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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.
LTR  DNN 
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.
LTR 
june 2018 by foodbaby

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