Sports Data Management, Mining, and Visualization

Most of the extant studies in social recommender system are based on explicit social relationships, while the potential of implicit relationships in the heterogeneous social networks remains largely unexplored. This study proposes a new approach to designing a recommender system by employing grey relational analysis on the heterogeneous social networks. It starts with the establishment of heterogeneous social networks through the user-item bipartite graph, user social network graph and user-attribute bipartite graph; and then uses grey relational analysis to identify implicit social relationships, which are then incorporated into the matrix factorization model. Five experiments were conducted to test the performance of our approach against four state-of-the-art baseline methods. The results show that compared with the baseline methods, our approach can effectively alleviate the sparsity problem, because the heterogeneous social network provides richer information. In addition, the grey relational analysis method has the advantage of low requirements for data size and efficiently relieves the cold start problem. Furthermore, our approach saves processing time, thus increases recommendation efficiency. Overall, the proposed approach can effectively improve the accuracy of rating prediction in social recommendations and provide accurate and efficient recommendation service for users.

Harnessing heterogeneous social networks for better recommendations: A grey relational analysis approach

Mining for Fake News

In food diet communication domain, images convey important information to capture users' attention beyond the traditional ingredient content, making it crucial to influence user-decision about the relevancy of a given diet. By using a deep learning-based image clustering method, this paper proposes an Explainable Food Recommendation system that uses the visual content of food to justify their recommendations. n the recommendation system. Especially, a new similarity score based on a tendency measure that quantifies the extent to which user community prefers a given food category is introduced and incorporated in the recommendation. Finally, a rule-based explainability is introduced to enhance transparency and interpretability of the recommendation outcome. Our experiments on a crawled dataset showed that the proposed method enhances recommendation quality in terms of precision, recall, F1, and Normalized Discounted Cumulative Gain (NDCG) by 7.35%, 6.70%, 7.32% and 14.38%, respectively, when compared to other existing methodologies for food recommendation. Besides ablation study is performed to demonstrate the technical soundness of the various components of our recommendation system. 

An effective explainable food recommendation using deep image clustering and community detection

The main goal of many researches focuses on improving subscriber experience by proposing a variety of services and features to better fit his needs and expectations. In order to improve the response to this subscriber expectation and considering the huge amount of information available in the network combined with performance expectation, we combine services' descriptions with subscriber requests and profile information (use history/log and use context) to orientate the service discovery as well as exposure processes to the needs of subscribers. To better tackle this problematic, we consider these three complementary perspectives in a coherent way. In purpose to consider a very constrained environment in terms of discovery/exposure performance, we considered the telecommunication domain which is very sensitive to all of these aspects and one of the most appetence domain in that respect. This work presents our funnel based joint dual mobile service discovery and exposure model and its mechanism in order to reduce the discard between discovered/exposed services and subscribers needs. The funnel targets gradually the discovered and exposed service. A correlation mechanism based upon an enhanced bipartite graph will help targeting the most appropriate service with better performances. Our model and its results show improvement compared with other related works on several axes.

A funnel based joint dual mobile service discovery and exposure model

IMS (IP Multimedia Subsystem) presents a variety of services and features to meet the needs and expectation of subscribers. This variety of choices presents risks of retrieving non oriented services while responding to subscriber request. This may be due to similarities between services and to the huge amount of available services. In addition to service information, it is possible to extract and explore subscriber information in IMS network. Thus, it becomes interesting to consider, besides of request details, subscriber profile information in service discovery process. This paper presents an overview of our model and its mechanism based upon IMS architecture which provides service discovery combined with proactive service exposure to better respond to subscribers' needs and expectations. The results show improvement of usual state of the art models on several axes.

New Joint Dual Mobile Service Discovery-Exposure Model using Bipartite Graph

In recent years, the ever-increasing number of cloud services has led to the service targeting issue. It becomes challenging to provide users with services that fit their needs. Recommender systems address the service targeting problem by helping users to easily retrieve services matching their tastes. Collaborative filtering-based methods are spreadly used recommendation approaches since numerous methods are based on them. However, most of them assume the stationarity of user profiles over time, which is an unrealistic consideration. Indeed, in practice, users’ tastes are dynamic and therefore show a significant variability over time. Time-aware recommender systems effectively integrate the variability of users’ needs to improve the recommendation quality. However, the recommendation credibility is another crucial requirement that is not met by those time-aware recommender systems that moreover badly deal with the data sparsity issue. To address the service targeting problem while meeting requirements on the recommendation reliability and the users’ tastes variability, we propose in this paper a realistic temporal service recommendation approach based on implicit trust relationships inference. Our method integrates the time feature into the recommendation process in order to consider the instantaneity of users’ needs. Our proposal is based on a trusted network that infers implicit trust relationships to ensure the recommendation credibility even in case of data sparsity. Experiments are conducted on real-world service invocations datasets. Our proposal is then compared to existing methods and displays valuable performances in terms of mean absolute error, root mean square error, and normalized discounted cumulative.

A time-aware service recommendation based on implicit trust relationships and enhanced user similarities

IMS (IP Multimedia Subsystem) contains a variety of services and information to better fit the needs and expectation of subscribers. Based on the fact that IMS subsystem contains huge amount of available services and that similarities between them are current, we should consider risks of discovering and exposing services that may not be well oriented to subscriber profile. Beside service information, IMS presents the possibility of extracting and exploring subscribers' information. Thus, it becomes interesting to consider these information beside service information in discovery and exposure processes. This paper presents an overview of our proactive advanced service discovery exposure broker (Advanced JDMS-DEMO) and its mechanism based on search approaches that combines information about services with subscribers' profiles information to reduce the gap between subscribers' needs and discovered services. Results show improvement compared with usual researches and models on several axes.

A Fuzzy Search Based Joint Dual Mobile Service Discovery and Exposure Model

Being one of the most solicited content (opinions) sharing platforms, Twitter is a granary of information serving as a base for our service discovery/exposure approach proposed in this paper. The growth of data caused by the internet growth has led to the birth of a growing number of services, making the task difficult for telecommunication operators in competition. In this paper, we propose a dual service discovery/exposure approach to reduce the gap between offered services and subscribers’ needs in an IMS context. This approach is based on opinion mining related to Twitter trending topics in order to estimate the sensitivity of the target user to a service or another. Compared to both the classic approach and the collaborative service discovery/exposure approach, our results show an improvement in the accuracy and error of the service targeting at the target user’s starting phase on the operator’s network.

Walid El Ayeb

Papers

New Joint Dual Mobile Service Discovery-Exposure Model using Bipartite Graph

A time-aware service recommendation based on implicit trust relationships and enhanced user similarities

A funnel based joint dual mobile service discovery and exposure model

A Fuzzy Search Based Joint Dual Mobile Service Discovery and Exposure Model

An IP Multimedia Subsystem Service Discovery and Exposure Approach Based on Opinion Mining by Exploiting Twitter Trending Topics