These days, most people refer to user reviews to purchase an online product. Unfortunately, spammers exploit this situation by posting deceptive reviews and misleading consumers either to promote a product with poor quality or to demote a brand and damage its reputation. Among the solutions to this problem is human verification. Unfortunately, the real-time nature of fake reviews makes the task more difficult, especially on e-commerce platforms. The purpose of this study is to conduct a systematic literature review to analyze solutions put out by researchers who have worked on setting up an automatic and efficient framework to identify fake reviews, unsolved problems in the domain, and the future research direction. Our findings emphasize the importance of the use of certain features and provide researchers and practitioners with insights on proposed solutions and their limitations. Thus, the findings of the study reveals that most approaches focus on sentiment analysis, opinion mining and, in particular, machine learning (ML), which contributes to the development of more powerful models that can significantly solve the problem and thus enhance further the accuracy and efficiency of detecting fake reviews.

With the ever-increasing volume of online information, recommender systems have been effective as a strategy to overcome information overload. They have a wide range of applications in many fields, including e-learning, e-commerce, e-government and scientific research. Recommender systems are search engines that are based on the user’s browsing history to suggest a product that expresses their interests. Being usually in the form of textual comments and ratings, such reviews are a valuable source of information about users’ perceptions. Recommender systems (RSs) apply various approaches to predict users’ interest on information, products and services among a huge amount of available items. In this paper, we will describe the recommender system, discuss ongoing research in this field, and address the challenges, limitations and the techniques adopted. This paper also discusses how review texts are interpreted to solve some of the major problems with traditional recommendation techniques. To assess the value of a recommender system, qualitative evaluation measures are discussed as well in this research. Based on a series of selected articles published between 2008 and 2020, the study allowed us to conclude that the efficiency of RSs is strongly centered on the control of information context, the operated exploration algorithm, the method, and the type of processed data in addition to the information on users’ trust.

Summary Ensuring quick responses as well as a high degree of reliability remains one of the biggest challenges in mediation systems. Generally, in order to reduce their response time and to be able to provide answers in case the sources are inaccessible, mediators can resort to semantic caching. However, this may lead to additional costs, especially if there is a large number of semantic regions in the cache. In this work, we propose a coverage based approach in order to decide whether it is optimal to use the cache if a new user query is submitted and to identify the semantic regions that can contribute in providing answer if it is. Our approach is based essentially on calculating attribute and predicate coverage rates of the new user query in each semantic region. To validate our proposition, we have performed an experimental evaluation using a prototype that generates random semantic regions.

Summary The recommendation in information systems is a specific form of information filtering that aims to present the relevant information interesting the user. This technique is used in different contexts such as social networking, e‐commerce and information retrieval. Generally, existing recommender system techniques implement collaborative filtering by deducing a part of user interests from the preferences of other users with similar profiles. Many techniques can be used to implement Collaborative Filtering such as Bayesian Networks, latent semantic, and clustering. We present in this work a novel clustering approach using a modified partitional algorithm. We propose a user model that integrates the relevant user information and a clustering algorithm that generates groups of similar user profiles by implementing a profile similarity function. The proposed approach is then evaluated based on a set of user profiles data corresponding to the context of an e‐commerce website.

Mapping, is an important operation for processes assuring the interoperability of information systems especially for applications such as exchange, integration and transformation of data. However, the problem of mapping arises when the number of information schemas is important. In this paper, we propose a large-scale mapping methodology structured in two different phases (matching and mapping), which aims to optimize the mapping discovery through a pre-processing phase based on Techniques for analysis, linguistic processing of elements of schemas and data extraction.

The amount of information offered by different software systems is growing exponentially and the need of personalized approaches for information access increases. This personalization aims to offer the user the pertinent information corresponding to his needs basing on his profile. For the same purpose, mediation systems have to identify user preferences in order to offer him the most relevant information .In this work we discuss different representations of user profile models designed for providing personalized information access in order to make a comparison and identify the most appropriate for our context in mediation systems.

The explosion of information and telecommunications technologies, has made easy the access and production of information. Thus, a very large mass of the latter has generated. This situation has made the integration systems an immediate necessity. Among these systems, there is the hybrid mediator. The latter interrogates one part of data on demand as in the virtual approach, while charging, filtering and storing the second part, as views, in a local database. The creation of this second part is a critical task. We propose in this paper, a new algorithm for creating views' schemas to materialize in the hybrid integration system.