An intelligent web service group-based recommendation system for long-term composition

2021 ◽  
Author(s):  
P. Kirubanantham ◽  
S. M. Udhaya Sankar ◽  
C. Amuthadevi ◽  
M. Baskar ◽  
M. Senthil Raja ◽  
...  
Keyword(s):  
Web Service ◽  
Recommendation System ◽  
Service Group

Operational Delivery of Customized Earth Observation Data Using Web Coverage Service

2011 ◽  
pp. 385-400
Author(s):  
Wenli Yang
Keyword(s):  
Web Service ◽  
Earth Observation ◽  
Observation Data ◽  
Data Systems ◽  
On Demand ◽  
Promising Solution ◽  
Open Geospatial Consortium ◽  
Earth Observation Data ◽  
Operational Data

Global long term Earth Observation (EO) provides valuable information about the land, ocean, and atmosphere of the Earth. EO data are often archived in specialized data systems managed by the data collector’s system. For the data to be fully utilized, one of the most important aspects is to adopt technologies that will enable users to easily find and obtain needed data in a form that can be readily used with little or no manipulation. Many efforts have been made in this direction but few, if any, data providers can deliver on-demand and operational data to users in customized form. Geospatial Web Service has been considered a promising solution to this problem. This chapter discusses the potential for operational and scalable delivery of on-demand personalized EO data using the interoperable Web Coverage Service (WCS) developed by the Open Geospatial Consortium (OGC).

A Movie E-Shop Recommendation Model Based on Web Usage and Ontological Data

2011 ◽  
pp. 51-68
Author(s):  
Andreas Aresti ◽  
Penelope Markellou ◽  
Ioanna Mousourouli ◽  
Spiros Sirmakessis ◽  
Athanasios Tsakalidis
Keyword(s):  
Artificial Intelligence ◽  
User Modeling ◽  
Web Mining ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Model Based ◽  
Web Usage ◽  
One To One ◽  
Online Shops

Recommendation systems are special personalization tools that help users to find interesting information and services in complex online shops. Even though today’s e-commerce environments have drastically evolved and now incorporate techniques from other domains and application areas such as Web mining, semantics, artificial intelligence, user modeling, and profiling setting up a successful recommendation system is not a trivial or straightforward task. This chapter argues that by monitoring, analyzing, and understanding the behavior of customers, their demographics, opinions, preferences, and history, as well as taking into consideration the specific e-shop ontology and by applying Web mining techniques, the effectiveness of produced recommendations can be significantly improved. In this way, the e-shop may upgrade users’ interaction, increase its usability, convert users to buyers, retain current customers, and establish long-term and loyal one-to-one relationships.

An Analysis on E-Learning and Its Recommendations

2021 ◽  
pp. 166-187
Author(s):  
Lalitha T. B. ◽  
Sreeja P. S.
Keyword(s):  
Personal Growth ◽  
Recommendation System ◽  
Professional Competence ◽  
Holistic Approach ◽  
Global Village ◽  
Approach To Learning ◽  
E Learning ◽  
Minimal Effort ◽  
The Web

Education provides a predominant source of worldly knowledge around us and changes the perspective of the living society as a global village. However, education has revealed fragmentary remains in the professional competence and personal growth of the learners without the involvement of online learning. E-learning brings out a broader vision of sources to the learners available over the web with the holistic approach to learning from anywhere without cost and minimal effort. The proposed theoretical framework analyses the long-term evolution of e-learning and its effect on mankind. The various methods, technologies, and approaches of e-learning that exist in various forms were discussed exponentially according to the range of necessities among the learners. The recommendation system plays a pivotal role in referring contents and enhancing the learning environment. The education promoted to the learners through the recommendations system over their personal preferences were explored here in detail.

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 Hierarchical Context Enabled Recurrent Neural Network for Recommendation

2019 ◽  
Vol 33 ◽  
pp. 4983-4991 ◽  
Author(s):  
Kyungwoo Song ◽  
Mingi Ji ◽  
Sungrae Park ◽  
Il-Chul Moon
Keyword(s):  
Neural Network ◽  
Recommendation System ◽  
Sequential Model ◽  
User Interests ◽  
Gate Structure ◽  
Term Dependency ◽  
User History ◽  
Personal Interests ◽  
Over Time

A long user history inevitably reflects the transitions of personal interests over time. The analyses on the user history require the robust sequential model to anticipate the transitions and the decays of user interests. The user history is often modeled by various RNN structures, but the RNN structures in the recommendation system still suffer from the long-term dependency and the interest drifts. To resolve these challenges, we suggest HCRNN with three hierarchical contexts of the global, the local, and the temporary interests. This structure is designed to withhold the global long-term interest of users, to reflect the local sub-sequence interests, and to attend the temporary interests of each transition. Besides, we propose a hierarchical context-based gate structure to incorporate our interest drift assumption. As we suggest a new RNN structure, we support HCRNN with a complementary bi-channel attention structure to utilize hierarchical context. We experimented the suggested structure on the sequential recommendation tasks with CiteULike, MovieLens, and LastFM, and our model showed the best performances in the sequential recommendations.

Research on Modeling User’s Preference in the Steel E-Trading Platform

2015 ◽  
Vol 743 ◽  
pp. 687-691
Author(s):  
Ping Wu ◽  
Tao Yu ◽  
J.B. Du ◽  
G.Q. Qu ◽  
Feng Xiong
Keyword(s):  
Recommendation System ◽  
User Preferences ◽  
Cluster Method ◽  
Basic Information ◽  
Short Term

In order to meet the increasing personalized needs of users in the steel trading platform, the intelligent recommendation system has been introduced into the platform. And the users’ interests and preferences-based modeling is the key and foundation of recommendation system, and changes with the change of time. So, in this paper, the user preferences are divided into long-term and short-term firstly, then the users’ basic information vectors and cluster method are used to model users’ long-term interests and preferences, while mining and analyzing users’ operating records in the platform to model users’ the short-term. Finally, the whole interest and preference’s model of user will be built by integrating the two models.

LA-LMRBF: Online and Long-term Web Service QoS Forecasting

2019 ◽  
pp. 1-1 ◽  
Author(s):  
Pengcheng Zhang ◽  
Huiying Jin ◽  
Hai Dong ◽  
Wei Song ◽  
Liyan Wang
Keyword(s):  
Web Service

scholarly journals The effect of long-term fertilization on the 0.01 M CaCl2 extractable nutrient content of a meadow soil

2012 ◽  
pp. 73-79
Author(s):  
Emese Bertáné Szabó
Keyword(s):  
Recommendation System ◽  
Arable Land ◽  
Soil Nutrient ◽  
Nutrient Content ◽  
Limit Values ◽  
Nutrient Contents ◽  
Ammonium Lactate ◽  
Supply Capacity ◽  
Mineralizable Nitrogen

During my research, I studied the 0.01 M CaCl2 extractable NO3--N, NH4+-N, Norg, P and K contents of the soil samples originated from a long term fertilisation trial in the experimental site Hajdúböszörmény. Relationships among the soil nutrient contents, the agronomic nutrient balances of the 2009 year, and fertilization were studied. From the results of the study it was concluded as follows:– Fertilization significantly increased the CaCl2 extractable NO3--N, NH4+-N, and K contents of soil.– Norg fraction increased as a function of the increasing yield. Hence, it can be assumed that the greater the produced yield, the more the stubble and root residues remain on the arable land. These organic residues can result significant increase in the Norg content of soils.– The CaCl2 extractable P and K contents were compared with the calculated P and K limit values. According to these, the experimental soil has a good phosphorus and lower potassium supply capacity. These results are in accordance with the results of the conventional Hungarian fertilization recommendation system.– It can be stated that the 0.01 M CaCl2 is able to determine not just inorganic N forms but Norg fraction as well that characterize the easily mineralizable nitrogen reserves. The results proved that AL-P and -K (ammonium lactate acetic acid, traditional Hungarian extractant) are in good agreement with the P and K reserves, but it is important from the aspect of environmental protection and plant nutrition to measure the easily soluble and exchangeable K-, and P-contents of soil. 0.01 M CaCl2 method is recommended for this.

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