Abstract
With the development of mobile network technology and the popularization of mobile terminals, traditional information recommendation systems are gradually changing in the direction of real-time and mobile information recommendation. Information recommendation brings the problem of user contextual sensitivity within the mobile environment. For this problem, first, this paper constructs a domain ontology, which is applicable to the contextual semantic reasoning model. Second, based on the “5W + 1H” method, this paper constructs a context pedigree of the mobile environment using a model framework of a domain ontology. The contextual factors of the mobile environment are divided into six categories: the What-object context, the Where-place context, the When-time context, the Who-subject context, the Why-reason context, and the How-effect context. Then, considering the degree of influence of each contextual factor from the mobile context pedigree to the user is different, this paper uses contextual conditional entropy to calculate the contextual weight of each contextual attribute in the recommendation process. Based on this, a contextual semantic reasoning model based on a domain ontology is constructed. Finally, based on the open dataset provided by GroupLens, this paper verifies the validity and efficiency of the model through a simulation experiment.
Highlights
User sensitivity to context and user preference are two major mechanisms of information recommendation in the mobile Internet environment
Based on the specific domain requirements involved in the mobile environment, the domain ontology required by the reasoning model is determined
It shows that the accuracy of this model is better than that of the traditional collaborative filtering model, the model based on content filtering, and the contextual semantic reasoning model that does not consider the contextual weight
Summary
User sensitivity to context and user preference are two major mechanisms of information recommendation in the mobile Internet environment. Most of the existing context-sensitive recommendation services provide user location information based on GPS and recommend eligible information using a group of users at the same location On this basis, to better provide personalized services for users in the mobile network environment, some scholars introduce other contextual factors such as time to achieve real-time and on-the-spot delivery of information corresponding to user context to meet the user’s personalized needs in a specific geographical location and at a specific time [5, 6]. E algorithm improved the overspecialization problem of traditional recommendations by employing a case similarity algorithm based on semantic association It enhanced the contextual sensitivity of recommendation results based on context awareness [7]. In a recommendation based on context awareness, the importance of each contextual factor to the user’s preferences is different, which cannot be treated so it is necessary to analyze the influence of different contextual factors on a user’s preferences
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