scholarly journals A case-based reasoning recommender system for sustainable smart city development

AI & Society ◽  
2020 ◽  
Author(s):  
Bokolo Anthony Jnr
Keyword(s):  
Recommender System ◽  
Smart City ◽  
Case Based Reasoning ◽  
City Development ◽  
Case Based

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

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.

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

scholarly journals When Collective Knowledge Meets Crowd Knowledge in a Smart City: A Prediction Method Combining Open Data Keyword Analysis and Case-Based Reasoning

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Ohbyung Kwon ◽  
Yun Seon Kim ◽  
Namyeon Lee ◽  
Yuchul Jung
Keyword(s):  
Open Source ◽  
Smart City ◽  
Class Imbalance ◽  
Classification Algorithms ◽  
Case Based Reasoning ◽  
Collective Knowledge ◽  
Open Source Data ◽  
Source Data ◽  
Google Search ◽  
Case Based

One of the significant issues in a smart city is maintaining a healthy environment. To improve the environment, huge amounts of data are gathered, manipulated, analyzed, and utilized, and these data might include noise, uncertainty, or unexpected mistreatment of the data. In some datasets, the class imbalance problem skews the learning performance of the classification algorithms. In this paper, we propose a case-based reasoning method that combines the use of crowd knowledge from open source data and collective knowledge. This method mitigates the class imbalance issues resulting from datasets, which diagnose wellness levels in patients suffering from stress or depression. We investigate effective ways to mitigate class imbalance issues in which the datasets have a higher proportion of one class over another. The results of this proposed hybrid reasoning method, using a combination of crowd knowledge extracted from open source data (i.e., a Google search, or other publicly accessible source) and collective knowledge (i.e., case-based reasoning), were that it performs better than other traditional methods (e.g., SMO, BayesNet, IBk, Logistic, C4.5, and crowd reasoning). We also demonstrate that the use of open source and big data improves the classification performance when used in addition to conventional classification algorithms.

Case Based Reasoning And Naive Bayes Implementation In Laptop Purchasing Recommender System

2021 ◽  
Vol 9 (3) ◽  
pp. 411
Author(s):  
I Gusti Ngurah Agung Dharmawangsa ◽  
I Wayan Supriana
Keyword(s):  
Recommender System ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Case Based Reasoning ◽  
The Right ◽  
Case Based ◽  
Selection Of ◽  
The Ideal

Purchasing a new laptop will be difficult if we do not know what the ideal laptop specification for our needs. Especially with a wide selection of laptops. From this problem, system that can give a recommendation to choose the right laptop based on purchaser’s specification choice is needed. This research using two method, Case Based Reasoning and Naive Bayes. The concept of Case Based Reasoning is the process of solving new problems based on the solutions of similar past problems, While Naive Bayes assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. Naive bayes will be implemented in retrive process of case based reasoning. The recommender system utilizing 7 feature, Kecepatan Processor, Kapasitas Ram, Tipe Grafis, Ukuran Layar, Ukuran Harddisk, Kecepatan Layar, and Harga. The percentage of respondents who said the system was successful in providing the right recommendations was 70 percent of the total respondents.

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