@article {712,
	title = {A CBR Decision Support System for Loan Evaluation},
	journal = {Journal of Digital Information Management},
	volume = {17},
	year = {2019},
	month = {04/2019},
	pages = {75-86},
	type = {Research},
	chapter = {75},
	abstract = {<p>Group decision making is time-consuming, complex, critical and recurring in nature. In group decision making, the decision-makers often perform periodic and repetitive activities. This prompts the decision makers to store their knowledge and their experience invoked in prior problem solving sessions. The development of this group knowledge clearly assists in similar later decision-making activities and reduces the time required to come to a decision, particularly, in a critical situation. This shared knowledge constitutes the group memory. In this paper, we develop a Case-Based Reasoning Decision Support System called CBR-DSS that makes use of the rules, policies and standard procedures of prior groups embedded in the group memory. The CBR process is evolved by using the Analytic Hierarchy Process (AHP). In an effort to enhance utility and show the feasibility, we experiment our approach in a bank loan analysis domain, a semi-structured multiple attributes decision-making environment, and compare CBR-DSS with a conventional case based reasoning system. CBR-DSS will compensate for lack of experience of young bank consultants, and allow disseminating and distributing available experience to different bank sites.</p>
},
	keywords = {Case-based Reasoning, Group Decision Making, Group Memory, Information retrieval, Recommender system},
	url = {https://doi.org/10.6025/jdim/2019/17/2/75-86},
	author = {Abdelkader Adla and Noureddine Benmessaoud}
}
@article {137,
	title = {Behavioral similarities for collaborative recommendations},
	journal = {Journal of Digital Information Management},
	volume = {6},
	year = {2008},
	note = {Cited By (since 1996):4Export Date: 10 July 2014},
	month = {2008},
	pages = {442 - 448},
	abstract = {Recommender systems contribute to the personalization of resources on web sites and information retrieval systems. These systems use different mining techniques to generate predictions corresponding to the needs of users. In this paper, we present a hybrid recommender system using a userbased approach, that combines predictions based on web usage patterns and rating data. We suggest a new technique that takes into account common usage patterns in order to compute correlations between users and select neighborhoods, without using any rating data. Unlike classical predictive systems that use directly mining techniques in order to suggest recommendations (like the Longest Common Subsequences technique or the frequent sequential patterns mining), the originality of our usage based technique is not to harness the discovered patterns to recommend resources, but to assess similarities of navigational users profiles. Recommendations are performed in a following step. The performance of our system is tested without and by combining predictions of both navigational based technique and classical collaborative filtering, in terms of accuracy and robustness. The experimentation put forward the impact of the navigational based technique on the performance of the hybrid recommender system in terms of precision and robustness. The tests show that the more the navigational based technique is involved in the recommendation process, the more the best predictions are accurate.},
	keywords = {Collaborative filtering system, Recommender system},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-77955227289\&partnerID=40\&md5=c1516b14dc70e7dac89d3e8bef4af7a1},
	author = {Esslimani, I. and Brun, A. and Boyer, A.}
}
