Abstract: This paper presents a context-sensitive media similarity search algorithm. One of the central problems regarding media search is the semantic gap between the low-level features computed automatically from media data and the human interpretation of them. This is because the notion of similarity is usually based on high-level abstraction but the low-level features do not sometimes reflect the human perception. Many media search algorithms have used the Minkowski metric to measure similarity between image pairs. However those functions cannot adequately capture the aspects of the characteristics of the human visual system as well as the nonlinear relationships in contextual information given by images in a collection. Our search algorithm tackles this problem by employing a similarity measure and a ranking strategy that reflect the nonlinearity of human perception and contextual information in a dataset. Similarity search in an image database based on this contextual information shows encouraging experimental results.
Abstract: Nowadays social media are important tools for web
resource discovery. The performance and capabilities of web searches
are vital, especially search results from social research paper
bookmarking. This paper proposes a new algorithm for ranking
method that is a combination of similarity ranking with paper posted
time or CSTRank. The paper posted time is static ranking for
improving search results. For this particular study, the paper posted
time is combined with similarity ranking to produce a better ranking
than other methods such as similarity ranking or SimRank. The
retrieval performance of combination rankings is evaluated using
mean values of NDCG. The evaluation in the experiments implies
that the chosen CSTRank ranking by using weight score at ratio 90:10
can improve the efficiency of research paper searching on social
bookmarking websites.