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4th International Conference on Advanced Aspects of Software Engineering (ICAASE)

作者： Zitouni, Hanane Bouchelik, Khadidja Saidi, Ramla Chekkai, Nassira Univ Constantine 2 Abdelhamid Mehri Comp Sci Dept Constantine Algeria Univ Algies 3 Brahim Soltane Chaibout Algeria

ISBN: (纸本)9781665422314

The varied number of suggested meals put people who spend most of their time outside to a confusing situation. In addition, the proposed items (meals) are standard for all people, meaning that they receive the same menu, although they differ in many attributes such as age, weight, health status, eating habits and tastes. A recommendation based on nutritional value can help people to prevent or control certain diseases such as diabetes, hypertension and obesity. However, learning about people's food preferences and making recommendations that simultaneously appeal to their health expectations, which can be very difficult, is really a very difficult task. Many existing recommendation systems do not take into account the context in which the user is placed and often suffer from the cold start system problem. Through this work, we propose a new collaborative and contextual recommendation system that allows the target user to be directed towards healthy menu of tasty meals. For this, the proposed approach was named Healthy and Tasty (H&T). H&T is a recommendation system based on a multilayer architecture, where the highest layer is based on the pre-filtering algorithm, which eliminates the cold start system problem, and is based mainly on the thresholds of the compatible nutritional elements with the user profile, thus ensuring the healthy factor in the recommendations. The middle layer based on collaborative filtering algorithm works in particular to ensure the taste factor, while the deepest layer is the pertinent layer based on the post-filtering algorithm, where the recommendation will be much more adapted to the environment of the user. The proposed approach has been realized via an Android application where the experimental results are promoters.

关键词： Context-aware collaborative filtering algorithm recommendation of healthy food

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Design of Hybrid Recommendation algorithm in Online Shopping System

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Journal of New Media 2021年第4期3卷 119-128页

作者： Yingchao Wang Yuanhao Zhu Zongtian Zhang Huihuang Liu Peng Guo Hunan University of Finance and Economics ChangshaChina University Malaysia Sabah Kota KinabaluMalaysia

In order to improve user satisfaction and loyalty on e-commerce websites,recommendation algorithms are used to recommend products that may be of interest to ***,the accuracy of the recommendation algorithm is a primary *** far,there are three mainstream recommendation algorithms,content-based recommendation algorithms,collaborative filtering algorithms and hybrid recommendation ***-based recommendation algorithms and collaborative filtering algorithms have their own *** content-based recommendation algorithm has the problem of the diversity of recommended items,while the collaborative filtering algorithm has the problem of data sparsity and *** the basis of these two algorithms,the hybrid recommendation algorithm learns from each other’s strengths and combines the advantages of the two algorithms to provide people with better *** article will focus on the use of a content-based recommendation algorithm to mine the user’s existing interests,and then combine the collaborative filtering algorithm to establish a potential interest model,mix the existing and potential interests,and calculate with the candidate search content *** similarity gets the recommendation list.

关键词： Recommendation algorithm hybrid recommendation algorithm content-based recommendation algorithm collaborative filtering algorithm

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A microservice recommendation mechanism based on mobile architecture

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JOURNAL OF NETWORK AND COMPUTER APPLICATIONS 2020年 152卷 102510-000页

作者： Wang, Ru Imran, Muhammad Saleem, Kashif Northwest A&F Univ Coll Informat Engn Xianyang Shaanxi Peoples R China King Saud Univ Coll Appl Comp Sci Riyadh Saudi Arabia King Saud Univ Ctr Excellence Informat Assurance Riyadh Saudi Arabia

The microservice recommendation mechanism, as a key supporting technology for providing users with intelligent and proactive application services, is one of the important research contents in mobile social networks. In this paper, a recommendation mechanism of mobile social network service based on user trajectory, trust network and collaborative filtering algorithm is proposed. The user trajectory prediction model based on Generative Adversarial Network (GAN) is introduced into the process of service recommendation, and combined with trust degree to constitute the 3D service recommendation model of user-service-trajectory prediction. This method improves the accuracy and reliability of service recommendation and avoids the blindness and randomness of service recommendation. The simulation experiments show that the service recommendation algorithm has higher accuracy than the traditional collaborative filtering algorithm.

关键词： Microservice recommendation mechanism GAN Trajectory prediction collaborative filtering algorithm

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Trust -based collaborative filtering algorithm

Trust -based collaborative filtering algorithm

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2012 Fifth International Symposium on Computational Intelligence and Design [第五届计算智能与设计国际会议 ISCID 2012]

作者： Xiaowei Xu Fudong Wang Glorious Sun School of Business and Management. Donghua University Shanghai China Glorious Sun School of Business and Management Donghua University Shanghai China

The rapid development of internet has brought us into the era of information explosion, which brings the widespread application of the personalized recommendation system. collaborative filtering recommendation algorithm is the most widely used algorithms in the personalized recommended system, but it faces problems like the data sparsity, cold start," idler" attack. With the development of social network, many e-commerce sites, social network sites introduce the trust mechanism, which becomes the new approach to overcome the problems of the traditional collaborative filtering algorithms. This paper first introduces the collaborative filtering recommendation algorithm and the existing problems, and then summarizes the current trust based collaborative filtering algorithms. For the trust based collaborative filtering algorithms, this paper summarizes the trust expressions and metrics, and focus on analyzing the representative trust models.

关键词： Personalized recommendation collaborative filtering algorithm trust

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Visually Impaired Friendly E-commerce website 4

Visually Impaired Friendly E-commerce website

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4th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT)

作者： Chand, Mallika Mulchandani, Shreya Mirkar, Sulalah SVKMs NMIMS Deemed Univ Mukesh Patel Sch Technol Management & Engn Dept Informat Technol Mumbai Maharashtra India

ISBN: (纸本)9781728132617

A website design that allows visually impaired and blind users to access and shop without any assistance. The system gives voice instruction to users and an input is requested from the user. The user is required to hover over the entire page using the curser to access different voice messages for various fields. The user provides input using the keyboard keys thus taking the process forward. The website is developed using *** with its database stored in SQL server. The website also provides recommendation based on customer selection using collaborative filtering algorithm. It is a process of filtering information or patterns using techniques involving collaboration of data gathered from different agents and data sources [1]. In this paper effectiveness and efficiency of the developed website is also calculated based on the usability study conducted with three evaluation methods tested by 4 blind individuals in which the entire system is divided into 5 tasks.

关键词： Visually Impaired User Blind User E-commerce Website Visually Impaired Friendly Human Computer Interaction Recommendation System collaborative filtering algorithm Usability Study Information System

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Research on Personalized Book Recommendation Model for New Readers 3

Research on Personalized Book Recommendation Model for New R...

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3rd International Conference on Information Systems Engineering (ICISE)

作者： Ji, Qi Song Yannan Shi, Liu Xiang, Liu Inner Mongolia Univ Sch Comp Hohhot Peoples R China

ISBN: (纸本)9781538662595

In recent years, with the improvement of computing level, it is possible to provide personalized recommendation lists for users based on user behavior. Increasing number of books in library in university has an urgent demand for personalized book recommendation. A model is established by author using collaborative filtering algorithm, and targeting students who have never borrowed books from the library. The model generates the book recommendation lists for the target users by using their course selection records and existing borrowing data of known users. Finally, the paper compares the effects of different parameters setting in the algorithm.

关键词： collaborative filtering algorithm cold start book recommendation

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Design and implementation of knowledge sharing system based on WeChat small program 3

Design and implementation of knowledge sharing system based ...

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3rd IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Gu, Xiaoxi Yang, Lingjun Cao, Sanxing Commun Univ China Dept Informat Engn Sch Beijing Peoples R China Commun Univ China Dept New Media Inst Beijing Peoples R China

ISBN: (纸本)9781538645093

Aiming at the needs and characteristics of the development of knowledge sharing, this paper designs and realizes the solution of knowledge sharing with the rise of the sharing economy as the background and the small program as the carrier. According to functions, the knowledge sharing system can be divided into six modules: video resource module, article resource module, question answering module, credits module, auditing module and recommendation module. The system adopts the C/S (client/server) architecture as the overall architecture. The backend relies on the tencent cloud server and the platform functions was accomplished through Java, JavaScript, MySQL database and the special languages of WeChat small program: WXML and WXSS. The classical collaborative filtering algorithm is used to recommend different resources to different users. This small program meets the user's need to acquire knowledge anytime and anywhere. It effectively solves the present problems in knowledge sharing and has great significance to the further development.

关键词： Wechat small program knowledge sharing Java collaborative filtering algorithm

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Personalized Recommendation with User Categories of Different Behavior Modes——A Case in an E-Commerce Platform in China

Personalized Recommendation with User Categories of Differen...

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作者： Yongzhong Wu Xiangying Chen Xiaoli Wu School of Business Adimistration South China University of Technology School of Business Adimistrat South China University of Technology

As resources of E-commerce platform grow exponentially in recent years, how to filter and recommend the items that users are interested in from the massive resources has become an increasingly concerned topic in both academia and industry. This paper, based on the users and sales data of Taobao, the biggest Ecommerce platform in China, studies the personalized recommendation in the e-commerce scenario. With users' behavior data, this paper first identifies different user groups with different shopping behavior modes. Secondly, recommendation strategies are established for the user group with a low purchase rate. Thirdly, item-based collaborative filtering and user-based collaborative filtering are applied to user groups with different degrees of personalized requirements. The experimental results show that the recommendation algorithm based on different user categories can better adapt to the users with different shopping behavior modes.

关键词： E-commerce platform collaborative filtering algorithm Clustering analysis Personalized recommendation

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A Hybrid collaborative filtering algorithm for Patent Domain

A Hybrid Collaborative Filtering Algorithm for Patent Domain

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International Workshop on Computer Science for Environmental Engineering and EcoInformatics (CSEEE)

作者： Zhi, Liping Wang, Hengshan Shanghai Univ Sci & Technol Sch Business Shanghai Peoples R China

ISBN: (纸本)9783642226915

In this paper, we present a hybrid collaborative filtering algorithm based on ontology and item rating prediction. As items can be classified and predicted from the semantic level, this method can efficiently improve the extreme sparsity of user rating data. Experimental results indicate that the algorithmic accuracy, measured by mean absolute error (MAE) has been improved larger compared with traditional algorithms.

关键词： collaborative filtering algorithm Hybrid Patent

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CluCF: a clustering CF algorithm to address data sparsity problem

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SERVICE ORIENTED COMPUTING AND APPLICATIONS 2017年第1期11卷 33-45页

作者： Yu, Chengyuan Huang, Linpeng Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China

In QoS-based Web service recommendation, predicting Quality of Service (QoS) for users will greatly aid service selection and discovery. collaborative filtering (CF) is an effective method for Web service selection and recommendation. Data sparsity is an important challenges for CF algorithms. Although model-based algorithms can address the data sparsity problem, those models are often time-consuming to build and update. Thus, these CF algorithms aren't fit for highly dynamic and large-scale environments, such as Web service recommendation systems. In order to overcome this drawback, this paper proposes a novel approach CluCF, which employs user clusters and service clusters to address the data sparsity problem and classifies the new user (the new service) by location factor to lower the time complexity of updating clusters. Additionally, in order to improve the prediction accuracy, CluCF employs time factor. Time-aware user-service matrix Mu;s(tk, d) is introduced, and the time-aware similarity measurement and time-aware QoS prediction are employed in this paper. Since the QoS performance of Web services is highly related to invocation time due to some time-varying factors (e.g., service status, network condition), time-aware similarity measurement and time-aware QoS prediction are more trustworthy than traditional similarity measurement and QoS prediction, respectively. Since similarity measurement and QoS prediction are two key steps of neighborhood-based CF, time-aware CF will be more accurate than traditional CF. Moreover, our approach systematically combines user-based and item-based methods and employs influence weights to balance these two predicted values, automatically. To validate our algorithm, this paper conducts a series of large-scale experiments based on a real-world Web service QoS dataset. Experimental results show that our approach is capable of alleviating the data sparsity problem.

关键词： Web service QoS prediction Time-aware collaborative filtering algorithm

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