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Dynamic pruning strategies for information retrieval systems can increase querying efficiency without decreasing effectiveness by using upper bounds to safely omit scoring documents that are unlikely to make the final retrieved set. Often, such...
Machine-learned ranking functions have shown successes in Web search engines. With the increasing demands on developing effective ranking functions for different search domains, we have seen a big bottleneck, that is, the problem of insufficient...
GRAS: An effective and efficient stemming algorithm for information retrieval
Jiaul H. Paik, Mandar Mitra, Swapan K. Parui, Kalervo Järvelin
Article No.: 19
A novel graph-based language-independent stemming algorithm suitable for information retrieval is proposed in this article. The main features of the algorithm are retrieval effectiveness, generality, and computational efficiency. We test our...
Recommendation systems with complex constraints: A course recommendation perspective
Aditya Parameswaran, Petros Venetis, Hector Garcia-Molina
Article No.: 20
We study the problem of making recommendations when the objects to be recommended must also satisfy constraints or requirements. In particular, we focus on course recommendations: the courses taken by a student must satisfy requirements (e.g.,...
Correlation-based retrieval for heavily changed near-duplicate videos
Jiajun Liu, Zi Huang, Heng Tao Shen, Bin Cui
Article No.: 21
The unprecedented and ever-growing number of Web videos nowadays leads to the massive existence of near-duplicate videos. Very often, some near-duplicate videos exhibit great content changes, while the user perceives little information change, for...
Query modeling for entity search based on terms, categories, and examples
Krisztian Balog, Marc Bron, Maarten De Rijke
Article No.: 22
Users often search for entities instead of documents, and in this setting, are willing to provide extra input, in addition to a series of query terms, such as category information and example entities. We propose a general probabilistic framework...