We present an information filtering and adaptive personalisation algorithm for arbitrary information systems based on databases. This algorithm is called GRAS (Gaussian Rating Adaptation Scheme), and it combines content-based and collaborative filtering. The goal is to filter retrieved documents of a query according to the personal interest of a user and to sort them according to the personal r...

In spite of the rapid growth of mobile multimedia contents market, most of the customers experience inconvenience, lengthy search processes and frustration in searching for the specific multimedia contents they want. These difficulties are attributable to the current mobile Internet service method based on inefficient sequential search. To overcome these difficulties, this paper proposes a MOBI...

MPEG-7 prescribes a format for semantic content models for multimedia to ensure interoperability across a multitude of platforms and application domains. However, the standard leaves open how the models should be used and how their content should be filtered. Filtering is a technique used to retrieve only content relevant to user requirements, thereby reducing the necessary content-sifting effo...

* Contact author Abstract  In recent years, an extensive research in the area of Content-Based Image Retrieval (CBIR) has been focused on Relevance Feedback (RF) techniques to improve the retrieval of images. In relevance feedback systems, a search engine dynamically updates the weights of various visual features in the query based on the user’s measure of retrieved images’ (ir)relevance. In t...

This paper presents an approach for content-based image retrieval via isotropic and anisotropic mappings. Isotropic mappings are defined as mappings invariant to the action of the planar Euclidean group on the image space – invariant to the translation, rotation and reflection of image data, and hence, invariant to orientation and position. Anisotropic mappings, on the other hand, are defined a...

Recommendation systems for rating movies and forming opinion have grown exponentially in popularity, they make it very convenient consumers to select films that suit their tastes. However, traditional recommendation often rely solely on user ratings or reviews, which may not accurately reflect the user's true feelings about a movie. To address this issue, sentiment analysis has been proposed as...

We propose a modular platform to support the development of personalized filtering systems. According our proposal, filtering systems can be constructed through the integration of different modules and changes on specific parameters. We also introduce a hybrid approximation to improve filtering performance based on the combination of content and collaborative filtering, which suppress weakness ...

Effective Web content filtering is a necessity in educational and workplace environments, but current approaches are far from perfect. We discuss a model for text-based intelligent Web content filtering, in which shallow linguistic analysis plays a key role. In order to demonstrate how this model can be realized, we have developed a lexical Named Entity Recognition system, and used it to improv...

recommender systems (rs) provide personalized recommendation according to user need by analyzing behavior of users and gathering their information. one of the algorithms used in recommender systems is user-based collaborative filtering (cf) method. the idea is that if users have similar preferences in the past, they will probably have similar preferences in the future. the important part of col...

Recommender systems are widely used in modern business. Most recommendation algorithms are based on collaborative filtering. In this paper, we study different ways to incorporate content information directly into the matrix completion approach of collaborative filtering. These content-boosted matrix completion algorithms can achieve better recommendation accuracy.