Case-Based Reasoning and Agent Based Job Offer Recommender System

2018 ◽  
pp. 21-33 ◽  
Author(s):  
Alfonso González-Briones ◽  
Alberto Rivas ◽  
Pablo Chamoso ◽  
Roberto Casado-Vara ◽  
Juan Manuel Corchado
Keyword(s):  
Recommender System ◽  
Case Based Reasoning ◽  
Agent Based ◽  
Case Based

An agent-based supplier selection framework

2015 ◽  
Vol 8 (2/3) ◽  
pp. 180-205 ◽  
Author(s):  
Alireza Jahani ◽  
Masrah Azrifah Azmi Murad ◽  
Md. Nasir bin Sulaiman ◽  
Mohd. Hasan Selamat
Keyword(s):  
Supply Chain ◽  
Supplier Selection ◽  
Information Overload ◽  
Case Based Reasoning ◽  
Multi Agent Systems ◽  
Content Type ◽  
Agent Based ◽  
Common Framework ◽  
Multi Agent ◽  
Case Based

Purpose – The purpose of this paper is to propose an approach that integrates three complementary perspectives, multi-agent systems, fuzzy logic and case-based reasoning. Unsatisfied customers, information overload and high uncertainty are the main challenges that are faced by today’s supply chains. In addition, a few existing agent-based approaches are tied to real-world supply chain functions like supplier selection. These approaches are static and do not adequately take the qualitative and quantitative factors into consideration. Therefore, an agent-based framework is needed to address these issues. Design/methodology/approach – The proposed approach integrates three complementary perspectives, multi-agent systems, fuzzy logic and case-based reasoning, as a common framework. These perspectives were rarely used together as a common framework in previous studies. Furthermore, an exploratory case study in an office furniture company is undertaken to illustrate the value of the framework. Findings – The proposed agent-based framework evaluates supply offers based on customers’ preferences, recommends alternative products in the case of stock-out and provides a collaborative environment among agents who represent different supply chain entities. The proposed fuzzy case-based reasoning (F-CBR) approach reduces the information overload by organizing them into the relevant cases that causes less overall search between cases. In addition, its fuzzy aspect addresses the high uncertainty of supply chains, especially when there are different customers’ orders with different preferences. Research limitations/implications – The present study does not include the functions of inventory management and negotiation between agents. Furthermore, only the case description and case retrieval phases of the case-based reasoning approach are investigated, and the remaining phases like case retaining, case reusing and case revising are not included in the scope of this paper. Originality/value – This framework balances the interests of different supply chain structural elements where each of them is represented by a specific agent for better collaboration, decision-making and problem-solving in a multi-agent environment. In addition, the supplier selection and order gathering mechanisms are developed based on customers’ orders.

scholarly journals Recommender System in Tourism Using Case based Reasoning Approach

2017 ◽  
Vol 9 (5) ◽  
pp. 34-43 ◽  
Author(s):  
Tamir Anteneh Alemu ◽  
◽  
Alemu Kumilachew Tegegne ◽  
Adane Nega Tarekegn
Keyword(s):  
Recommender System ◽  
Case Based Reasoning ◽  
Case Based

Hybrid Intelligence Agents Architecture Design for Product Return Administration

2011 ◽  
Vol 403-408 ◽  
pp. 3339-3343
Author(s):  
Chen Shu Wang
Keyword(s):  
Intelligent Agents ◽  
Case Based Reasoning ◽  
End User ◽  
Improve Performance ◽  
Information Transparency ◽  
Agent Based ◽  
Intelligent Algorithms ◽  
Product Return ◽  
Hybrid Intelligence ◽  
Case Based

Return is a critical but controversial issue. To deal with such a vague return problem, business must improve information transparency about end users’ return activities. This research proposed an agent-based architecture for return administration. The intelligent return administration expert system (iRAES) architecture consists of two KDD mechanisms and two intelligent agents that can predict the possibility of the end user will return the product (via return diagnosis agent, RDA) and provide return centre staff with recommendations for return administration (via return recommender agent, RRA). iRAES is implemented successfully and adopts hybrid artificial intelligent algorithms, including the following: data mining is employed to implement the RDA agent, and case-based reasoning is adopted to design the RRA agent. A demonstrated 3C-iShop scenario is presented to illustrate the feasibility and efficiency of iRAES architecture. As the experiment results show, iRAES can decrease the 70% effort for return administration evaluation and improve performance with return administration suggestions by 37%. Therefore, return administration and the knowledge management about product return can be accelerated via iRAES.

COBRAS

2011 ◽  
pp. 184-202
Author(s):  
Hager Karoui
Keyword(s):  
Recommender System ◽  
Relevance Feedback ◽  
Software Agents ◽  
Case Based Reasoning ◽  
User Evaluation ◽  
Related Data ◽  
Document Indexing ◽  
Single Person ◽  
Case Based ◽  
Interesting People

In this chapter, the authors propose a case-based reasoning recommender system called COBRAS: a Peer-to-Peer (P2P) bibliographical reference recommender system. COBRAS’s task is to find relevant documents and interesting people related to the interests and preferences of a single person belonging to a like-minded group in an implicit and an intelligent way. Each user manages their own bibliographical database in isolation from others. Target users use a common vocabulary for document indexing but may interpret the indexing vocabulary differently from others. Software agents are used to ensure indirect cooperation between users. A P2P architecture is used to allow users to control their data sharing scheme with others and to ensure their autonomy and privacy. The system associates a software assistant agent with each user. Agents are attributed three main skills: a) detecting the associated user’s hot topics, b) selecting a subset of peer agents that are likely to provide relevant recommendations, and c) recommending both documents and other agents in response to a recommendation request sent by a peer agent. The last two skills are handled by implementing two inter-related data-driven case-based reasoning systems. The basic idea underlying the document recommendation process is to map hot topics sent by an agent to local topics. Documents indexed by mapped topics are then recommended to the requesting agent. This agent will provide later, a relevance feedback computed after the user evaluation of the received recommendations. Provided feedbacks are used to learn to associate a community of peer agents to each local hot topic. An experimental study involving one hundred software agents using real bibliographical data is described. The Obtained results demonstrate the validity of the proposed approach.

scholarly journals A Cold Start Context-Aware Recommender System for Tour Planning Using Artificial Neural Network and Case Based Reasoning

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Zahra Bahramian ◽  
Rahim Ali Abbaspour ◽  
Christophe Claramunt
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Recommender System ◽  
User Satisfaction ◽  
Cold Start ◽  
Case Based Reasoning ◽  
Specific Information ◽  
Context Aware ◽  
Artificial Neural ◽  
Case Based

Nowadays, large amounts of tourism information and services are available over the Web. This makes it difficult for the user to search for some specific information such as selecting a tour in a given city as an ordered set of points of interest. Moreover, the user rarely knows all his needs upfront and his preferences may change during a recommendation process. The user may also have a limited number of initial ratings and most often the recommender system is likely to face the well-known cold start problem. The objective of the research presented in this paper is to introduce a hybrid interactive context-aware tourism recommender system that takes into account user’s feedbacks and additional contextual information. It offers personalized tours to the user based on his preferences thanks to the combination of a case based reasoning framework and an artificial neural network. The proposed method has been tried in the city of Tehran in Iran. The results show that the proposed method outperforms current artificial neural network methods and combinations of case based reasoning withk-nearest neighbor methods in terms of user effort, accuracy, and user satisfaction.

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