Research On Catering O2O Ontology Modeling And Recommendation Method Based On Context Awareness

With the development of mobile Internet,the O2O e-commerce model for catering and other services gradually shows its value and development potential.However,due to the constantly growing amount of data carried by O2O platforms,the dilemma that "full of data but lack of knowledge" is caused,and the problem of information overload is increased.In order to solve this problem,personalized recommendation systems are widely used in all kinds of e-commerce websites to extract information that meets users' needs from massive resources through information filtering.The online and offline interaction of catering O2O e-commerce is more contextual and diversified,and the personalized needs of users are context sensitive.Therefore,there are differences between O2O recommendation and traditional recommendations in the dimensions and sparseness of data,user preference features,the instantaneity of recommendation,etc.,which put forward higher requirements for the analysis and mining of users' dynamic preferences.However,the development of ubiquitous computing provides the basis for more comprehensive and real-time access to context information.In this background,this paper conducts knowledge representation and reasoning for users' context and recommendation resources in catering field through ontology modeling,and studies the method of integrating context information into recommendation models.The main research contents are as follows:Firstly,the catering O2O recommendation ontology based on context-aware is studied.Based on the design of the context service model of the two-layer ontology structure,the acquisition method of context modeling information is analyzed.Through the concept and attribute classes,attributes,and instances of context ontology and catering domain ontology,the interaction between entity concepts in catering O2O recommendation is described in detail.On this basis,the attributes and rules of ontology are used to establish the knowledge base of catering field,and the O2O recommendation of catering based on knowledge filtering is realized by rule reasoning.Secondly,contextual post-filtering collaborative filtering recommendation method is studied.Aiming at the sparse matrix and cold start problems of traditional recommendation methods used in catering O2O recommendation,a method of converting user-item rating matrix into user-item attribute value rating matrix is designed by referring to the TF-IDF algorithm which is used to calculate the weight of text features.According to the similarity group constructed by the user's rating preference and static context information,the bayesian method with context weighting based on the KL divergence is used to predict the user's dynamic preference for item attribute values.Through experiment and analysis,the effectiveness of the contextual post-filtering recommendation method is verified.Finally,the catering O2O recommendation process and system implementation are studied.Considering the actual use needs of users of the catering O2O recommendation system,the two algorithms of knowledge filtering and collaborative filtering are integrated according to the process.Then a modular system framework is designed,and the acquisition of context information as well as the ontology module with inference function is realized.According to the process of using the system,the effect of the client implementation is shown.