Ontologybased products reputation model Different reputation models are used in the web in order to generate reputation values for products using usesu0027 review data. Most of the current reputation models use review ratings and neglect usersu0027 textual reviews, because it is more difficult to process. However, we argue that the overall reputation score for an item does not reflect the actual reputation for all of its features. And thatu0027s why the use of usersu0027 textual reviews is necessary. In our work we introduce a new reputation model that defines a new aggregation method for usersu0027 extracted opinions about productsu0027 features from usersu0027 text. Our model uses features ontology in order to define general features and subfeatures of a product. It also reflects the frequencies of positive and negative opinions. We provide a case study to show how our results compare with other reputation models.

Unsupervised Qualitative Scoring for Binary Item Features The qualitative score of tags is widely used to describe which product is better in terms of the given property. For example, in a restaurantnavigation site, properties such as food, location, and mood are given in the form of numerical values, representing the goodness of each aspect. In this paper, we propose a novel approach to estimate the qualitative score from the binary features of products. Based on a natural assumption that an item with a better property is more popular among users who prefer that property, in short, experts know best, we introduce one discriminative and two generative models with which user preferences and itemqualitative scores are inferred from useritem interactions. Our approach contributes to resolving the following difficulties: (1) no supervised data for the score estimation, (2) implicit user purpose, and (3) irrelevant tag contamination. We evaluate our models by using two artificial datasets and two realworld datasets of movie and book ratings and observe that our models outperform baseline models.