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With the development of the location-based social networks (LBSNs) and the popular of mobile devices, a plenty of user's check-in data accumulated enough to enable personalized Point-of-Interest recommendations services. In this paper, we propose a scheme of modeling user's preferences on spatiotemporal topics (UPOST scheme) for accurate individualized POI recommendation. In the UPOST scheme, we discover...
We focus on the problem of learning distributed representations for entity search queries, named entities, and their short descriptions. With our representation learning models, the entity search query, named entity, and description can be represented as low-dimensional vectors with minimal human preprocessing. Our goal is to develop a simple but effective model that can make the distributed representations...
The paper presents deep learning models for tweets binary classification. Our approach is based on the Long Short-Term Memory (LSTM) recurrent neural network and hence expects to be able to capture long-term dependencies among words. We develop two models for tweets classification. The basic model, called LSTM-TC, takes word embeddings as input, uses the LSTM layer to derive semantic tweet representation,...
Using neural networks to train high quality distributed representations of words and multi-relational data has attracted a great attention in recent years. Mapping the words and their relations to low-dimensional continues vector spaces has proved to be useful in natural language processing and information extraction tasks. In this paper, we present a neural network based model that can train word...
Wire is a intermediate language to enable static program analysis on low level objects such as native executables. It has practical benefit in analysing the structure and semantics of malware, or for identifying software defects in closed source software. In this paper we describe how an executable program is disassembled and translated to the Wire intermediate language. We define the formal syntax...
How to map from texts to structured case representations and how to automatically generate representations have become the research hotspot in the fields of Textual Case-based Reasoning (TCBR). This paper presents methods that support automatically generation ontology-based representation for textual cases. We used the Ontology to describe the relationship between terms in application fields. First,...
In open network, unfamiliar web services are unavailable due to lack of effective mechanism to evaluation their performances for users. In order to help the user recognizing unknown web services, semantics understanding and reputation based evaluation mechanism is introduced into web service research. Semantics understanding based evaluation is according to the user's requirements semantics and the...
Semantics involved in the decision-making process are introduced as driven force for decision support system in order to solve the key problem of lacking effective control mechanism during the decision-making. Decision process control could be divided into three stages: decision task decomposition, decision process programming and decision task scheduling. In first stage, system evaluated that decision...
In open and distributed environment, model selection of decision-making is difficult due to the lack of model's knowledge. In order to find an effective mechanism for decision parties, semantics based trust and reputation computations are introduced into decision-making. Trust between decision parties and models are divided into trust dependence and trust relationship which demonstrate the matching...
Semantics comprehension is the key for intelligent decision making. Semantic based interaction is the method of model management in decision support system. A semantic driven interaction method is presented for DSS. Two ontologies are defined to provide semantics, according to the characteristics of knowledge of model. Modelpsilas semantics are defined as model description semantics and model action...
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