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An E-Commerce Recommender System based on content-based filtering

Wuhan University Journal of Natural Sciences 2006年第5期11卷 1091-1096页

作者： HE Weihong CAO Yi Department of Computer Science and Technology Hunan Institute of Technology Hengyang 421101 HunanChina School of Business Central South UniversityChangsha 410083 Hunan China

content-based filtering E-commerce recommender system was discussed fully in this paper. Users＇ unique features can be explored by means of vector space model firstly. Then based on the qualitative value of products informa tion, the recommender lists were obtained. Since the system can adapt to the users＇ feedback automatically, its performance were enhanced comprehensively. Finally the evaluation of the system and the experimental results were presented.

关键词： E-commerce recommender system personalized recommendation content-based filtering Vector Spatial Model(VSM)

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Effective Hybrid content-based Collaborative filtering Approach for Requirements Engineering

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Computer Systems Science & Engineering 2022年第1期40卷 113-125页

作者： Qusai Y.Shambour Abdelrahman H.Hussein Qasem M.Kharma Mosleh M.Abualhaj Faculty of Information Technology Al-Ahliyya Amman UniversityAmman19328Jordan

Requirements engineering(RE)is among the most valuable and critical processes in software *** quality of this process significantly affects the success of a software *** important step in RE is requirements elicitation,which involves collecting project-related requirements from different *** of reusable requirements are typically important sources of an increasing number of reusable software ***,the process of searching such repositories to collect valuable project-related requirements is time-consuming and difficult to perform *** systems have been widely recognized as an effective solution to such ***,this study proposes an effective hybrid content-based collaborative filtering recommendation *** proposed approach will support project stake-holders in mitigating the risk of missing requirements during requirements elicitation by identifying related requirements from software requirement *** experimental results on the RALIC dataset demonstrate that the proposed approach considerably outperforms baseline collaborative filtering-based recom-mendation methods in terms of prediction accuracy and coverage in addition to mitigating the data sparsity and cold-start item problems.

关键词： Requirements engineering recommender systems requirements elicitation collaborative filtering content-based filtering

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Personalized Service System based on Hybrid filtering for Digital Library

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Tsinghua Science and Technology 2007年第1期12卷 1-8页

作者：高凤荣邢春晓杜小勇王珊 Web and Software Technology R&D Center Research Institute of Information Technology Tsinghua University School of Information Renmin University of China

Personalized service systems are an effective way to help users obtain recommendations for unseen items, within the enormous volume of information available based on their preferences. The most commonly used personalized service system methods are collaborative filtering, content-based filtering, and hybrid filtering. Unfortunately, each method has its drawbacks. This paper proposes a new method which unified partition-based collaborative filtering and meta-information filtering. In partition-based collaborative filtering the user-item rating matrix can be partitioned into low-dimensional dense matrices using a matrix clustering algorithm. Recommendations are generated based on these low-dimensional matrices. Additionally, the very low ratings problem can be solved using meta-information filtering. The unified method is applied to a digital resource management system. The experimental results show the high efficiency and good performance of the new approach.

关键词： personalized service system content-based filtering collaborative filtering user preferences model category-based collaborative filtering meta-information filtering

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Hybrid Recommender System for Tourism based on Big Data and AI:A Conceptual Framework

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Big Data Mining and Analytics 2021年第1期4卷 47-55页

作者： Khalid AL Fararni Fouad Nafis Badraddine Aghoutane Ali Yahyaouy Jamal Riffi Abdelouahed Sabri LISAC Laboratory Department of InformaticsFaculty of Sciences Dhar El MahrazSidi Mohamed Ben Abdellah UniversityFez-Atlas 30000Morocco IA Laboratory Department of Computer ScienceFaculty of SciencesMoulay Ismail UniversityMoulay Ismail UniversityMeknes 50070Morocco

With the development of the Internet,technology,and means of communication,the production of tourist data has multiplied at all levels(hotels,restaurants,transport,heritage,tourist events,activities,etc.),especially with the development of Online Travel Agency(OTA).However,the list of possibilities offered to tourists by these Web search engines(or even specialized tourist sites)can be overwhelming and relevant results are usually drowned in informational"noise",which prevents,or at least slows down the selection *** assist tourists in trip planning and help them to find the information they are looking for,many recommender systems have been *** this article,we present an overview of the various recommendation approaches used in the field of *** this study,an architecture and a conceptual framework for tourism recommender system are proposed,based on a hybrid recommendation *** proposed system goes beyond the recommendation of a list of tourist attractions,tailored to tourist *** can be seen as a trip planner that designs a detailed program,including heterogeneous tourism resources,for a specific visit *** ultimate goal is to develop a recommender system based on big data technologies,artificial intelligence,and operational research to promote tourism in Morocco,specifically in the Daraa-Tafilalet region.

关键词： recommender systems user profiling content-based filtering collaborative filtering hybrid recommender system e-tourism trip planning

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A Combined filtering Approach for Chinese Papers and Its Empirical Results

A Combined Filtering Approach for Chinese Papers and Its Emp...

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2006年国际电子、工程及科学领域的分布式计算和应用学术研讨会

作者： Lijun Bai,Yujia Ge,Changbing Jiang College of Computer and Information Engineering,Zhejiang Gongshang University Hangzhou,Zhejiang 310035,China

With the explosive growth of information and the popularization of the Internet,it is necessary to have an efficient approach to facilitate paper searching for *** existing general purpose search engines cannot satisfy the needs of users with specific background and objective,Information filtering(IF) could be seen as a solution to solve this *** paper examines two kinds of information filtering approaches:content-based filtering and collaborative *** each of them has its limitations and filtering results are then *** to make up their weaknesses complementarily,a new combined approach of information filtering is *** paper also reports the experiments to compare the filtering results of these three approaches on Chinese technical *** experiment results show that the combined filtering approach can provide better recommendation accuracy than content-based filtering or collaborative filtering.

关键词： Information filtering content-based filtering Collaborative filtering User Profile and filtering System

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改进的个性化信息推荐系统的设计与实现

改进的个性化信息推荐系统的设计与实现

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2005年全国理论计算机科学学术年会

作者：张玉连张波张敏燕山大学信息科学与工程学院燕山大学信息科学与工程学院燕山大学信息科学与工程学院

＜正＞ 1 引言当前,搜索引擎正经历着从“数量累积阶段”向“质量精炼阶段”的变革。随着万维网上的信息数量呈指数级增长,大量信息垃圾也混杂其中,这样从搜索引擎返回的检索结果在数量上远远超出了用户的吸收和使用能力,让人感到束手... 详细信息

＜正＞ 1 引言当前,搜索引擎正经历着从“数量累积阶段”向“质量精炼阶段”的变革。随着万维网上的信息数量呈指数级增长,大量信息垃圾也混杂其中,这样从搜索引擎返回的检索结果在数量上远远超出了用户的吸收和使用能力,让人感到束手无策。这也就是所谓的“信息过载”现象。信息过滤技术就是在这样的背景下应运而生。信息过滤技术的目的是让搜索引擎能够更加深入,更加细致地参与到用户的整个检索过程中,帮助用户在浩如烟海的信息中找到高质量的和需求真正相关的资料。

关键词： Informational recommendation content-based filtering Collaborative filtering

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