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I want to build ... something (website? app? tool of some variety?) that searches other sites -- such as Amazon -- for specific items and then lists whether or not those items exist. Ideally it could also pull prices, but that's secondary.
I'd like to be able to enter a (very specific, an identification number) search term into the thing that I build and then have the thing return whether or not the searched item exists on the sites that it checks (a predetermined list). I'd also like it to take a list of ID numbers and search them all at once.
I have no idea where to begin. Can anyone point me in the right direction? What do I need to learn to make this happen?
You will need to learn a few key languages in order to start working on a program like this.
PHP: you need a server side language to skim the site
Javascript: For the input on the users side
HTML: to implement the javascript
Once you learn the basics, search stackoverflow for specific questions relating to a specific problem.
This is certainly a too broad question, but as OP asks to point in some direction here are few suggesstions-
Well this seems to be a big projects. You'll need to find if there is some official api given the other sites from where you want to fetch the product info, if yes use the api to retrieve the product info or else use web scraping where you retrieve the data by parsing the page and storing into your local database.
Amazon provides EC2 instances, where you can hourly rent specific configuration server as needed e.g. Linux with apache/mysql/php, or linux/java.
Amazon has a set of other tools like the S3 storage where you can host your images/docs/video and link them on the site.
Hope this helps in someway.
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I'm trying to develop a comment box like application, similar to Facebook, using MERN stack. Adding simple comments is not an issue. But how does the tagging mechanism works where we tag some user with '#' symbol?
Do I need to store the comment in MongoDb database as some special structure which makes it easy to display the tagged user in react? Or it is achieved with some different technique?
Edit: The question is what roles will the UI and API play, how the code flow will be, and what additional things (eg reference section in comment-document) should be part of the Mongo Database to make this work?
It is unclear if you want to know the database semantics or the frontend part as your question is very broad. But here are some pointers;
Frontend: you can use onKeyDown event on a textarea to figure out what char was typed. If it was a # you can show the user a dropdown of users and reduce that if the user keeps on typing.
Backend: once submitted, you want to find all the #user mentions. This can be done with regex to find everything that starts with a #. E.g e regex like [\s\.]?#(\w)\s would give you all the mentions within a sentence. You then would find the user in your database and add a reference to the comment entry in your database.
Database: once you know the user, you may want to notify the mentioned user, add to inbox etc. There are many different techniques to achieve this and it really depends on your requirements and database of choice and your schema. But generally speaking there are two widely used techniques called Fan out on Read or Fan out on Write
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What are some ways including machine learning that I can use in my projects to generate things related to another. Like related apps, related websites, related products, etc.
I've been brainstorming these are strategies...
one way i can think of is show items from same category. But that would be too broad.
2nd way improves upon previous step, it's to keep track of what people click next and promote that item. Meanwhile keep bottom list randomized to let other relevant items show up and get clicked.
3rd way is to use machine learning and provide training data somehow and use that.
I want something simple but smart, as it gets better with time.
Collaborative filtering is designed for solving exactly this problem. The problem with this approach is that produces good results having a lot of data only. I mean... A LOT. And it's not a really simple thing to use. However, any machine learning technique is not simple. There are some node.js packages for CF available, but I have no idea how good are they.
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I'm pretty new on search engines and pretty newbie on machine learning. But I wanted to know if there is a way to combine functionalities of search engines like elasticsearch or Apache Solr and machine learning project like Apache Mahout, H2O or PredictionIO.
For exemple, if you work on a travel website where you can search for a destination. You start type "au", so the first suggestions are "AUstria", "AUstralia", "mAUrice island", "mAUritania"... etc... This is typically what elasticsearch can do.
But you know that this user has already travelled on Mauritania three times, so you want that Mauritania goes on the first place of suggestions. And I guess that's typically what machine learning can do.
Is there bridges between this two type of technologies ? Can machine learning ensure the work of search engine efficiently ?
I'm open to all answers, regardless of the technologies used. If you have ever experienced this type of problems, my ears are wide open :-)
Thank you
Your question is very general in nature- so my answer will have to be the same.
Consider a recommender framework such as the one in Apache Mahout correlated co-occurance. Unlike the vanilla spark recommender, this implementation allows for multiple types of actions, such as viewed a web site, booked a trip their before, demographic information, etc.
Now you would calculate the recommendations for each user at whatever interval. Recommendations being based on multiple criteria and what other people similar to this user has done. Consider your 'items' in this case to be every destination in the world. So we now have every possible destination ranked for each user.
It is then a trivial extension to index elastic search by user/the ordered list of that users recommended destinations.
For example, we have a user who has visited Berlin, looked at several hotels in Vienna, and is from Romainia. When the user types in "au", we would expect to see "Austria" come up in the results much higher than 'Austrailia'
Per the comments and down votes- you probably should have either A) asked a more specific programming question or B) asked this question on another forum such as Data Science Stack Exchange, fyi
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The scenario is this:
My company has 2000 customers, and we need to send the all 2000 customers a credit application via PDF. What complicates this is that certain parts of the PDF need to have customer data filled in before the form is sent to the customer. The data that needs to be filled in is currently inside of an Excel array.
As you probably guessed, what I want to do is write a script that takes the Excel array and for each row of data the script will fill it into the PDF form, save a copy of it, print the document, and repeat until all rows have been filled into their respective PDF forms.
My questions are as follows:
Is there one particular programming language (or framework) that is particularly well suited for this, and does it have a low learning curve? (I only know enough to write basic JavaScript at the moment)
In the recommended language/framework, what specifically will I need to learn? (aside from the basics like print, for each, if statements)
Are there any particular or general GOTCHAs I should watch out for in writing the script? Keep in mind, this will be the first computer script I will have ever created, so even basic/elementary GOTCHAs can come into play due to my total lack of experience.
EDIT
I should probably specify that I would prefer to write this script in Python if it is at all possible, simply because of all the good things I have heard about it so far.
There are applications that might be able to do this without programming. Here's one that costs $249
http://www.pureforms.com/Products/PFPrintMerge/pfprintmerge.htm
I have never used it -- but there are plenty of products if you search google. If you decide to go the programming route -- you need a PDF SDK for whatever language/framework you choose. There are many to choose from for .NET and Java.
EDIT: You asked for python. See this other SO question.
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I'm writing a small helper utility for obscure software that is used at a local shop. Basically, I would like to know if anyone searches for anything associated with that software and if publishing my work on the Internet would make any sense. I entered the name of the software into Google Trends, but my terms "do not have enough search volume to show graphs" despite the fact that Google lists 250,000 results for the software name, or 35,000 if I explicitly remove terms such as serial and warez from the search.
Does anyone know of alternatives to Google Trends? Or of another way to find out if people search for a particular keyword?
I found what I was looking for.
Google AdWords Keyword Tool
Yahoo Clues is a service similar to Google Trends. But I don't think it's as effective for any category that is non-entertainment.
If you don't get an answer here, another place to ask might be The Business of Software.
Google Trends was also telling me there wasn't enough data for my query. I found Google Insights to do job nicely. And unlike the AdWords tool mentioned in the author's answer, it actually shows a trend.
Here's an example which shows the emergence of 3 terms with too low of volume to show up on Trends: #bigdata, #datascientist & #datajournalism.
Here's a related SO question.