Business data are subject to change by time or by the modifications of business rules. New knowledge needs to be extracted to reflect the most up to date situations hence periodic or occasional re-mining is essential. This paper proposes an active multidimensional association mining framework that incorporates with user preference ontology, which contains surrogate queries that represent freque...

In Community question answering (QA) sites, malicious users may provide deceptive answers to promote their products or services. It is important to identify and filter out these deceptive answers. In this paper, we first solve this problem with the traditional supervised learning methods. Two kinds of features, including textual and contextual features, are investigated for this task. We furthe...

The research introduces an adaptive food searching and recommending engine by taste and user preference using fuzzy logic. In contrast with existing system where food is searched by predefined keywords, this system searches food by its taste and users’ preference which allows the system to provide better results. As food taste cannot be measured and user’s preference is relative to each user, t...

A common challenge in personalized user preference prediction is the cold-start problem. Due to lack of user-item interactions, directly learning from new users' log data causes serious over-fitting Recently, many existing studies regard as a few-shot problem, where each task and recommended items are classes, gradient-based meta method (MAML) leveraged address this challenge. However, real-wor...

There are many alternatives in a Recommender System (RS) that can be represented by numerical attributes. One of the most challenging tasks in developing RS is the design of techniques that can infer user preferences through observation of their actions. A RS usually stores a personal preference profile associated with each user, but the initial profile of a user is usually incomplete and impre...