loading reading lists ...
');
function adjustReadingListIcon(isInReadingList){
$readingListToggle.toggleClass("fa-plus", !isInReadingList);
$readingListToggle.toggleClass("fa-check", isInReadingList);
var tooltipMessage = isInReadingList ? "edit in reading lists" : "add to reading list";
$readingListToggle.attr("title", tooltipMessage);
$readingListToggle.attr("data-original-title", tooltipMessage);
}
$.ajax({
url: "/readingList/isInReadingList",
data: {
productId: 2287
}
}).done(function (data) {
adjustReadingListIcon(data && data.hasProductInReadingList);
}).catch(function(e){
console.log(e);
adjustReadingListIcon(false);
});
$readingListToggle.on("click", function(){
if(codePromise == null){ showToast() }
loadCode().then(function(store){
store.requestReadingListSpecificationForProduct({
id: window.readingListsServerVars.externalId,
manningId: window.readingListsServerVars.productId,
title: window.readingListsServerVars.title
});
ReadingLists.ReactDOM.render(
ReadingLists.React.createElement(ReadingLists.ManningOnlineReadingListModal, {
store: store,
}),
document.getElementById("reading-lists-modal")
);
}).catch(function(e){
console.log("Error loading code reading list code");
});
});
var codePromise
var readingListStore
function loadCode(){
if(codePromise) { return codePromise }
return codePromise = new Promise(function (resolve, reject){
$.getScript(window.readingListsServerVars.libraryLocation).done(function(){
hideToast()
readingListStore = new ReadingLists.ReadingListStore(
new ReadingLists.ReadingListProvider(
new ReadingLists.ReadingListWebProvider(
ReadingLists.SourceApp.marketplace,
getDeploymentType()
)
)
);
readingListStore.onReadingListChange(handleChange);
readingListStore.onReadingListModalChange(handleChange);
resolve(readingListStore);
}).catch(function(){
hideToast();
console.log("Error downloading reading lists source");
$readingListToggle.css("display", "none");
reject();
});
});
}
function handleChange(){
if(readingListStore != null) {
adjustReadingListIcon(readingListStore.isInAtLeastOneReadingList({
id: window.readingListsServerVars.externalId,
manningId: window.readingListsServerVars.productId
}));
}
}
var $readingListToast = $("#reading-list-toast");
function showToast(){
$readingListToast.css("display", "flex");
setTimeout(function(){
$readingListToast.addClass("shown");
}, 16);
}
function hideToast(){
$readingListToast.removeClass("shown");
setTimeout(function(){
$readingListToast.css("display", "none");
}, 150);
}
function getDeploymentType(){
switch(window.readingListsServerVars.deploymentType){
case "development":
case "test":
return ReadingLists.DeploymentType.dev;
case "qa":
return ReadingLists.DeploymentType.qa;
case "production":
return ReadingLists.DeploymentType.prod;
case "docker":
return ReadingLists.DeploymentType.docker;
default:
console.error("Unknown deployment environment, defaulting to production");
return ReadingLists.DeploymentType.prod;
}
}
});
}
});
- prerequisites
- basics of Python • basics of pandas • basics of scikit-learn • basics of machine learning
- skills learned
- selecting, cleaning and choosing data for collaborative filtering • neighborhood and model based collaborative filtering techniques with the Surprise library
pro $24.99 per month
- access to all Manning books, MEAPs, liveVideos, liveProjects, and audiobooks!
- choose one free eBook per month to keep
- exclusive 50% discount on all purchases
lite $19.99 per month
- access to all Manning books, including MEAPs!
team
5, 10 or 20 seats+ for your team - learn more
Look inside
In this liveProject, you’ll create a product recommendation engine for an online store using collaborative filtering techniques from the Surprise library. You’ll work with Amazon review datasets to create your data corpus, and identify which would be best for a collaborative filtering recommender. You’ll then use two different approaches—neighbourhood-based and matrix factorization—to implement different solutions to the rating matrix completion problem. You’ll learn how to select and clean the necessary data for these different approaches. When you’re finished, you’ll have built a system that can predict the rating for a product a user has not yet purchased.
This project is designed for learning purposes and is not a complete, production-ready application or solution.
features
Surprise project for free
