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It is well known that the key issue of online marketing is to accurately find the target user groups for the corresponding advertisements. Traditionally, the advertising products target user groups based on search keywords (e.g. AdWords), page visiting (e.g. AdSense), and etc. In this work, we explore a new targeting
a text database using the filtered results. We further conduct a cache-based adaptation method on the resulting language model, in which keywords in the filtered results are cached and used to boost the word probability. In an experimental evaluation over real lectures, we obtained a significant improvement of ASR
propose n keywords, in order to optimise the information gain expectation. Its implementation, CFAsT, endeavours to keep the best from both worlds: the universality and automatic generation from search engines, and the usability, the assistance and the self optimisation provided by the dialogue systems. Thus, a beta dialogue
Automatic image annotation is the process of assigning relevant keywords to the images. It is considered to be potential research area in current scenario. Annotation to an image can be defined as the information which could describe an image by considering three ways i.e. when these images were taken, what are the
utterance of manipulator through training. In experiments we test how many necessary keywords the outputs of traditional system and our system can cover respectively. Finally we ask volunteers to give scores to both systems for the sake of demonstrating satisfactions to their utterances.
. Luckily, tweets always show up with rich user-generated hash tags as keywords. In this paper, we propose a novel topic model to handle such semi-structured tweets, denoted as Hash tag Graph based Topic Model (HGTM). By utilizing relation information between hash tags in our hash tag graph, HGTMestablishes word semantic
Adult image detection plays an important role in Internet pornographic information detection and filtering. By analyzing the shortcomings of existing pornographic image detection algorithms depending only on image content or keywords of text, a new adult image detection algorithm fusing image semantic features and
This paper addresses a quantification technique of cooperative characteristics on Concern For Others: CFO in a cooperative work by multiple operators. The cooperative works are generally required suitable cooperation to accomplish the task adequately. Although the “cooperation” is one of keywords for
on extracting intentional goals from service descriptions. In this paper, based on the ranked domain keywords, we investigate how to extract domain-specific service goals from service descriptions, which can contribute to services discovery and recommendation. Programmable Web, a publicly accessible service repository
is considered as more interesting and useful. Most of recent search engines are designed based on only measuring the similarity between keywords and articles. However, the social relations between authors of articles and searcher have not been taken into account in recent research. Therefore, in order to improve the
Pattern searching and retrieval plays important role in task of content-based audio analysis for requirements of media database management or in surveillance systems for detecting significant audio events and keywords. In the paper, we present algorithm for spotting audio patterns in record, using Hidden Markov Models
, e.g. genres, product categories, keywords) must be used. We describe a method that maps such entity (e.g. user or item) attributes to the latent features of a matrix (or higher-dimensional) factorization model. With such mappings, the factors of a MF model trained by standard techniques can be applied to the new-user and
time. Comparing the 'like' query in the standard SQL in relational databases, which can not decide the similarity according users' interests when keywords appear in several different fields, a novel similarity evaluation is given in algorithm of the personalized recommendation. Using the method, a personalized digital
In this paper, we introduce an alpha-numerical sequences extraction system (keywords, numerical fields or alpha-numerical sequences) in unconstrained handwritten documents. Contrary to most of the approaches presented in the literature, our system relies on a global handwriting line model describing two kinds of
as titles, abstracts, keywords and the Chinese Library Classification Codes (CLCCs). According to the reviewer's interest model, we then propose a recommendation approach, which can send a paper published online to the reviewers that are experts in the scoop of the paper. Experimental results show that our
term-by-document matrix, it inevitably loses the information of relations between query terms in the document in the first place. This paper presents a modified vector space model for measuring similarity between the query and the document when responding to a multi-term query. More weight is assigned to the keywords
based on statistical method, the expression of semantic relations between different keywords, the description of document semantic vectors and the similarity calculating, etc. Finally, the experimental results show that the retrieval ability of our new model has significant improvement both on recall and precision.
A kernel PCA-based semantic feature estimation approach for similar image retrieval is presented in this paper. Utilizing database images previously annotated by keywords, the proposed method estimates unknown semantic features of a query image. First, our method performs semantic clustering of the database images and
Through the analysis of the information on the contents of the document which contained in title, abstract and keywords, find out which documents are more relativity with user's retrieval expectation, this paper adopted "document retrieval expected value" as be the indicator, builds the mathematical model for it, and
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