ltr   120

« earlier    

New CSS Logical Properties! – Elad Shechter – Medium
about css logical properties: a new way of naming things like padding-left padding-right etc according to reading direction of site
webdesign  web  design  css  logical  properties  rtl  ltr  multi  language  box  model 
11 weeks ago by piperh
Click data as implicit relevance feedback in web search
Search sessions consist of a person presenting a query to a search engine, followed by that person examining the search results, selecting some of those search results for further review, possibly following some series of hyperlinks, and perhaps backtracking to previously viewed pages in the session. The series of pages selected for viewing in a search session, sometimes called the click data, is intuitively a source of relevance feedback information to the search engine. We are interested in how that relevance feedback can be used to improve the search results quality for all users, not just the current user. For example, the search engine could learn which documents are frequently visited when certain search queries are given.

In this article, we address three issues related to using click data as implicit relevance feedback: (1) How click data beyond the search results page might be more reliable than just the clicks from the search results page; (2) Whether we can further subselect from this click data to get even more reliable relevance feedback; and (3) How the reliability of click data for relevance feedback changes when the goal becomes finding one document for the user that completely meets their information needs (if possible). We refer to these documents as the ones that are strictly relevant to the query.

Our conclusions are based on empirical data from a live website with manual assessment of relevance. We found that considering all of the click data in a search session as relevance feedback has the potential to increase both precision and recall of the feedback data. We further found that, when the goal is identifying strictly relevant documents, that it could be useful to focus on last visited documents rather than all documents visited in a search session.
IR  relevance  LTR  click  data 
september 2018 by foodbaby
The Web Is Not Just Left-to-Right, by Chen Hui Jing
This talk traces the parallel history of western and eastern typography from handwriting to the internet age, setting the context for how the web is a brand new medium for typesetting. CSS allows us to implement advanced typographic features and multi-directional layouts, that not only benefits internationalisation, but opens up a myriad of options for creative and interesting layouts in general as well.
fridayfrontend  video  language  rtl  ltr  typography  chinese 
august 2018 by spaceninja
[1702.06106] An Attention-Based Deep Net for Learning to Rank
In information retrieval, learning to rank constructs a machine-based ranking model which given a query, sorts the search results by their degree of relevance or importance to the query. Neural networks have been successfully applied to this problem, and in this paper, we propose an attention-based deep neural network which better incorporates different embeddings of the queries and search results with an attention-based mechanism. This model also applies a decoder mechanism to learn the ranks of the search results in a listwise fashion. The embeddings are trained with convolutional neural networks or the word2vec model. We demonstrate the performance of this model with image retrieval and text querying data sets.
june 2018 by foodbaby
LETOR: A Benchmark Collection for Research on Learning to Rank for Information Retrieval - Microsoft Research
LETOR is a benchmark collection for the research on learning to rank for information retrieval, released by Microsoft Research Asia. In this paper, we describe the details of the LETOR collection and show how it can be used in different kinds of researches. Specifically, we describe how the document corpora and query sets in LETOR are selected, how the documents are sampled, how the learning features and meta information are extracted, and how the datasets are partitioned for comprehensive evaluation. We then compare several state-of-the-art learning to rank algorithms on
LETOR, report their ranking performances, and make discussions on the results. After that, we discuss possible new research topics that can be supported by LETOR, in addition to algorithm comparison. We hope that this paper can help people to gain deeper understanding of LETOR, and enable more interesting research projects on learning to rank and related topics.
june 2018 by foodbaby

« earlier    

related tags

2011  3d  3dprinter  3dprinting  a11y  advice  ai  ala  analysis  annotation  apco  apps  arabic  articles  ask_metafilter_posts  attraction  bandit  bdi  bias  bidi  bidirectional  bidrectional  box  brk  candidate  childfree  chinese  click-models  click  clip  contact  coolidge  corpus  couples  css  culture  customer-service  dark  darkness  data  dataset  dating  design  development  dim  dir  direction  diversity  dnn  drupal  edacs  effect  elephants  encoding  evaluation  example  examples  excitement  expectation  experience  eye  fairpairs  feature-selection  filament  float  fridayfrontend  gadgets  gesture  gizmos  griffey  hack  hebrew  hiding  howto  html  hyperopt  i18n  ifttt  inline-block  interleaving  internationalization  ipad  iphone  ir  it_-_technical  job  joeclark  language  layout  learnin  learning-to-rank  left  libraries  library  librarytechreport  lifestages  linguistics  listnet  logical  love  marriage  matching  mature  maturity  mirror  mirroring  mixins  ml  mlibs  mobile  mobile_phone  model  multi  navigation  offline  online  p25  pairwise  papers  php  pipeline  popularity  portable_project  position  post-pc  printing  programming  properties  psychology  pua  qml  qt  radio  ranking  ranksvm  reading  recsys  red  reddit  reference  relationship  relationships  relevance  relevancy  resources  reverse  right  romance  rtl  sass  scanner  search  secret  slicing  slides  snagajob  solr  spark  stages  stocks  survey  teaching  techsource  tensorflow  testing  text-align  text  themes  tool  topic-modeling  transform  translation  trp  trunk  twitter  typography  uber  ui  unicode  utf  utf8  ux  video  w3c  web  webdesign  webdev  wikimedia  wikipedia  wisdom  wordpress  writing  yahoo 

Copy this bookmark: