In order to accurately measure the value of internet word of mouth (iwom). Based on each attribute of online reviews, which are the major form of iwom, we proposes the influence factors of iwom, include the quantifiable indicators: the number of online reviews, the rating, the proportion of negative reviews, the price of product, and the unquantifiable indicators which can control of the false online reviews and express consumer emotion: the feature words of product attributes, the emotional intensity of reviews. Then we build the model of influence factors of iwom perception. And we take the influence of endogenous into account. Before and after the control of the endogenous influence, we find that the relationship between influence factors and iwom has changed by using cross section analysis and first order difference analysis.

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.

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