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	Information Retrieval Kluwer Academic Publishers         Options: Date Reviewed Title Author Publisher Published Date Descending Order Ascending Order     1-10 of 30 reviews Date Reviewed     Website replica detection with distant supervision Carvalho C., Moura E., Veloso A., Ziviani N. Information Retrieval 21(4): 253-272, 2018.  Type: Article Detecting duplicate web pages is of great importance for search engines. This is because duplicates are very costly to index. The work of Carvalho et al. advances the technology for detecting web page duplicates, with the potential to ... Jan 18 2019   The orchestration of a collaborative information seeking learning task Knight S., Rienties B., Littleton K., Tempelaar D., Mitsui M., Shah C. Information Retrieval 20(5): 480-505, 2017.  Type: Article The paper provides a collaborative information seeking (CIS) learning task and definitions and descriptions for CIS, along with studies/examples. The authors examine collaborative, exploratory learning as a well-researched and engaging... Jul 5 2018   Optimizing search results for human learning goals Syed R., Collins-Thompson K. Information Retrieval 20(5): 506-523, 2017.  Type: Article For many students, web search is an important part of the learning process. However, existing search engines are optimized so as to achieve the largest average customer satisfaction among different categories of customers. Because of t... Feb 7 2018   There’s a creepy guy on the other end at Google!: Engaging middle school students in a drawing activity to elicit their mental models of Google Kodama C., St. Jean B., Subramaniam M., Taylor N. Information Retrieval 20(5): 403-432, 2017.  Type: Article Mental models in youth are deeply explored in this paper that investigates the ways in which students (aged 10-14) mentally visualize the inner workings of the Google search engine. The authors delve into considerable depth about the r... Jan 4 2018   Waves: a fast multi-tier top-k query processing algorithm Daoud C., Silva de Moura E., Fernandes D., Soares da Silva A., Rossi C., Carvalho A. Information Retrieval 20(3): 292-316, 2017.  Type: Article Daoud and colleagues propose and demonstrate the effectiveness of their innovative waves algorithm, a fast multitier top-k query processing algorithm. In the waves algorithm, the entire document collection is divided... Dec 28 2017   Performance improvements for search systems using an integrated cache of lists + intersections Tolosa G., Feuerstein E., Becchetti L., Marchetti-Spaccamela A. Information Retrieval 20(3): 172-198, 2017.  Type: Article This work’s title should more aptly start with “Performance improvement for full-text search systems” because it focuses on that area of application. The lengthy paper (25 pages) revisits and expands on pr... Nov 21 2017   Beyond entities: promoting explorative search with bundles Bordino I., Lalmas M., Mejova Y., Van Laere O. Information Retrieval 19(5): 447-486, 2016.  Type: Article Search results are usually ranked lists of documents relevant to query terms. In this paper, the entity search results are bundled with those beyond the query term, by constructing the entity network where extracted entities and pairwi... Jan 25 2017   Opinions matter: a general approach to user profile modeling for contextual suggestion Yang P., Wang H., Fang H., Cai D. Information Retrieval 18(6): 586-610, 2015.  Type: Article Recommender systems heavily rely on modeling accurate user profiles and preferences. For place recommender systems, the authors present the opinion-based user profile model. A user’s rating (like or dislike) groups the review... Feb 26 2016   Pooling-based continuous evaluation of information retrieval systems Tonon A., Demartini G., Cudré-Mauroux P. Information Retrieval 18(5): 445-472, 2015.  Type: Article Web search engines, such as Google and Yahoo, are using information retrieval (IR) applications or systems to answer users’ requests. Using IR systems/applications enables the reduction of information overload in large databa... Jan 25 2016   A term-based methodology for query reformulation understanding Sloan M., Yang H., Wang J. Information Retrieval 18(2): 145-165, 2015.  Type: Article When conducting a search, a user typically reformulates the query after observing the results to maximize the likelihood of satisfying her information needs. An interesting research question is how to model the query term evolution and... Jun 29 2015       Display 510152550100 per column
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