Increasing Profitability: Voice-Based Browsing to Recommendation System Web Services

2005 ◽  
pp. 279-285
Author(s):  
Hendrik T. Macedo ◽  
Jacques Robin
Keyword(s):  
Web Services ◽  
Recommendation System

Novel recommendation system based on long‐term composition for adaptive web services

2020 ◽  
Vol 36 (3) ◽  
pp. 1063-1077
Author(s):  
P Kirubanantham ◽  
G Vijayakumar
Keyword(s):  
Web Services ◽  
Recommendation System

scholarly journals Collaborative Filtering Method of Web Service Recommendation Based on Location Aware and Personalized

CONVERTER ◽  
2021 ◽  
pp. 583-589
Author(s):  
Li Ziman
Keyword(s):  
Web Services ◽  
Web Service ◽  
Autonomous System ◽  
Recommendation System ◽  
Service Recommendation ◽  
Filtering Method ◽  
Location Aware ◽  
The Face ◽  
Location Clustering ◽  
User Location

With the rapid growth of the number of Web services, it is necessary to build an efficient web service recommendation system in the face of massive web services. In order to recommend high-quality services to users, the key problem is how to obtain the s value of Web services. This paper proposes a collaborative web service recommendation method based on location clustering. Firstly, users are clustered according to the autonomous system by using the correlation between QoS and user location. According to the clustering results, the system fills in the vacancy Qos value; Then, the vacancy Qos value is filled in in advance and the similarity between active users and each user is calculated. Based on this, to P-K algorithm is used to obtain the most similar Qos value to predict the unknown service for active users to complete the recommendation. The method proposed in this paper can effectively solve the problem of data sparsity and cold start of Web services. At the same time, a better balance between accuracy and coverage is obtained.

Online Job and Candidate Recommendation System

2019 ◽  
Vol 1 (2) ◽  
pp. 84-89
Author(s):  
Punitavathi D ◽  
Shinu V ◽  
Siva Kumar S ◽  
Vidhya Priya S P
Keyword(s):  
Web Services ◽  
Web Application ◽  
Recommendation System ◽  
Data Transfer ◽  
Data Retrieval ◽  
Third Party ◽  
Text Field ◽  
Efficient Data ◽  
Privacy Breaches

To develop an enhanced web application, using web services for both online job and candidate recommendation system. By using Professional Social Recommender (PSR) and Text field filtering the recommendation of jobs and candidates will be classified. Three tier architecture designs have been implemented for efficient data retrieval and data transfer. They are Job seeker interface, Candidate recruitment interface and Recommendation database will be the architecture taken for developing this application. The primary architecture will be the job seeker interface, in followed with candidate recruitment interface and Recommendation database will be interconnected. The professional social recommender will works as a third party agent and the agent will retrieves all the recommended job and candidate profiles. A panel will be designed for displaying the recommended job and candidate details. All the displayed jobs will be more relevant to the user’s profile. The generated user and candidate profile will be encrypted in order to overcome the privacy breaches.

scholarly journals Educational Recommender System based on Learner’s Annotative Activity

2021 ◽  
Vol 16 (10) ◽  
pp. 108
Author(s):  
Omar Mazhoud ◽  
Anis Kalboussi ◽  
Ahmed Hadj Kacem
Keyword(s):  
Web Services ◽  
Recommender System ◽  
Service Discovery ◽  
Recommendation System ◽  
Information Overload ◽  
Empirical Studies ◽  
Learning Activity ◽  
Learning Context ◽  
Online Learners ◽  
Student’S T

In recent years, Educational Recommender Systems (ERSs) have attracted great attention as a solution towards addressing the problem of information overload in e-learning environments and providing relevant recommendations to online learners. These systems play a key role in helping learners to find educational resources relevant and pertinent to their profiles and context. So, it is necessary to identify information that helps learner’s profile definition and in identifying requests and interests. In this context, we suggest to take advantage of the annotation activity used usually in the learning context for different purposes and which may reflect certain learner’s characteristics useful as input data for the recommendation process. Therefore, we propose an educational recommender system of web services based on learner’s annotative activity to assist him in his learning activity. This process of recommendation is founded on two preparatory phases: the phase of modelling learner’s personality profile through analysis of annotation digital traces in learning environment realized through a profile constructor module and the phase of discovery of web services which can meet the goals of annotations made by learner via the web service discovery module. The evaluation of the developed annotation based recommendation system through empirical studies realized on groups of learners based on the Student’s t-test showed significant results.

Analysis on Research Paper Publication Recommendation System with Composition of Papers and Conferences Matrices

2020 ◽  
pp. 120-127
Author(s):  
Htay Htay Win ◽  
Aye Thida Myint ◽  
Mi Cho Cho
Keyword(s):  
Social Network ◽  
Dimensionality Reduction ◽  
Correspondence Analysis ◽  
Recommendation System ◽  
Research Paper ◽  
Topic Modelling ◽  
Research Papers ◽  
The Right ◽  
Publication Recommendation

For years, achievements and discoveries made by researcher are made aware through research papers published in appropriate journals or conferences. Many a time, established s researcher and mainly new user are caught up in the predicament of choosing an appropriate conference to get their work all the time. Every scienti?c conference and journal is inclined towards a particular ?eld of research and there is a extensive group of them for any particular ?eld. Choosing an appropriate venue is needed as it helps in reaching out to the right listener and also to further one’s chance of getting their paper published. In this work, we address the problem of recommending appropriate conferences to the authors to increase their chances of receipt. We present three di?erent approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modelling. In all these approaches, we apply Correspondence Analysis (CA) to obtain appropriate relationships between the entities in question, such as conferences and papers. Our models show hopeful results when compared with existing methods such as content-based ?ltering, collaborative ?ltering and hybrid ?ltering.

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