Nullege: A Search Engine for Python source code. Alexksikes/fSphinx - GitHub. Faceted Search With Sphinx. I decided to use the Sphinx search engine for the GeeQe iPhone app I build last year because it was fast and had a very small memory footprint.
Recently I wanted to experiment with a search interface that had facets and wondered if I would need to move away from Sphinx to something like Solr. As it turns out Sphinx can do faceted search almost as well as Solr can. The first half of what follows contains instructions on how to get Sphinx ready for faceted searches. If you are familiar with setting up Sphinx and already have data indexed by Sphinx you may find it better to skip to the second half after reading the intro to faceted search. You have almost certainly seen faceted searching or faceted browsing already. In each of the above cases you will find the left hand navigation displaying the facets. One of the ways facets are useful is the ability to drill down into the results and create filters based on the facets. If you haven't already, now is the time to install Sphinx. Guest Post: Information Retrieval using a Bayesian Model of Learning and Generalization.
Dinesh Vadhia, CEO and founder of “item search” company Xyggy, has been an active member of the Noisy Community for at least a year, and it is with pleasure that I publish this guest post by him, University of Cambridge / CMU Professor Zoubin Ghahramani, and University of Cambridge / Gatsby Computational Neuroscience Unit researcher Katherine Heller.
I’ve annotated the post with Wikipedia links in the hope of making it more accessible to readers without a background in statistics or machine learning. People are very good at learning new concepts after observing just a few examples. For instance, a child will confidently point out which animals are “dogs” after having seen only a couple of examples of dogs before in their lives. This ability to learn concepts from examples and to generalize to new items is one of the cornerstones of intelligence. By contrast, search services currently on the internet exhibit little or no learning and generalization. The score Automatically learns. Faceted search. Facets correspond to properties of the information elements.
They are often derived by analysis of the text of an item using entity extraction techniques or from pre-existing fields in a database such as author, descriptor, language, and format. Thus, existing web-pages, product descriptions or online collections of articles can be augmented with navigational facets. Development[edit] The Association for Computing Machinery's Special Interest Group on Information Retrieval provided the following description of the role of faceted search for a 2006 workshop: The web search world, since its very beginning, has offered two paradigms:Navigational search uses a hierarchy structure (taxonomy) to enable users to browse the information space by iteratively narrowing the scope of their quest in a predetermined order, as exemplified by Yahoo!
Projects[edit] The most notable academic efforts in faceted search are the following: Mass market use[edit] Online retail[edit] Libraries[edit] See also[edit] Alexksikes/mass-scraping - GitHub. Faceted Navigation : A robot guided navigation and drill down search. Tree for buzzmap in buzzmap. Facet.