Brahim Ouhbi

Multi-Criteria Recommender Systems based on Multi-Attribute Decision Making

The Multi-Criteria Recommender systems continue to be interesting and challenging problem. In this paper we will propose an approach for selection of relevant items in a RS based on multi-criteria ratings and a method of computing weights of criteria taken from Multi-criteria Decision Making (MCDM). This method proposes a correlation coefficient and standard deviation integrated approach for determining weight of criteria in multi-criteria recommender systems. We evaluated the proposed method on an example of movies recommendation. Our approach was compared to some other metrics used in Information Theoretic approach to illustrate its potential applications.

A knowledge-based outranking approach for multi-criteria decision-making with hesitant fuzzy linguistic term sets

Abstract The modeling and solving of multi-criteria decision-making (MCDM) problems under uncertainty is still a challenging topic. In real-life decision-making, using linguistic terms to represent experts’ judgments is suitable and straightforward since precise quantitative values may often be unavailable or the cost for their computation is too high. The introduction of hesitant fuzzy linguistic term sets (HFLTSs) was motivated by the limitations of prior linguistic fuzzy models and need for richer linguistic tools. However, since their introduction, comparing HFLTSs is still one of the major concerns of researchers in this area. The existing approaches in the literature commonly rely on (1) labels and intervals from the linguistic terms as the central elements of an envelope-based approach or (2) linguistic scale functions as the basis of a distance-based approach. The two approaches retain certain shortcomings resulting information distortion and loss which may inevitably degrade their credibility. In this paper, the authors are involved in the recent proposal of combining outranking approaches with HFLTSs in an MCDM context. After reviewing the existing approaches, an outranking method based on a novel knowledge-based paradigm for comparing HFLTSs is developed. Alternatively, the paradigms foundations are the introduced concepts of fuzzy preference relations and profiles considering uncertainty degrees in decision makers’ assessments. The paradigm is then associated with a multi-criteria relational clustering (MCRC) algorithm that additionally extracts fuzzy preference relations between the resultant clusters. Last, an illustrative example is given to verify the appropriateness and efficacy of the developed approach and comparisons are made with other existing ones.

HCHIRSIMEX: An extended method for domain ontology learning based on conditional mutual information

This paper presents HCHIRSIMEX, an extended version of our previous algorithm HCHIRSIM for building domain ontology from web corpus. The new version introduces a novel measure based on the Conditional Mutual Information (CMI) statistic method to define the taxonomic relations and the similarity between selected concepts. By using this method, the ontology extracted by HCHIRSIMEX is more concise and contains a richer concept knowledge base compared with the previous version HCHIRSIM. To evaluate our new algorithm effectiveness, we apply the two algorithms and Sanchez et al. algorithm in Finance domain ontology constructed from the web. Then, we compare the obtained concepts with those on the “Financial glossary” provided by Yahoo.com.

Text Document Clustering with Hybrid Feature Selection

Finding the appropriate information and understanding to human research is a delicate task when dealing with an outstanding number of unstructured texts created daily. Hence the objective of clustering algorithms which are part of the powerful text mining tools. In this paper, we propose a novel text document clustering based on a new hybrid feature selection method that we call HFSM. This technique extracts statistical and semantic relevant terms to pilot the clustering mechanism. The experiments conducted on Reuters corpus demonstrate the practical aspects of our algorithm and show that it generates more accurate clustering than the one obtained by other existing algorithms.

Bootstrapping recommender systems based on a multi-criteria decision making approach

Recommender Systems (RSs) cope with the problem of information overload, by providing to users content that fit with what they prefer. Generally, RSs work much better for those users on which they have more information about. Satisfying the new users becomes a challenge, as ensuring for them recommendations of quality is vital for the growth of the RS. Coping with this issue can be made by ensuring a certain brief interview with the user-called bootstrapping process-through which we acquire a users feedback on a set of items, to subsequently enrich the user profile and inferring efficient recommendations. In this paper, we will propose an approach for bootstrapping a RS based on multi-criteria ratings and a method of computing weights of criteria taken from Multi-criteria Decision Making (MCDM).

Using rule-based classifiers in systematic reviews: a semantic class association rules approach

Systematic review is the scientific process that provides reliable answers to a particular research question by interpreting the current pertinent literature. There is a significant shift from using manual human approach to decision support tools that provides a semi-automated screening phase by reducing the required time and effort to the group of experts. Most of proposed works apply supervised Machine Learning (ML) algorithms to infer exclusion and inclusion rules by observing a human screener. Unless, these techniques holds very little promise in study identification phase, because the rate of excluding citations erroneously still unreasonable. In this paper, we contribute to this line of works by proposing an alternative approach, not yet tested in this domain based on semantic rule-based classifiers. This approach involved applying a novel Hybrid Feature Selection Method (HFSM) within a Class Association Rules (CARs) algorithm. Experiments are conducted on a corpus resulting from an actual systematic review. The obtained results show that our algorithm outperforms the existing algorithms in the literature.

Building ontologies: A state of the art, and an application to Finance domain

In this paper, we develop a framework for comparing ontology leaning systems and place a number of the more prominent ones into it. We have selected 11 specific projects for this study, including automatic and semi-automatic domain ontology building from unstructured or semi-structured text document. The comparison framework includes general characteristics, such as the purpose of ontology, its coverage (general or domain specific), and the formalism used. It also includes the design process used in creating ontology and the methods used to evaluate it. We synthesize the characteristics of the studied algorithms as a comparison matrix based on predefined criteria. Finally, we selected three methodologies for ontology construction: HCHIRSIM [1], Sanchez et al. 2004 [2] and Text2onto [3] and apply them in Finance domain ontology constructed from the web. We compare the obtained concepts with those on the “Financial glossary” provided by Yahoo.com.

Automatic keyphrase extraction: An overview of the state of the art

Keyphrases are useful for a variety of tasks in information retrieval systems and natural language processing, such as text summarization, automatic indexing, clustering/classification, ontology learning and building and conceptualizing particular knowledge domains, etc. However, assigning these keyphrases manually is time consuming and expensive in term of human resources. Therefore, there is a need to automate the task of extracting keyphrases. A wide range of techniques of keyphrase extraction have been proposed, but they are still suffering from the low accuracy rate and poor performance. This paper presents a state of the art of automatic keyphrase extraction approaches to identify their strengths and weaknesses. We also discuss why some techniques perform better than others and how can we improve the task of automatic keyphrase extraction.

Collaborative Filtering with Hybrid Clustering Integrated Method to Address New-Item Cold-Start Problem

Recommender Systems (RSs) are a valuable and practical tool to cope with information overload, as they help users to find interesting products in a large space of possible options. The Collaborative Filtering (CF) approach is probably the most used technique in RSs field due to several advantages as the ease of implementation, accuracy and diversity of recommendations. Despite being much favored over Content-Based (CB) techniques, it suffers from a major problem related to the lack of sufficient data for new-item cold-start problem, which affects recommendations’ quality. This paper is focused on resolving issues related to item-side in order to produce effective recommendations. To overcome the above problem, we use a powerful content clustering based on Hybrid Features Selection Method (HFSM), to get the maximum profit from the content. Then, it will be combined side by side to CF under a hybrid RS to improve its performance and handle new-item issue. We evaluate the proposed algorithm experimentally either in no cold-start situation or in a simulation of a new-item cold-start scenario. The conducted experiments show the ability of our hybrid recommender to deliver more accurate predictions for any item and its outperformance on the classical CF approach, which doesn’t work as usual especially in cold-start situations.

Towards an Intelligent Decision Support System for Renewable Energy management

Renewable Energy (RE) field provides significant new challenges for research in Intelligent Decision Support Systems (IDSS) since decision-making process requires more intelligent algorithms and mechanisms that can solve complex problems involving a large number of stakeholders in an uncertainty, dynamic, and distributed environment. In this paper, we present an intelligent RE-DSS framework to facilitate the decision-making process for the planning and designing intelligent Renewable Energy Management Systems. The framework offers a widespread adoption of more intelligent components in classical RE-DSS that will eventually lead to more efficient decision-making in all levels of RE projects management.