greater than minimum confidence coefficient threshold according to the processed data and by means of the improved Apriori data mining algorithm to generate association rule database. The association matching is performed by the personalized recommendation sub-module according to the borrower and his selected books in the association rule database. The book information associated with the books read by borrower is recommended to him to realize personalized recommendation of the book information. The experimental results show that the system can effectively recommend book related information, and its CPU occupation rate is only 6.47% under the condition that 50 clients are running it at the same time. Anyway, it has good performance.

A Survey of Online Course Recommendation Techniques

With the development of information technology, online learning has gradually become an indispensable way of knowledge acquisition. However, with the increasing amount of data information, it is increasingly difficult for people to find appropriate learning materials from a large number of educational resources. The recommender system has been widely used in various Internet applications due to its high efficiency in filtering information, helping users to quickly find personalized resources from thousands of information, thereby alleviating the problem of information overload. In addition, due to its great use value, many new researches have been proposed in the field of recommender systems in recent years, but there are not many works on online course recommendation at present. Therefore, this paper aims to sort out the existing cutting-edge recommendation algorithms and the work related to online course recommendation, so as to provide a comprehensive overview of the online course recommender system. Specifically, we will first introduce the main technologies and representative work used in the online course recommender system, explain the advantages and disadvantages of various technologies, and finally discuss the future research direction of the online course recommender system.

An Entropy-Based Model for Recommendation of Taxis’Cruising Route

Cruising route recommendation based on trajectory mining can improve taxi-drivers'income and reduce energy consumption.However,existing methods mostly recommend pick-up points for taxis only.Moreover,their performance is not good enough since there lacks a good evaluation model for the pick-up points.Therefore,we propose an entropy-based model for recommendation of taxis'cruising route.Firstly,we select more positional attributes from historical pick-up points in order to obtain accurate spatial-temporal features.Secondly,the information entropy of spatial-temporal features is integrated in the evaluation model.Then it is applied for getting the next pick-up points and further recommending a series of successive points.These points are constructed a cruising route for taxi-drivers.Experimental results show that our method is able to obviously improve the recommendation accuracy of pick-up points,and help taxi-drivers make profitable benefits more than before.

CSRecommender: A Cloud Service Searching and Recommendation System

Cloud Computing and in particular cloud services have become widely used in both the technology and business industries. Despite this significant use, very little research or commercial solutions exist that focus on the discovery of cloud services. This paper introduces CSRecommender—a search engine and recommender system specifically designed for the discovery of these services. To engineer the system to scale, we also describe the implementation of a Cloud Service Identifier which enables the system to crawl the Internet without human involvement. Finally, we examine the effectiveness and usefulness of the system using real-world use cases and users.

A User-Recommendation Method Based on Social Media

User-analysis techniques are mainly used to recommend friends and information. This paper discusses the data characteristics of microblog users and describes a multidimensional user rec- ommendation algorithm that takes into account microblog length, relativity between microblog and users, and familiarity between users. The experimental results show that this multidi- mensional algorithm is more accurate than a traditional recom- mendation algorithm.

Meta-Path-Based Deep Representation Learning for Personalized Point of Interest Recommendation

With the wide application of location-based social networks(LBSNs),personalized point of interest(POI)recommendation becomes popular,especially in the commercial field.Unfortunately,it is challenging to accurately recommend POIs to users because the user-POI matrix is extremely sparse.In addition,a user's check-in activities are affected by many influential factors.However,most of existing studies capture only few influential factors.It is hard for them to be extended to incorporate other heterogeneous information in a unified way.To address these problems,we propose a meta-path-based deep representation learning(MPDRL)model for personalized POI recommendation.In this model,we design eight types of meta-paths to fully utilize the rich heterogeneous information in LBSNs for the representations of users and POIs,and deeply mine the correlations between users and POIs.To further improve the recommendation performance,we design an attention-based long short-term memory(LSTM)network to learn the importance of different influential factors on a user's specific check-in activity.To verify the effectiveness of our proposed method,we conduct extensive experiments on a real-world dataset,Foursquare.Experimental results show that the MPDRL model improves at least 16.97%and 23.55%over all comparison methods in terms of the metric Precision@N(Pre@N)and Recall@N(Rec@N)respectively.

Incorporating User’s Preferences into Scholarly Publications Recommendation

Over the years, there has been increasing growth in academic digital libraries. It has therefore become overwhelming for researchers to determine important research materials. In most existing research works that consider scholarly paper recommendation, the researcher’s preference is left out. In this paper, therefore, Frequent Pattern (FP) Growth Algorithm is employed on potential papers generated from the researcher’s preferences to create a list of ranked papers based on citation features. The purpose is to provide a recommender system that is user oriented. A walk through algorithm is implemented to generate all possible frequent patterns from the FP-tree after which an output of ordered recommended papers combining subjective and objective factors of the researchers is produced. Experimental results with a scholarly paper recommendation dataset show that the proposed method is very promising, as it outperforms recommendation baselines as measured with nDCG and MRR.

MOOCDR-VSI:一种融合视频字幕信息的MOOC资源动态推荐模型

学习者在面对浩如烟海的在线学习课程资源时往往存在“信息过载”和“信息迷航”等问题,基于学习者的学习记录,向学习者推荐与其知识偏好和学习需求相符的MOOC资源变得愈加重要.针对现有MOOC推荐方法没有充分利用MOOC视频中所蕴含的隐式信息,容易形成“蚕茧效应”以及难以捕获学习者动态变化的学习需求和兴趣等问题,提出了一种融合视频字幕信息的动态MOOC推荐模型MOOCDR-VSI,模型以BERT为编码器,通过融入多头注意力机制深度挖掘MOOC视频字幕文本的语义信息,采用基于LSTM架构的网络动态捕捉学习者随着学习不断变化的知识偏好状态,引入注意力机制挖掘MOOC视频之间的个性信息和共性信息,最后结合学习者的知识偏好状态推荐出召回概率Top N的MOOC视频.实验在真实学习场景下收集的数据集MOOCCube分析了MOOCDR-VSI的性能,结果表明,提出的模型在HR@5,HR@10,NDCG@5,NDCG@10,NDCG@20评价指标上比目前最优方法分别提高了2.35%,2.79%,0.69%,2.2%,3.32%.

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。

AI人机交互用户个性化推荐中隐私信息披露影响因素研究

[目的/意义]以ChatGPT为代表的生成式人工智能的发展为人机交互带来颠覆性影响,也为数据安全与个人信息保护带来更大的冲击与挑战。对AI人机交互用户个性化推荐中隐私信息披露及个人信息保护问题的研究将具有重要的实践价值与意义。[方法/过程]文章通过对15名AI人机交互平台用户进行深度访谈,运用扎根理论研究方法进行编码分析,识别AI人机交互用户个性化推荐中隐私信息披露所涉及的个人信息类型和敏感信息类型,并对用户的隐私意识和影响用户隐私信息披露意愿的客观因素进行分析,进而提出加强隐私信息披露风险规制及个人信息安全保障的对策建议。[结果/结论]基于编码结果,将AI人机交互用户个性化推荐中隐私信息披露的影响因素归纳为用户因素、平台因素、社会环境因素、隐私权衡因素4个维度,以此建构理论分析模型,并作为强化AI人机交互用户个性化推荐中隐私信息披露风险规制及个人信息保护的重要指引。

迈向信任的算法个性化推荐——“一键关闭”的法律反思

个性化推荐主要面临威胁人类主体自主性、“信息茧房”与“回音室”效应、大数据“杀熟”方面的质疑。“一键关闭”功能因能够实现隐私个人控制、促进信息自由流通和预防价格歧视而被推崇。然而,用户往往缺少隐私自治能力,个性化推荐造成群体极化之前首先具有议程凝聚、促进交往的价值,以关闭个性化推荐来解决价格歧视存在目标和手段的错位。“一键关闭”不仅难以符合规制预期,其自身还存在可操作度低和所承载公共价值模糊的弊端。个性化推荐的治理应以算法向善为方向,通过打造以信任为中心的透明可解释、用户交互友好的算法推荐,发挥个性化推荐的公共价值,增进社会福利。

基于局部-邻域图信息与注意力机制的会话推荐

针对基于匿名用户的会话推荐忽略了不同会话之间可能存在的协作信息,以及未考虑所预测的目标项与历史行为的相关性问题,提出一种基于局部-邻域图信息与注意力机制的会话推荐模型(SR-LNG-AM)。从当前会话和邻域会话构建的图结构中分别学习两种类型的项目转换信息,将其融合得到项目嵌入。使用软注意力机制生成全局嵌入,使用目标注意力机制针对不同的目标项自适应生成不同的目标嵌入。结合局部嵌入,进行预测。在两个真实数据集上与多个基线方法进行实验对比,实验指标均有提高,验证了该方法的有效性。

基于多嵌入融合的top-N推荐

异构信息网络(Heterogeneous Information Network, HIN)凭借其丰富的语义信息和结构信息被广泛应用于推荐系统中,虽然取得了很好的推荐效果,但较少考虑局部特征放大、信息交互和多嵌入聚合等问题。针对这些问题,提出了一种新的用于top-N推荐的多嵌入融合推荐(Multi-embedding Fusion Recommendation, MFRec)模型。首先,该模型在用户和项目学习分支中都采用对象上下文表示网络,充分利用上下文信息以放大局部特征,增强相邻节点的交互性;其次,将空洞卷积和空间金字塔池化引入元路径学习分支,以便获取多尺度信息并增强元路径的节点表示;然后,采用多嵌入融合模块以便更好地进行用户、项目以及元路径的嵌入融合,细粒度地进行多嵌入之间的交互学习,并强调了各特征的不同重要性程度;最后,在两个公共推荐系统数据集上进行了实验,结果表明所提模型MFRec优于现有的其他top-N推荐系统模型。

融合跨平台用户偏好与异质信息网络的推荐算法研究

[目的/意义]本文基于跨平台用户的异构大数据,提出一种融合跨平台用户偏好与异质信息网络的推荐算法(CPHAR),对于缓解个性化推荐的稀疏性和冷启动问题具有重要意义。[方法/过程]首先,根据跨平台用户信息构建核心兴趣朋友圈,使用卷积神经网络和自注意力机制捕捉用户在源平台和目标平台中的信息偏好特征;其次,根据核心兴趣网络以及推荐项目之间的关系构建异质信息网络,使用异质图注意力网络模型进行特征聚合;最后,将以上特征嵌入改进后的矩阵分解模型,计算推荐得分。[结果/结论]模型在自主构建的4个跨平台数据集中均表现出优越的性能,本文不仅弥补了推荐领域中跨平台多属性和细粒度数据集的空缺,而且通过引入跨平台特征进一步完善了推荐系统相关的理论与方法体系。