Do czego może przydać się tie w Dismax’ie ?

Rafał Kuć Solr , , , 0 Comment

Dismax parser obecny jest w Solr od niepamiętnych czasów. W większości wypadków jednak korzystamy z parameterów takich, jak qf, pf, czy mm zapominając zupełnie o bardzo przydatnym parametrze pozwalającym kontrolować wpływ mniej ważnych pól na score dokumentu, czyli o parametrze tie.

Tie, czyli co ?

Parametr tie pozwala kontrolować, jak mocny wpływ na score mają pola, które otrzymały score mniejszy od najwyższego. W przypadku ustawienia parametru tie na wartość 0.0, podczas liczenia score będą brane pod uwagę tylko i wyłącznie te pola, które otrzymały najwyższy score. W przypadku ustawienia tego parametru na wartość 0.99, pola, które mają score mniejszy od najwyższego będą traktowane prawie identycznie, jak pole o najwyższym wyliczonym score. Sprawdźmy zatem, czy jest to prawda.

Struktura danych i ich przykład

Do testów wybrałem sobie bardzo prostą strukturę indeksu, która teoretycznie mogłaby przedstawiać produkty w sklepie internetowym, oczywiście w ogromnym uproszczeniu: 

<field name="id" type="string" indexed="true" stored="true" required="true" />
<field name="title" type="text_ws" indexed="true" stored="true" />
<field name="description" type="text_ws" indexed="true" stored="true" />
<field name="author" type="text_ws" indexed="true" stored="true" multiValued="true" />

Typ text_ws został zdefiniowany w następujący sposób: 

<fieldType name="text_ws" class="solr.TextField" positionIncrementGap="100">
 <analyzer>
  <tokenizer class="solr.WhitespaceTokenizerFactory"/>
  <filter class="solr.LowerCaseFilterFactory"/>
 </analyzer>
</fieldType>

Natomiast przykładowe dane wyglądają w następujący sposób: 

<add>
 <doc>
  <field name="id">1</field>
  <field name="title">First test book</field>
  <field name="description">This is a description of the first test book by Joe and Jane Blow</field>
  <field name="author">Joe Blow</field>
  <field name="author">Jane Blow</field>
 </doc>
 <doc>
  <field name="id">2</field>
  <field name="title">Second test book</field>
  <field name="description">This is a description of the second test book by Joe Blow</field>
  <field name="author">Joe Blow</field>
 </doc>
</add>

Rezultat z tie == 0.01

Zacznijmy więc testy. Na pierwszy ogień idzie następujące zapytanie: 

defType=dismax&qf=title^1000 description author^10&tie=0.01&fl=id,score&debugQuery=on&indent=true&q=joe blow book

Powyższe skutkuje następującymi wynikami zwróconymi przez Solr (wizualizacja – http://explain.solr.pl/explains/cf0wnkpj): 

<?xml version="1.0" encoding="UTF-8"?>
<response>
<lst name="responseHeader">
  <int name="status">0</int>
  <int name="QTime">8</int>
  <lst name="params">
    <str name="fl">id,score</str>
    <str name="debugQuery">on</str>
    <str name="indent">true</str>
    <str name="tie">0.01</str>
    <str name="q">joe blow book</str>
    <str name="qf">title^1000 description author^10</str>
    <str name="defType">dismax</str>
  </lst>
</lst>
<result name="response" numFound="2" start="0" maxScore="0.07342677">
  <doc>
    <float name="score">0.07342677</float>
    <str name="id">2</str>
  </doc>
  <doc>
    <float name="score">0.073365316</float>
    <str name="id">1</str>
  </doc>
</result>
<lst name="debug">
  <str name="rawquerystring">joe blow book</str>
  <str name="querystring">joe blow book</str>
  <str name="parsedquery">+((DisjunctionMaxQuery((author:joe^10.0 | title:joe^1000.0 | description:joe)~0.01) DisjunctionMaxQuery((author:blow^10.0 | title:blow^1000.0 | description:blow)~0.01) DisjunctionMaxQuery((author:book^10.0 | title:book^1000.0 | description:book)~0.01))~3) ()</str>
  <str name="parsedquery_toString">+(((author:joe^10.0 | title:joe^1000.0 | description:joe)~0.01 (author:blow^10.0 | title:blow^1000.0 | description:blow)~0.01 (author:book^10.0 | title:book^1000.0 | description:book)~0.01)~3) ()</str>
  <lst name="explain">
    <str name="2">
0.07342677 = (MATCH) sum of:
  0.07342677 = (MATCH) sum of:
    8.957935E-4 = (MATCH) max plus 0.01 times others of:
      8.9543534E-4 = (MATCH) weight(author:joe^10.0 in 1), product of:
        0.0024097771 = queryWeight(author:joe^10.0), product of:
          10.0 = boost
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0532142E-4 = queryNorm
        0.3715843 = (MATCH) fieldWeight(author:joe in 1), product of:
          1.0 = tf(termFreq(author:joe)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.625 = fieldNorm(field=author, doc=1)
      3.5817415E-5 = (MATCH) weight(description:joe in 1), product of:
        2.4097772E-4 = queryWeight(description:joe), product of:
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0532142E-4 = queryNorm
        0.14863372 = (MATCH) fieldWeight(description:joe in 1), product of:
          1.0 = tf(termFreq(description:joe)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.25 = fieldNorm(field=description, doc=1)
    8.957935E-4 = (MATCH) max plus 0.01 times others of:
      8.9543534E-4 = (MATCH) weight(author:blow^10.0 in 1), product of:
        0.0024097771 = queryWeight(author:blow^10.0), product of:
          10.0 = boost
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0532142E-4 = queryNorm
        0.3715843 = (MATCH) fieldWeight(author:blow in 1), product of:
          1.0 = tf(termFreq(author:blow)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.625 = fieldNorm(field=author, doc=1)
      3.5817415E-5 = (MATCH) weight(description:blow in 1), product of:
        2.4097772E-4 = queryWeight(description:blow), product of:
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0532142E-4 = queryNorm
        0.14863372 = (MATCH) fieldWeight(description:blow in 1), product of:
          1.0 = tf(termFreq(description:blow)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.25 = fieldNorm(field=description, doc=1)
    0.07163518 = (MATCH) max plus 0.01 times others of:
      0.07163482 = (MATCH) weight(title:book^1000.0 in 1), product of:
        0.2409777 = queryWeight(title:book^1000.0), product of:
          1000.0 = boost
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0532142E-4 = queryNorm
        0.29726744 = (MATCH) fieldWeight(title:book in 1), product of:
          1.0 = tf(termFreq(title:book)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.5 = fieldNorm(field=title, doc=1)
      3.5817415E-5 = (MATCH) weight(description:book in 1), product of:
        2.4097772E-4 = queryWeight(description:book), product of:
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0532142E-4 = queryNorm
        0.14863372 = (MATCH) fieldWeight(description:book in 1), product of:
          1.0 = tf(termFreq(description:book)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.25 = fieldNorm(field=description, doc=1)
</str>
    <str name="1">
0.073365316 = (MATCH) sum of:
  0.073365316 = (MATCH) sum of:
    7.1670645E-4 = (MATCH) max plus 0.01 times others of:
      7.163483E-4 = (MATCH) weight(author:joe^10.0 in 0), product of:
        0.0024097771 = queryWeight(author:joe^10.0), product of:
          10.0 = boost
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0532142E-4 = queryNorm
        0.29726744 = (MATCH) fieldWeight(author:joe in 0), product of:
          1.0 = tf(termFreq(author:joe)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.5 = fieldNorm(field=author, doc=0)
      3.5817415E-5 = (MATCH) weight(description:joe in 0), product of:
        2.4097772E-4 = queryWeight(description:joe), product of:
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0532142E-4 = queryNorm
        0.14863372 = (MATCH) fieldWeight(description:joe in 0), product of:
          1.0 = tf(termFreq(description:joe)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.25 = fieldNorm(field=description, doc=0)
    0.0010134276 = (MATCH) max plus 0.01 times others of:
      0.0010130694 = (MATCH) weight(author:blow^10.0 in 0), product of:
        0.0024097771 = queryWeight(author:blow^10.0), product of:
          10.0 = boost
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0532142E-4 = queryNorm
        0.42039964 = (MATCH) fieldWeight(author:blow in 0), product of:
          1.4142135 = tf(termFreq(author:blow)=2)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.5 = fieldNorm(field=author, doc=0)
      3.5817415E-5 = (MATCH) weight(description:blow in 0), product of:
        2.4097772E-4 = queryWeight(description:blow), product of:
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0532142E-4 = queryNorm
        0.14863372 = (MATCH) fieldWeight(description:blow in 0), product of:
          1.0 = tf(termFreq(description:blow)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.25 = fieldNorm(field=description, doc=0)
    0.07163518 = (MATCH) max plus 0.01 times others of:
      0.07163482 = (MATCH) weight(title:book^1000.0 in 0), product of:
        0.2409777 = queryWeight(title:book^1000.0), product of:
          1000.0 = boost
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0532142E-4 = queryNorm
        0.29726744 = (MATCH) fieldWeight(title:book in 0), product of:
          1.0 = tf(termFreq(title:book)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.5 = fieldNorm(field=title, doc=0)
      3.5817415E-5 = (MATCH) weight(description:book in 0), product of:
        2.4097772E-4 = queryWeight(description:book), product of:
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0532142E-4 = queryNorm
        0.14863372 = (MATCH) fieldWeight(description:book in 0), product of:
          1.0 = tf(termFreq(description:book)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.25 = fieldNorm(field=description, doc=0)
    </str>
  </lst>
</lst>
</response>

Pierwszy dokument

Drugi dokument

Czyli jak to wygląda ?

Jak widać w przypadku przekazania wartości 0.01 do parametru tie pola, które mają najwyższy score, mają największe znaczenie, z niedużym znaczeniem innych pól. Widać to dobrze na przykładzie słowa book w pierwszym dokumencie na liście wyników. Score dla tego słowa, to 0.07163518, na który został wyliczony jako suma pola, które ma najwyższy score dla tego słowa, czyli pola title oraz wartości score reszty pól pomnożonych przez wartość tie, czyli 0.01.

Rezultat z tie == 0.99

Drugie zapytanie wygląda w następujący sposób: 

defType=dismax&qf=title^1000 description author^10&tie=0.99&fl=id,score&debugQuery=on&indent=true&q=joe blow book

Wyniki zwrócone przez Solr (wizualizacja – http://explain.solr.pl/explains/1w7b06lv): 

<?xml version="1.0" encoding="UTF-8"?>
<response>
<lst name="responseHeader">
  <int name="status">0</int>
  <int name="QTime">15</int>
  <lst name="params">
    <str name="fl">id,score</str>
    <str name="debugQuery">on</str>
    <str name="indent">true</str>
    <str name="tie">0.99</str>
    <str name="q">joe blow book</str>
    <str name="qf">title^1000 description author^10</str>
    <str name="defType">dismax</str>
  </lst>
</lst>
<result name="response" numFound="2" start="0" maxScore="0.07352995">
  <doc>
    <float name="score">0.07352995</float>
    <str name="id">2</str>
  </doc>
  <doc>
    <float name="score">0.0734685</float>
    <str name="id">1</str>
  </doc>
</result>
<lst name="debug">
  <str name="rawquerystring">joe blow book</str>
  <str name="querystring">joe blow book</str>
  <str name="parsedquery">+((DisjunctionMaxQuery((author:joe^10.0 | title:joe^1000.0 | description:joe)~0.99) DisjunctionMaxQuery((author:blow^10.0 | title:blow^1000.0 | description:blow)~0.99) DisjunctionMaxQuery((author:book^10.0 | title:book^1000.0 | description:book)~0.99))~3) ()</str>
  <str name="parsedquery_toString">+(((author:joe^10.0 | title:joe^1000.0 | description:joe)~0.99 (author:blow^10.0 | title:blow^1000.0 | description:blow)~0.99 (author:book^10.0 | title:book^1000.0 | description:book)~0.99)~3) ()</str>
  <lst name="explain">
    <str name="2">
0.07352995 = (MATCH) sum of:
  0.07352995 = (MATCH) sum of:
    9.308678E-4 = (MATCH) max plus 0.99 times others of:
      8.9540955E-4 = (MATCH) weight(author:joe^10.0 in 1), product of:
        0.0024097078 = queryWeight(author:joe^10.0), product of:
          10.0 = boost
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0530972E-4 = queryNorm
        0.3715843 = (MATCH) fieldWeight(author:joe in 1), product of:
          1.0 = tf(termFreq(author:joe)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.625 = fieldNorm(field=author, doc=1)
      3.581638E-5 = (MATCH) weight(description:joe in 1), product of:
        2.4097077E-4 = queryWeight(description:joe), product of:
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0530972E-4 = queryNorm
        0.14863372 = (MATCH) fieldWeight(description:joe in 1), product of:
          1.0 = tf(termFreq(description:joe)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.25 = fieldNorm(field=description, doc=1)
    9.308678E-4 = (MATCH) max plus 0.99 times others of:
      8.9540955E-4 = (MATCH) weight(author:blow^10.0 in 1), product of:
        0.0024097078 = queryWeight(author:blow^10.0), product of:
          10.0 = boost
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0530972E-4 = queryNorm
        0.3715843 = (MATCH) fieldWeight(author:blow in 1), product of:
          1.0 = tf(termFreq(author:blow)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.625 = fieldNorm(field=author, doc=1)
      3.581638E-5 = (MATCH) weight(description:blow in 1), product of:
        2.4097077E-4 = queryWeight(description:blow), product of:
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0530972E-4 = queryNorm
        0.14863372 = (MATCH) fieldWeight(description:blow in 1), product of:
          1.0 = tf(termFreq(description:blow)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.25 = fieldNorm(field=description, doc=1)
    0.071668215 = (MATCH) max plus 0.99 times others of:
      0.07163276 = (MATCH) weight(title:book^1000.0 in 1), product of:
        0.24097076 = queryWeight(title:book^1000.0), product of:
          1000.0 = boost
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0530972E-4 = queryNorm
        0.29726744 = (MATCH) fieldWeight(title:book in 1), product of:
          1.0 = tf(termFreq(title:book)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.5 = fieldNorm(field=title, doc=1)
      3.581638E-5 = (MATCH) weight(description:book in 1), product of:
        2.4097077E-4 = queryWeight(description:book), product of:
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0530972E-4 = queryNorm
        0.14863372 = (MATCH) fieldWeight(description:book in 1), product of:
          1.0 = tf(termFreq(description:book)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.25 = fieldNorm(field=description, doc=1)
</str>
    <str name="1">
0.0734685 = (MATCH) sum of:
  0.0734685 = (MATCH) sum of:
    7.517859E-4 = (MATCH) max plus 0.99 times others of:
      7.1632763E-4 = (MATCH) weight(author:joe^10.0 in 0), product of:
        0.0024097078 = queryWeight(author:joe^10.0), product of:
          10.0 = boost
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0530972E-4 = queryNorm
        0.29726744 = (MATCH) fieldWeight(author:joe in 0), product of:
          1.0 = tf(termFreq(author:joe)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.5 = fieldNorm(field=author, doc=0)
      3.581638E-5 = (MATCH) weight(description:joe in 0), product of:
        2.4097077E-4 = queryWeight(description:joe), product of:
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0530972E-4 = queryNorm
        0.14863372 = (MATCH) fieldWeight(description:joe in 0), product of:
          1.0 = tf(termFreq(description:joe)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.25 = fieldNorm(field=description, doc=0)
    0.0010484984 = (MATCH) max plus 0.99 times others of:
      0.0010130403 = (MATCH) weight(author:blow^10.0 in 0), product of:
        0.0024097078 = queryWeight(author:blow^10.0), product of:
          10.0 = boost
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0530972E-4 = queryNorm
        0.42039964 = (MATCH) fieldWeight(author:blow in 0), product of:
          1.4142135 = tf(termFreq(author:blow)=2)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.5 = fieldNorm(field=author, doc=0)
      3.581638E-5 = (MATCH) weight(description:blow in 0), product of:
        2.4097077E-4 = queryWeight(description:blow), product of:
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0530972E-4 = queryNorm
        0.14863372 = (MATCH) fieldWeight(description:blow in 0), product of:
          1.0 = tf(termFreq(description:blow)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.25 = fieldNorm(field=description, doc=0)
    0.071668215 = (MATCH) max plus 0.99 times others of:
      0.07163276 = (MATCH) weight(title:book^1000.0 in 0), product of:
        0.24097076 = queryWeight(title:book^1000.0), product of:
          1000.0 = boost
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0530972E-4 = queryNorm
        0.29726744 = (MATCH) fieldWeight(title:book in 0), product of:
          1.0 = tf(termFreq(title:book)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.5 = fieldNorm(field=title, doc=0)
      3.581638E-5 = (MATCH) weight(description:book in 0), product of:
        2.4097077E-4 = queryWeight(description:book), product of:
          0.5945349 = idf(docFreq=2, maxDocs=2)
          4.0530972E-4 = queryNorm
        0.14863372 = (MATCH) fieldWeight(description:book in 0), product of:
          1.0 = tf(termFreq(description:book)=1)
          0.5945349 = idf(docFreq=2, maxDocs=2)
          0.25 = fieldNorm(field=description, doc=0)
    </str>
  </lst>
</lst>
</response>

Pierwszy dokument

Drugi dokument

Czyli jak to wygląda ?

Jak widać, score poszczególnych dokumentów uległ zmianie. Spójrzmy na ten sam dokument i te same słowo book. W przypadku, kiedy przekazaliśmy wartość 0.99 jako wartość parametru tie, wartość score zwiększyła się w stosunku do tej, którą obserwowaliśmy w przypadku tie równego 0.01. Oczywiście na zmianę wartości score ma także wpływ współczynnik normalizacji, ale pomińmy go w celu uproszczenia 🙂 Zatem w drugim przypadku obserwujemy score wynoszący 0.071668215, składa się na to wartość score dla pola title oraz suma wartości score dla każdego z pozostałych pól, ale tym razem pomnożonych przez wartość 0.99.

Podsumowując

Jak widać, parametr tie pozwala nam na dość dużą kontrolę tego, jak wyliczany jest score w przypadku DisjunctionMaxQuery. W ekstremalnym przypadku, kiedy chcielibyśmy, aby tylko pola, które mają najwyższy score miały znaczenie, możemy ustawić parametr tie na wartość 0.0. Tie pozwala nam na kontrolę tego, czy chcemy, aby mało znaczące pola, po których wyszukujemy, wpływały na wartość score dokumentów, a tym samym na ich pozycję na liście wyników wyszukiwania kiedy korzystamy z Dismax’a.

Na koniec

Jeżeli zastanawiasz się z pomocą czego wygenerowaliśmy wykresy, które widoczne są w treści, zapraszamy na http://explain.solr.pl/help, może http://explain.solr.pl/ będzie przydatne w Twoim przypadku.

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We collect information from you when you register on our site or place an order. When ordering or registering on our site, as appropriate, you may be asked to enter your: name, e-mail address or mailing address.

What do we use your information for?

Any of the information we collect from you may be used in one of the following ways: To personalize your experience (your information helps us to better respond to your individual needs) To improve our website (we continually strive to improve our website offerings based on the information and feedback we receive from you) To improve customer service (your information helps us to more effectively respond to your customer service requests and support needs) To process transactions Your information, whether public or private, will not be sold, exchanged, transferred, or given to any other company for any reason whatsoever, without your consent, other than for the express purpose of delivering the purchased product or service requested. To administer a contest, promotion, survey or other site feature To send periodic emails The email address you provide for order processing, will only be used to send you information and updates pertaining to your order.

How do we protect your information?

We implement a variety of security measures to maintain the safety of your personal information when you place an order or enter, submit, or access your personal information. We offer the use of a secure server. All supplied sensitive/credit information is transmitted via Secure Socket Layer (SSL) technology and then encrypted into our Payment gateway providers database only to be accessible by those authorized with special access rights to such systems, and are required to?keep the information confidential. After a transaction, your private information (credit cards, social security numbers, financials, etc.) will not be kept on file for more than 60 days.

Do we use cookies?

Yes (Cookies are small files that a site or its service provider transfers to your computers hard drive through your Web browser (if you allow) that enables the sites or service providers systems to recognize your browser and capture and remember certain information We use cookies to help us remember and process the items in your shopping cart, understand and save your preferences for future visits, keep track of advertisements and compile aggregate data about site traffic and site interaction so that we can offer better site experiences and tools in the future. We may contract with third-party service providers to assist us in better understanding our site visitors. These service providers are not permitted to use the information collected on our behalf except to help us conduct and improve our business. If you prefer, you can choose to have your computer warn you each time a cookie is being sent, or you can choose to turn off all cookies via your browser settings. Like most websites, if you turn your cookies off, some of our services may not function properly. However, you can still place orders by contacting customer service. Google Analytics We use Google Analytics on our sites for anonymous reporting of site usage and for advertising on the site. If you would like to opt-out of Google Analytics monitoring your behaviour on our sites please use this link (https://tools.google.com/dlpage/gaoptout/)

Do we disclose any information to outside parties?

We do not sell, trade, or otherwise transfer to outside parties your personally identifiable information. This does not include trusted third parties who assist us in operating our website, conducting our business, or servicing you, so long as those parties agree to keep this information confidential. We may also release your information when we believe release is appropriate to comply with the law, enforce our site policies, or protect ours or others rights, property, or safety. However, non-personally identifiable visitor information may be provided to other parties for marketing, advertising, or other uses.

Registration

The minimum information we need to register you is your name, email address and a password. We will ask you more questions for different services, including sales promotions. Unless we say otherwise, you have to answer all the registration questions. We may also ask some other, voluntary questions during registration for certain services (for example, professional networks) so we can gain a clearer understanding of who you are. This also allows us to personalise services for you. To assist us in our marketing, in addition to the data that you provide to us if you register, we may also obtain data from trusted third parties to help us understand what you might be interested in. This ‘profiling’ information is produced from a variety of sources, including publicly available data (such as the electoral roll) or from sources such as surveys and polls where you have given your permission for your data to be shared. You can choose not to have such data shared with the Guardian from these sources by logging into your account and changing the settings in the privacy section. After you have registered, and with your permission, we may send you emails we think may interest you. Newsletters may be personalised based on what you have been reading on theguardian.com. At any time you can decide not to receive these emails and will be able to ‘unsubscribe’. Logging in using social networking credentials If you log-in to our sites using a Facebook log-in, you are granting permission to Facebook to share your user details with us. This will include your name, email address, date of birth and location which will then be used to form a Guardian identity. You can also use your picture from Facebook as part of your profile. This will also allow us and Facebook to share your, networks, user ID and any other information you choose to share according to your Facebook account settings. If you remove the Guardian app from your Facebook settings, we will no longer have access to this information. If you log-in to our sites using a Google log-in, you grant permission to Google to share your user details with us. This will include your name, email address, date of birth, sex and location which we will then use to form a Guardian identity. You may use your picture from Google as part of your profile. This also allows us to share your networks, user ID and any other information you choose to share according to your Google account settings. If you remove the Guardian from your Google settings, we will no longer have access to this information. If you log-in to our sites using a twitter log-in, we receive your avatar (the small picture that appears next to your tweets) and twitter username.

Children’s Online Privacy Protection Act Compliance

We are in compliance with the requirements of COPPA (Childrens Online Privacy Protection Act), we do not collect any information from anyone under 13 years of age. Our website, products and services are all directed to people who are at least 13 years old or older.

Updating your personal information

We offer a ‘My details’ page (also known as Dashboard), where you can update your personal information at any time, and change your marketing preferences. You can get to this page from most pages on the site – simply click on the ‘My details’ link at the top of the screen when you are signed in.

Online Privacy Policy Only

This online privacy policy applies only to information collected through our website and not to information collected offline.

Your Consent

By using our site, you consent to our privacy policy.

Changes to our Privacy Policy

If we decide to change our privacy policy, we will post those changes on this page.
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