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https://issues.apache.org/jira/browse/PIO-195?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16713829#comment-16713829
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ASF GitHub Bot commented on PIO-195:
------------------------------------
dszeto closed pull request #497: [PIO-195] Improve readability and grammar
URL: https://github.com/apache/predictionio/pull/497
This is a PR merged from a forked repository.
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diff --git a/docs/manual/source/templates/recommendation/quickstart.html.md.erb
b/docs/manual/source/templates/recommendation/quickstart.html.md.erb
index b5dae35ef..8ad2ff395 100644
--- a/docs/manual/source/templates/recommendation/quickstart.html.md.erb
+++ b/docs/manual/source/templates/recommendation/quickstart.html.md.erb
@@ -37,12 +37,12 @@ By default, the template requires the following events to
be collected:
- user 'rate' item events
- user 'buy' item events
-NOTE: You can customize to use other event.
+NOTE: You can customize this engine to use other events.
### Input Query
- user ID
-- num of recommended items
+- number of recommended items
### Output PredictedResult
@@ -67,7 +67,7 @@ Next, let's collect some training data. By default,
the Recommendation Engine Template supports 2 types of events: **rate** and
**buy**. A user can give a rating score to an item or buy an item. This
template requires user-view-item and user-buy-item events.
-INFO: This template can easily be customized to consider more user events such
as *like*, *dislike* etc.
+INFO: This template can easily be customized to consider more user events such
as *like*, *dislike*, etc.
<%= partial 'shared/quickstart/collect_data' %>
@@ -258,8 +258,8 @@ client.createEvent(buyEvent);
<%= partial 'shared/quickstart/import_sample_data' %>
-A Python import script `import_eventserver.py` is provided in the template to
import the data to
-Event Server using Python SDK. Please upgrade to the latest Python SDK.
+A Python import script `import_eventserver.py` is provided in the template to
import the data to the
+Event Server using the Python SDK. Please upgrade to the latest Python SDK.
<%= partial 'shared/quickstart/install_python_sdk' %>
@@ -284,7 +284,7 @@ Now the movie ratings data is stored as events inside the
Event Store.
<%= partial 'shared/quickstart/query_eventserver_short' %>
-INFO: By default, the template train the model with "rate" events (explicit
rating). You can customize the engine to [read other custom
events](/templates/recommendation/reading-custom-events/) and [handle events of
implicit preference (such as, view,
buy)](/templates/recommendation/training-with-implicit-preference/)
+INFO: By default, the template trains the model with "rate" events (explicit
rating). You can customize the engine to [read other custom
events](/templates/recommendation/reading-custom-events/) and [handle events of
implicit preference (such as, view,
buy)](/templates/recommendation/training-with-implicit-preference/)
## 5. Deploy the Engine as a Service
@@ -294,12 +294,12 @@ INFO: By default, the template train the model with
"rate" events (explicit rati
## 6. Use the Engine
-Now, You can try to retrieve predicted results. To recommend 4 movies to user
+Now, you can try to retrieve predicted results. To recommend 4 movies to a user
whose id is 1, you send this JSON `{ "user": "1", "num": 4 }` to the deployed
-engine and it will return a JSON of the recommended movies. Simply send a query
-by making a HTTP request or through the `EngineClient` of an SDK.
+engine and it will return a JSON result of the recommended movies. Simply send
a query
+by making an HTTP request or through the `EngineClient` of an SDK.
-With the deployed engine running, open another terminal and run the following
`curl` command or use SDK to send the query:
+With the deployed engine running, open another terminal and run the following
`curl` command or use an SDK to send the query:
<div class="tabs">
<div data-tab="REST API" data-lang="json">
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> Improve readability and grammar of documentation
> ------------------------------------------------
>
> Key: PIO-195
> URL: https://issues.apache.org/jira/browse/PIO-195
> Project: PredictionIO
> Issue Type: Improvement
> Components: Documentation
> Affects Versions: 0.13.0
> Reporter: Takako Shimamoto
> Priority: Major
>
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