Intelligent Personalized Searching

Search engine is a very useful tool for almost everyone nowadays. People use search engine for the purpose of searching about their personal finance, restaurants, electronic products, and travel information, to name a few. As helpful as search engines are in terms of providing information, they can also manipulate people behaviors because most people trust online information without a doubt. Furthermore, ordinary users usually only pay attention the highest-ranking pages from the search results. Knowing this predictable user behavior, search engine providers such as Google and Yahoo take advantage and use it as a tool for them to generate profit. Search engine providers are enterprise companies with the goal to generate profit, and an easy way for them to do so is by ranking up particular web pages to promote the product or services of their own or their paid customers. The results from search engine could be misleading. The goal of this project is to filter the bias from search results and provide best matches on behalf of users’ interest

A Simplified Latent Semantic Indexing Approach for Multi-Linguistic Information Retrieval

Latent Semantic Indexing (LSI) approach provides a promising solution to overcome the language barrier between queries and documents, but unfortunately the high dimensions of the training matrix is computationally prohibitive for its key step of Singular Value Decomposition (SVD). Based on the semantic parallelism of the multi-linguistic training corpus we prove in this paper that, theoretically if the training term-by-document matrix can appear in either of two symmetry forms, strong or weak, the dimension of the matrix under decomposition can be reduced to the size of a monolingual matrix. The retrieval accuracy will not deteriorate in such a simplification. And we also discuss what these two forms of symmetry mean in the context of multi-linguistic information retrieval. Although in real world data the term-by-document matrices are not naturally in either symmetry form, we suggest a way to make them appear more symmetric in the strong form by means of word clustering and term weighting. A real data experiment is also given to support our method of simplification.

A fast image retrieval method designed for network big data

In the field of big data applications, image information is widely used. The value density of information utilization in big data is very low, and how to extract useful information quickly is very important. So we should transform the unstructured image data source into a form that can be analyzed. In this paper, we proposed a fast image retrieval method which designed for big data. First of all, the feature extraction method is necessary and the feature vectors can be obtained for every image. Then, it is the most important step for us to encode the image feature vectors and make them into database, which can optimize the feature structure. Finally, the corresponding similarity matching is used to determined the retrieval results. There are three main contributions for image retrieval in this paper. New feature extraction method, reasonable elements ranking and appropriate distance metric can improve the algorithm performance. Experiments show that our method has a great improvement in the effective performance of feature extraction and can also get better search matching results