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  Browse All Reviews > Information Systems (H) > Information Storage And Retrieval (H.3) > Information Search And Retrieval (H.3.3) > Retrieval Models (H.3.3...)  
 
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  1-10 of 204 Reviews about "Retrieval Models (H.3.3...)": Date Reviewed
   Incorporating system-level objectives into recommender systems
Abdollahpouri H.  WWW 2019 (Companion Proceedings of the 2019 World Wide Web Conference, San Francisco, CA,  May 13-17, 2019) 2-6, 2019. Type: Proceedings

This paper proposes and evaluates two algorithms for recommendation systems. Most recommendation systems concentrate on optimizing one primary metric, for example, the advantage of a consumer purchasing an item or minimizing the cost of operations...

Jan 20 2022
  Modeling information retrieval by formal logic: a survey
Abdulahhad K., Berrut C., Chevallet J., Pasi G.  ACM Computing Surveys 52(1): 1-37, 2019. Type: Article

As the title indicates, formal logic is used for modeling information retrieval (IR). Readers can expect a literature review (of IR models), supported with graphs, mathematical formulas, and examples that lead to some interesting conclusions....

Apr 12 2021
  Neural graph collaborative filtering
Wang X., He X., Wang M., Feng F., Chua T.  SIGIR 2019 (Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Paris, France,  Jul 21-25, 2019) 165-174, 2019. Type: Proceedings

Typically, collaborative filtering (CF) is simply a nearest neighbor (NN) algorithm used either in its original form or in machine learning (ML), especially in supervised learning, to predict user preferences in recommender systems. Here, neural g...

Oct 14 2020
  Concepts and methods for a librarian of the web
Kubek M.,  Springer International Publishing, New York, NY, 2020. 173 pp. Type: Book (978-3-030231-35-4)

“A librarian of the web” is an interesting proposition that might, when fully implemented, help people find documents they are looking for in a more effective way. The first two chapters start by introducing library services, the key r...

Mar 4 2020
  Interactive recommendation with user-specific deep reinforcement learning
Lei Y., Li W.  ACM Transactions on Knowledge Discovery from Data 13(6): 1-15, 2019. Type: Article

Recommender systems are widely used, especially by online applications with a view to enhancing user experience. In most conventional systems, past history of a user’s implicit online behavior is used to derive a new recommendation. By enabl...

Mar 2 2020
   Natural language processing for social media (2nd ed.)
Farzindar A., Inkpen D., Cohen S.,  Morgan&Claypool Publishers, San Rafael, CA, 2018. 196 pp. Type: Book (978-1-681736-12-9)

As social networks gain widespread popularity, they dominate all other forms of communication, particularly among youth (but of course not only). Perhaps this is because of real-time information and opinion exchange; another reason seems to be a v...

Aug 19 2019
  Predicting information retrieval performance
Losee R.,  Morgan&Claypool Publishers, San Rafael, CA, 2019. 79 pp. Type: Book (978-1-681734-72-9)

As any Internet user knows, searching is a major activity. Given the size of the total data content available, it is amazing how quickly various search engines are able to provide results. Search providers want to understand the performance of the...

Apr 10 2019
  Hybrid textual-visual relevance learning for content-based image retrieval
Cui C., Lin P., Nie X., Yin Y., Zhu Q.  Journal of Visual Communication and Image Representation 48 367-374, 2017. Type: Article

Current content-based image retrieval (CBIR) methods are inefficient due to several fundamental problems such as (1) the sparsity and reliability of tags, (2) the representation of image semantics, and (3) the fusion of textual and visual relevanc...

Apr 11 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 this, search ...

Feb 7 2018
  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 previous work aimed at inc...

Nov 21 2017
 
 
 
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