The paper is concerned with the design and the evaluation of the combination of user interaction and informative content features for implicit and pseudo feedback-based document re-ranking. The features are observed during the visit of the top-ranked documents returned in response to a query. Experiments on a TREC Web test collection have been carried out and the experimental results are illust...

The main challenge of a search engine is ranking web documents to provide the best response to a user`s query. Despite the huge number of the extracted results for user`s query, only a small number of the first results are examined by users; therefore, the insertion of the related results in the first ranks is of great importance. In this paper, a ranking algorithm based on the reinforcement le...

Document similarity search aims to find documents similar to a query document in a text corpus and return a ranked list of similar documents. Most existing approaches to document similarity search compute similarity scores between the query and the documents based on a retrieval function (e.g. Cosine) and then rank the documents by their similarity scores. In this paper, we proposed a novel ret...

We present an approach to improving the precision of an initial document ranking wherein we utilize cluster information within a graph-based framework. The main idea is to perform re-ranking based on centrality within bipartite graphs of documents (on one side) and clusters (on the other side), on the premise that these are mutually reinforcing entities. Links between entities are created via c...

When two terms occur together in a document, the probability of a close relationship between them and the document itself is greater if they are in nearby positions. However, ranking functions including term proximity (TP) require larger indexes than traditional document-level indexing, which slows down query processing. Previous studies also show that this technique is not effective for all ty...

The Knowledge Media Institute of the Open University participated in the entity ranking and entity list completion tasks of the Entity Ranking Track in INEX 2007. In both the entity ranking and entity list completion tasks, we have considered document features in addition to a basic document content based relevance model. These document features include categorizations of documents, relevance o...

Manifold ranking has been successfully applied in query-oriented multi-document summarization. It not only makes use of the relationships among sentences, but also between given query and sentences. However, information original is often insufficient. So we present a expansion method, which combined manifold to resolve this problem. Our method utilizes term itself knowledge base WordNet expand ...