Абстрактный

A Novel Approach to Ranking On Data Manifold With Sink Points

A. Mahesh. M.E. , G.R.Sumithra

Ranking of documents has various applications in data mining, information retrieval and natural language processing. Many approaches are proposed to rank documents according to their measure of importance or relevance. In addition to importance and relevance of ranked documents, diversity is also recognized as an important criterion in ranking. Top ranked documents in traditional approaches contain redundant information, which is not desired by users. In order to address diversity, relevance, novelty and importance of ranked documents, a new novel approach named Manifold Ranking with Sink Points is proposed. The Manifold ranking process finds the most important and relevant data objects very efficiently. The ranked objects are converted to sink points in data manifold and the redundant objects are prevented from receiving a high rank. The Manifold Ranking with Sink Points approach has good convergence property and also satisfies optimization explanation. MRSP is applied on two applications: query recommendation and update summarization, where diversity is of important concern in ranking. Experimental results present that MRSP has strong empirical performance than traditional approaches like MMR and DivRank.