Biblio

The present age of digital information has presented a heterogeneous online environment which makes it a formidable mission for a noble user to search and locate the required online resources timely. Recommender systems were implemented to rescue this information overload issue. However, majority of recommendation algorithms focused on the accuracy of the recommendations, leaving out other important aspects in the definition of good recommendation such as diversity and serendipity. This results in low coverage, long-tail items often are left out in the recommendations as well. In this paper, we present and explore a recommendation technique that ensures that diversity, accuracy and serendipity are all factored in the recommendations. The proposed algorithm performed comparatively well as compared to other algorithms in literature.

Automatic face recognition techniques applied on particular group or mass database introduces error cases. Error prevention is crucial for the court. Reranking of recognition results based on anthropology analysis can significant improve the accuracy of automatic methods. Previous studies focused on manual facial comparison. This paper proposed a weighted facial similarity computing method based on morphological analysis of components characteristics. Search sequence of face recognition reranked according to similarity, while the interference terms can be removed. Within this research project, standardized photographs, surveillance videos, 3D face images, identity card photographs of 241 male subjects from China were acquired. Sequencing results were modified by modeling selected individual features from the DMV altas. The improved method raises the accuracy of face recognition through anthroposophic or morphologic theory.