Personalized Mobile Catering Recommender System Based on Context Ontology Model and Rule Inference

2013 ◽  
Vol 717 ◽  
pp. 708-713 ◽  
Author(s):  
Dan Xiang Ai ◽  
Hui Zuo ◽  
Jun Yang
Keyword(s):  
Pervasive Computing ◽  
Recommender System ◽  
Personalized Recommendation ◽  
Context Aware ◽  
Rule Generation ◽  
Context Model ◽  
Ontology Modeling ◽  
Ontology Model ◽  
Smart Mobile ◽  
Rule Inference

With the development of smart mobile terminals and pervasive computing, mobile recommender systems are proposed to realize context-aware personalized recommendation services. A context model plays a key role in a mobile recommender. We discussed the context ontology modeling approach specific for mobile recommendation, and developed a two-level context model including a upper ontology and a domain ontology. We also designed a personalized mobile catering recommender system based on the context ontology model and rule inference. The framework of the system is depicted. And the process of rule generation and rule inference based on the context ontologies is demonstrated.

Intelligent Petrol Station Recommender Based on Context Ontology and Rule Inference

2013 ◽  
Vol 303-306 ◽  
pp. 1412-1415
Author(s):  
Hui Zuo ◽  
Dan Xiang Ai ◽  
Jun Yang
Keyword(s):  
Recommender System ◽  
Domain Ontology ◽  
Petrol Station ◽  
Ontology Modeling ◽  
Ontology Model ◽  
Rule Inference ◽  
Upper Ontology

An intelligent mobile petrol station recommender system based on context ontology and rule inference is deigned. The approach of context ontology modeling specific for mobile recommendation is discussed. And a two-level context ontology model including upper ontology and domain ontology used in petrol station recommender is developed. The generation of recommendation rules based on the context ontologies and the process of the rule inference for recommendation are also demonstrated.

Ontology-Based Context Modeling for Mobile Catering Recommendation

2013 ◽  
Vol 662 ◽  
pp. 953-956
Author(s):  
Dan Xiang Ai ◽  
Hui Zuo ◽  
Jun Yang
Keyword(s):  
Recommender System ◽  
Layer Structure ◽  
Domain Ontology ◽  
Use Case ◽  
Context Aware ◽  
Context Modeling ◽  
Specific Context ◽  
Contextual Model ◽  
Ontology Model ◽  
Upper Ontology

To support context-aware mobile recommendation, an ontology-based context modeling approach was proposed. We analyzed the framework of the mobile recommender system based on contextual model and suggested designing the model with two-layer structure including an upper ontology layer and a domain ontology layer. The ontologies provides formalizations representing the main entities, including users, objects, contexts, and their interactive relationships in mobile recommendation environments. A specific context ontology model for catering recommendation was developed and a use case of the instantiated context ontology was demonstrated.

scholarly journals Context-Aware Multi-layered Ontology for Composite Situation Model in Pervasive Computing

2020 ◽  
Vol 25 (5) ◽  
pp. 543-558
Author(s):  
Abderrahim Lakehal ◽  
Adel Alti ◽  
Philippe Roose
Keyword(s):  
Pervasive Computing ◽  
Real Life ◽  
Situation Model ◽  
Context Information ◽  
Mobile Service ◽  
Context Aware ◽  
Smart Systems ◽  
Services Delivery ◽  
Iot Devices ◽  
Smart Mobile

With the rapid advancement of technologies and analysis tools in the smart systems, enabling real-time context monitoring of user's living conditions and quality services delivery is increasing. Current studies in this area are focused on developing mobile applications with specific services, based on toolkit that allow developers to obtain context information from sensors. However, there exists a notable lack of ontology able to represent all the necessary context information starting from distributed users, and constantly changing environment. The modeling of user’s domains to represent diverse mobile and IoT devices, and finalizing with the description of user’s composite situations in smart-*(health, home, cities, car, office, etc.) domains. Considering interoperability, reusability, and flexibility, a new context composite situation ontology for smart systems is proposed with better representation of heterogeneous context. The ontology enables to sense, reason, and infer composite situations in various smart domains, prioritizes critical situations and facilitates the delivery of smart mobile service. Proposed ontology is formalized and validated on different smart environments with different user’s situations. Several experiments were carried out with a real-life motivating scenario. Experimental results showed that the proposed approach has reduced queries times and improved flexibility.

Meta Model Context Based Space for Ubiquitous Computing

2015 ◽  
Vol 7 (2) ◽  
pp. 51-66
Author(s):  
Mohammed Fethi Khalfi ◽  
Sidi Mohamed Benslimane
Keyword(s):  
Pervasive Computing ◽  
Situation Awareness ◽  
Context Awareness ◽  
Real Life ◽  
Context Aware ◽  
Context Modeling ◽  
Context Model ◽  
Meta Model ◽  
Domain Specific ◽  
Model Context

Pervasive computing is a paradigm that focuses on the availability of computer resources anytime anywhere for any application and supports integration of computing services into everyday life. Context awareness is the core feature of pervasive computing. High-level context awareness can be enhanced by situation awareness that represents the ability to detect and reason about the real-life situations. In this paper, in order to deal with the problem in context-aware modeling in pervasive computing environments, the authors present a comprehensive and integrated approach for context modeling. They first propose a Meta model context based on ontology for Pervasive Computing aiming firstly to overcome the limitations of the existing ontologies, and secondly extend its capabilities by adding new environmental aspects. They divide the context model into Meta Ontology level and Domain-specific Ontology level according to the abstraction hierarchy. The Meta Ontology is the high abstract level which extracting the basic elements of the context knowledge. The Domain-specific Ontology is the lower abstract lever which focusing on different domains knowledge, directed by the Meta Ontology. The advantage is that it can provide a flexible modeling mechanism for multiple applications of context-aware pervasive computing. A case study of HealthCare domain is given to illustrate the practicality of the authors' Model.

Algorithms for Rule Generation and Matchmaking in Context-Aware System

2009 ◽  
Vol 20 (10) ◽  
pp. 2655-2666 ◽  
Author(s):  
Dong LIU ◽  
Xiang-Wu MENG ◽  
Jun-Liang CHEN ◽  
Ya-Mei XIA
Keyword(s):  
Context Aware ◽  
Rule Generation ◽  
Context Aware System

An interpretable mechanism for personalized recommendation based on cross feature

2021 ◽  
pp. 1-12
Author(s):  
Lv YE ◽  
Yue Yang ◽  
Jian-Xu Zeng
Keyword(s):  
Recommender System ◽  
Online Shopping ◽  
Side Information ◽  
Attention Mechanism ◽  
Personalized Recommendation ◽  
Model Based ◽  
Embedded Model ◽  
Feature Attention ◽  
Match Score

The existing recommender system provides personalized recommendation service for users in online shopping, entertainment, and other activities. In order to improve the probability of users accepting the system’s recommendation service, compared with the traditional recommender system, the interpretable recommender system will give the recommendation reasons and results at the same time. In this paper, an interpretable recommendation model based on XGBoost tree is proposed to obtain comprehensible and effective cross features from side information. The results are input into the embedded model based on attention mechanism to capture the invisible interaction among user IDs, item IDs and cross features. The captured interactions are used to predict the match score between the user and the recommended item. Cross-feature attention score is used to generate different recommendation reasons for different user-items.Experimental results show that the proposed algorithm can guarantee the quality of recommendation. The transparency and readability of the recommendation process has been improved by providing reference reasons. This method can help users better understand the recommendation behavior of the system and has certain enlightenment to help the recommender system become more personalized and intelligent.

Developing Context-Aware System using the Conceptual Context Model

2006 ◽  
Author(s):  
Chung-seong Hong ◽  
Kang-woo Lee ◽  
Young-ho Suh ◽  
Hyoung-sun Kim ◽  
Hyun Kim ◽  
...  
Keyword(s):  
Context Aware ◽  
Context Model ◽  
Context Aware System
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