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SDLib

SDLib: A Python library used to collect shilling detection methods. (for academic research)

How to Run it

  • 1.Configure the **xx.conf** file in the directory named config. (xx is the name of the method you want to run)
  • 2.Run the **main.py** in the project, and then input following the prompt.

How to Configure the Detection Method

Essential Options

EntryExampleDescription
ratings../dataset/averageattack/ratings.txtSet the path to the dirty recommendation dataset. Format: each row separated by empty, tab or comma symbol.
label../dataset/averageattack/labels.txtSet the path to labels (for users). Format: each row separated by empty, tab or comma symbol.
ratings.setup-columns 0 1 2-columns: (user, item, rating) columns of rating data are used; -header: to skip the first head line when reading data
MethodNameDegreeSAD/PCASelect/etc.The name of the detection method
evaluation.setup-testSet ../dataset/testset.txtMain option: -testSet, -ap, -cv
-testSet path/to/test/file (need to specify the test set manually)
-ap ratio (ap means that the user set (including items and ratings) are automatically partitioned into training set and test set, the number is the ratio of test set. e.g. -ap 0.2)
-cv k (-cv means cross validation, k is the number of the fold. e.g. -cv 5)
output.setupon -dir ./Results/Main option: whether to output recommendation results
-dir path: the directory path of output results.

How to extend it

  • 1.Make your new algorithm generalize the proper base class.
  • 2.Rewrite some of the following functions as needed.
- readConfiguration()
- printAlgorConfig()
- initModel()
- buildModel()
- saveModel()
- loadModel()
- predict()

How to generate spammers

  • 1.Configure the **xx.conf** file in shillingmodels/config/.
  • 2.Modify /shillingmodels/generateData.py as needed and run it.

How to Configure the Shilling Model

Essential Options

EntryExampleDescription
ratings../dataset/averageattack/ratings.txtSet the path to the recommendation dataset. Format: each row separated by empty, tab or comma symbol.
ratings.setup-columns 0 1 2-columns: (user, item, rating) columns of rating data are used; -header: to skip the first head line when reading data
attackSize0.01The ratio of the injected spammers to genuine users
fillerSize0.01The ratio of the filler items to all items
selectedSize0.001The ratio of the selected items to all items
targetCount20The count of the targeted items
targetScore5.0The score given to the target items
threshold3.0Item has an average score lower than threshold may be chosen as one of the target items
minCount3Item has a ratings count larger than minCount may be chosen as one of the target items
maxCount50Item has a rating count smaller that maxCount may be chosen as one of the target items
outputDir./data/ User profiles and labels will be output here

Implemented Methods

AlgorithmPaper
DegreeSAD李文涛，等，一种基于流行度分类特征的托攻击检测算法， 自动化学报

About

A Python library used to collect shilling detection methods. (for academic research)

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Readme
GPL-3.0 license
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
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SDLib
SDLib: A Python library used to collect shilling detection methods. (for academic research)
How to Run it
  • 1.Configure the **xx.conf** file in the directory named config. (xx is the name of the method you want to run)
  • 2.Run the **main.py** in the project, and then input following the prompt.
How to Configure the Detection Method
Essential Options
 EntryExampleDescription
ratings../dataset/averageattack/ratings.txtSet the path to the dirty recommendation dataset. Format: each row separated by empty, tab or comma symbol. 
label../dataset/averageattack/labels.txtSet the path to labels (for users). Format: each row separated by empty, tab or comma symbol. 
ratings.setup-columns 0 1 2-columns: (user, item, rating) columns of rating data are used;
-header: to skip the first head line when reading data
MethodNameDegreeSAD/PCASelect/etc.The name of the detection method
evaluation.setup-testSet ../dataset/testset.txtMain option: -testSet, -ap, -cv 

-testSet path/to/test/file (need to specify the test set manually)

-ap ratio (ap means that the user set (including items and ratings) are automatically partitioned into training set and test set, the number is the ratio of test set. e.g. -ap 0.2)

-cv k (-cv means cross validation, k is the number of the fold. e.g. -cv 5)
output.setupon -dir ./Results/Main option: whether to output recommendation results

-dir path: the directory path of output results.

How to extend it
  • 1.Make your new algorithm generalize the proper base class.
  • 2.Rewrite some of the following functions as needed.

- readConfiguration()

- printAlgorConfig()

- initModel()

- buildModel()

- saveModel()

- loadModel()

- predict()
How to generate spammers
  • 1.Configure the **xx.conf** file in shillingmodels/config/. 
  • 2.Modify /shillingmodels/generateData.py as needed and run it.
How to Configure the Shilling Model
Essential Options
 EntryExampleDescription
ratings../dataset/averageattack/ratings.txtSet the path to the recommendation dataset. Format: each row separated by empty, tab or comma symbol. 
ratings.setup-columns 0 1 2-columns: (user, item, rating) columns of rating data are used;
-header: to skip the first head line when reading data
attackSize0.01The ratio of the injected spammers to genuine users
fillerSize0.01The ratio of the filler items to all items 
selectedSize0.001The ratio of the selected items to all items 
targetCount20The count of the targeted items 
targetScore5.0The score given to the target items
threshold3.0Item has an average score lower than threshold may be chosen as one of the target items
minCount3Item has a ratings count larger than minCount may be chosen as one of the target items
maxCount50Item has a rating count smaller that maxCount may be chosen as one of the target items
outputDir./data/ User profiles and labels will be output here 
Implemented Methods
AlgorithmPaper
DegreeSAD李文涛，等，一种基于流行度分类特征的托攻击检测算法， 自动化学报
About
A Python library used to collect shilling detection methods. (for academic research)
Resources
Readme
GPL-3.0 license
Activity
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0 watching
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0 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
SDLib
SDLib: A Python library used to collect shilling detection methods. (for academic research)
How to Run it
  • 1.Configure the **xx.conf** file in the directory named config. (xx is the name of the method you want to run)
  • 2.Run the **main.py** in the project, and then input following the prompt.
How to Configure the Detection Method
Essential Options
 EntryExampleDescription
ratings../dataset/averageattack/ratings.txtSet the path to the dirty recommendation dataset. Format: each row separated by empty, tab or comma symbol. 
label../dataset/averageattack/labels.txtSet the path to labels (for users). Format: each row separated by empty, tab or comma symbol. 
ratings.setup-columns 0 1 2-columns: (user, item, rating) columns of rating data are used;
-header: to skip the first head line when reading data
MethodNameDegreeSAD/PCASelect/etc.The name of the detection method
evaluation.setup-testSet ../dataset/testset.txtMain option: -testSet, -ap, -cv 

-testSet path/to/test/file (need to specify the test set manually)

-ap ratio (ap means that the user set (including items and ratings) are automatically partitioned into training set and test set, the number is the ratio of test set. e.g. -ap 0.2)

-cv k (-cv means cross validation, k is the number of the fold. e.g. -cv 5)
output.setupon -dir ./Results/Main option: whether to output recommendation results

-dir path: the directory path of output results.

How to extend it
  • 1.Make your new algorithm generalize the proper base class.
  • 2.Rewrite some of the following functions as needed.

- readConfiguration()

- printAlgorConfig()

- initModel()

- buildModel()

- saveModel()

- loadModel()

- predict()
How to generate spammers
  • 1.Configure the **xx.conf** file in shillingmodels/config/. 
  • 2.Modify /shillingmodels/generateData.py as needed and run it.
How to Configure the Shilling Model
Essential Options
 EntryExampleDescription
ratings../dataset/averageattack/ratings.txtSet the path to the recommendation dataset. Format: each row separated by empty, tab or comma symbol. 
ratings.setup-columns 0 1 2-columns: (user, item, rating) columns of rating data are used;
-header: to skip the first head line when reading data
attackSize0.01The ratio of the injected spammers to genuine users
fillerSize0.01The ratio of the filler items to all items 
selectedSize0.001The ratio of the selected items to all items 
targetCount20The count of the targeted items 
targetScore5.0The score given to the target items
threshold3.0Item has an average score lower than threshold may be chosen as one of the target items
minCount3Item has a ratings count larger than minCount may be chosen as one of the target items
maxCount50Item has a rating count smaller that maxCount may be chosen as one of the target items
outputDir./data/ User profiles and labels will be output here 
Implemented Methods
AlgorithmPaper
DegreeSAD李文涛，等，一种基于流行度分类特征的托攻击检测算法， 自动化学报
About
A Python library used to collect shilling detection methods. (for academic research)
Resources
Readme
GPL-3.0 license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
SDLib
SDLib: A Python library used to collect shilling detection methods. (for academic research)
How to Run it
  • 1.Configure the **xx.conf** file in the directory named config. (xx is the name of the method you want to run)
  • 2.Run the **main.py** in the project, and then input following the prompt.
How to Configure the Detection Method
Essential Options
 EntryExampleDescription
ratings../dataset/averageattack/ratings.txtSet the path to the dirty recommendation dataset. Format: each row separated by empty, tab or comma symbol. 
label../dataset/averageattack/labels.txtSet the path to labels (for users). Format: each row separated by empty, tab or comma symbol. 
ratings.setup-columns 0 1 2-columns: (user, item, rating) columns of rating data are used;
-header: to skip the first head line when reading data
MethodNameDegreeSAD/PCASelect/etc.The name of the detection method
evaluation.setup-testSet ../dataset/testset.txtMain option: -testSet, -ap, -cv 

-testSet path/to/test/file (need to specify the test set manually)

-ap ratio (ap means that the user set (including items and ratings) are automatically partitioned into training set and test set, the number is the ratio of test set. e.g. -ap 0.2)

-cv k (-cv means cross validation, k is the number of the fold. e.g. -cv 5)
output.setupon -dir ./Results/Main option: whether to output recommendation results

-dir path: the directory path of output results.

How to extend it
  • 1.Make your new algorithm generalize the proper base class.
  • 2.Rewrite some of the following functions as needed.

- readConfiguration()

- printAlgorConfig()

- initModel()

- buildModel()

- saveModel()

- loadModel()

- predict()
How to generate spammers
  • 1.Configure the **xx.conf** file in shillingmodels/config/. 
  • 2.Modify /shillingmodels/generateData.py as needed and run it.
How to Configure the Shilling Model
Essential Options
 EntryExampleDescription
ratings../dataset/averageattack/ratings.txtSet the path to the recommendation dataset. Format: each row separated by empty, tab or comma symbol. 
ratings.setup-columns 0 1 2-columns: (user, item, rating) columns of rating data are used;
-header: to skip the first head line when reading data
attackSize0.01The ratio of the injected spammers to genuine users
fillerSize0.01The ratio of the filler items to all items 
selectedSize0.001The ratio of the selected items to all items 
targetCount20The count of the targeted items 
targetScore5.0The score given to the target items
threshold3.0Item has an average score lower than threshold may be chosen as one of the target items
minCount3Item has a ratings count larger than minCount may be chosen as one of the target items
maxCount50Item has a rating count smaller that maxCount may be chosen as one of the target items
outputDir./data/ User profiles and labels will be output here 
Implemented Methods
AlgorithmPaper
DegreeSAD李文涛，等，一种基于流行度分类特征的托攻击检测算法， 自动化学报
About
A Python library used to collect shilling detection methods. (for academic research)
Resources
Readme
GPL-3.0 license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
SDLib
SDLib: A Python library used to collect shilling detection methods. (for academic research)
How to Run it
  • 1.Configure the **xx.conf** file in the directory named config. (xx is the name of the method you want to run)
  • 2.Run the **main.py** in the project, and then input following the prompt.
How to Configure the Detection Method
Essential Options
 EntryExampleDescription
ratings../dataset/averageattack/ratings.txtSet the path to the dirty recommendation dataset. Format: each row separated by empty, tab or comma symbol. 
label../dataset/averageattack/labels.txtSet the path to labels (for users). Format: each row separated by empty, tab or comma symbol. 
ratings.setup-columns 0 1 2-columns: (user, item, rating) columns of rating data are used;
-header: to skip the first head line when reading data
MethodNameDegreeSAD/PCASelect/etc.The name of the detection method
evaluation.setup-testSet ../dataset/testset.txtMain option: -testSet, -ap, -cv 

-testSet path/to/test/file (need to specify the test set manually)

-ap ratio (ap means that the user set (including items and ratings) are automatically partitioned into training set and test set, the number is the ratio of test set. e.g. -ap 0.2)

-cv k (-cv means cross validation, k is the number of the fold. e.g. -cv 5)
output.setupon -dir ./Results/Main option: whether to output recommendation results

-dir path: the directory path of output results.

How to extend it
  • 1.Make your new algorithm generalize the proper base class.
  • 2.Rewrite some of the following functions as needed.

- readConfiguration()

- printAlgorConfig()

- initModel()

- buildModel()

- saveModel()

- loadModel()

- predict()
How to generate spammers
  • 1.Configure the **xx.conf** file in shillingmodels/config/. 
  • 2.Modify /shillingmodels/generateData.py as needed and run it.
How to Configure the Shilling Model
Essential Options
 EntryExampleDescription
ratings../dataset/averageattack/ratings.txtSet the path to the recommendation dataset. Format: each row separated by empty, tab or comma symbol. 
ratings.setup-columns 0 1 2-columns: (user, item, rating) columns of rating data are used;
-header: to skip the first head line when reading data
attackSize0.01The ratio of the injected spammers to genuine users
fillerSize0.01The ratio of the filler items to all items 
selectedSize0.001The ratio of the selected items to all items 
targetCount20The count of the targeted items 
targetScore5.0The score given to the target items
threshold3.0Item has an average score lower than threshold may be chosen as one of the target items
minCount3Item has a ratings count larger than minCount may be chosen as one of the target items
maxCount50Item has a rating count smaller that maxCount may be chosen as one of the target items
outputDir./data/ User profiles and labels will be output here 
Implemented Methods
AlgorithmPaper
DegreeSAD李文涛，等，一种基于流行度分类特征的托攻击检测算法， 自动化学报
About
A Python library used to collect shilling detection methods. (for academic research)
Resources
Readme
GPL-3.0 license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
SDLib
SDLib: A Python library used to collect shilling detection methods. (for academic research)
How to Run it
  • 1.Configure the **xx.conf** file in the directory named config. (xx is the name of the method you want to run)
  • 2.Run the **main.py** in the project, and then input following the prompt.
How to Configure the Detection Method
Essential Options
 EntryExampleDescription
ratings../dataset/averageattack/ratings.txtSet the path to the dirty recommendation dataset. Format: each row separated by empty, tab or comma symbol. 
label../dataset/averageattack/labels.txtSet the path to labels (for users). Format: each row separated by empty, tab or comma symbol. 
ratings.setup-columns 0 1 2-columns: (user, item, rating) columns of rating data are used;
-header: to skip the first head line when reading data
MethodNameDegreeSAD/PCASelect/etc.The name of the detection method
evaluation.setup-testSet ../dataset/testset.txtMain option: -testSet, -ap, -cv 

-testSet path/to/test/file (need to specify the test set manually)

-ap ratio (ap means that the user set (including items and ratings) are automatically partitioned into training set and test set, the number is the ratio of test set. e.g. -ap 0.2)

-cv k (-cv means cross validation, k is the number of the fold. e.g. -cv 5)
output.setupon -dir ./Results/Main option: whether to output recommendation results

-dir path: the directory path of output results.

How to extend it
  • 1.Make your new algorithm generalize the proper base class.
  • 2.Rewrite some of the following functions as needed.

- readConfiguration()

- printAlgorConfig()

- initModel()

- buildModel()

- saveModel()

- loadModel()

- predict()
How to generate spammers
  • 1.Configure the **xx.conf** file in shillingmodels/config/. 
  • 2.Modify /shillingmodels/generateData.py as needed and run it.
How to Configure the Shilling Model
Essential Options
 EntryExampleDescription
ratings../dataset/averageattack/ratings.txtSet the path to the recommendation dataset. Format: each row separated by empty, tab or comma symbol. 
ratings.setup-columns 0 1 2-columns: (user, item, rating) columns of rating data are used;
-header: to skip the first head line when reading data
attackSize0.01The ratio of the injected spammers to genuine users
fillerSize0.01The ratio of the filler items to all items 
selectedSize0.001The ratio of the selected items to all items 
targetCount20The count of the targeted items 
targetScore5.0The score given to the target items
threshold3.0Item has an average score lower than threshold may be chosen as one of the target items
minCount3Item has a ratings count larger than minCount may be chosen as one of the target items
maxCount50Item has a rating count smaller that maxCount may be chosen as one of the target items
outputDir./data/ User profiles and labels will be output here 
Implemented Methods
AlgorithmPaper
DegreeSAD李文涛，等，一种基于流行度分类特征的托攻击检测算法， 自动化学报
About
A Python library used to collect shilling detection methods. (for academic research)
Resources
Readme
GPL-3.0 license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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})();
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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SDLib
SDLib: A Python library used to collect shilling detection methods. (for academic research)
How to Run it
  • 1.Configure the **xx.conf** file in the directory named config. (xx is the name of the method you want to run)
  • 2.Run the **main.py** in the project, and then input following the prompt.
How to Configure the Detection Method
Essential Options
 EntryExampleDescription
ratings../dataset/averageattack/ratings.txtSet the path to the dirty recommendation dataset. Format: each row separated by empty, tab or comma symbol. 
label../dataset/averageattack/labels.txtSet the path to labels (for users). Format: each row separated by empty, tab or comma symbol. 
ratings.setup-columns 0 1 2-columns: (user, item, rating) columns of rating data are used;
-header: to skip the first head line when reading data
MethodNameDegreeSAD/PCASelect/etc.The name of the detection method
evaluation.setup-testSet ../dataset/testset.txtMain option: -testSet, -ap, -cv 

-testSet path/to/test/file (need to specify the test set manually)

-ap ratio (ap means that the user set (including items and ratings) are automatically partitioned into training set and test set, the number is the ratio of test set. e.g. -ap 0.2)

-cv k (-cv means cross validation, k is the number of the fold. e.g. -cv 5)
output.setupon -dir ./Results/Main option: whether to output recommendation results

-dir path: the directory path of output results.

How to extend it
  • 1.Make your new algorithm generalize the proper base class.
  • 2.Rewrite some of the following functions as needed.

- readConfiguration()

- printAlgorConfig()

- initModel()

- buildModel()

- saveModel()

- loadModel()

- predict()
How to generate spammers
  • 1.Configure the **xx.conf** file in shillingmodels/config/. 
  • 2.Modify /shillingmodels/generateData.py as needed and run it.
How to Configure the Shilling Model
Essential Options
 EntryExampleDescription
ratings../dataset/averageattack/ratings.txtSet the path to the recommendation dataset. Format: each row separated by empty, tab or comma symbol. 
ratings.setup-columns 0 1 2-columns: (user, item, rating) columns of rating data are used;
-header: to skip the first head line when reading data
attackSize0.01The ratio of the injected spammers to genuine users
fillerSize0.01The ratio of the filler items to all items 
selectedSize0.001The ratio of the selected items to all items 
targetCount20The count of the targeted items 
targetScore5.0The score given to the target items
threshold3.0Item has an average score lower than threshold may be chosen as one of the target items
minCount3Item has a ratings count larger than minCount may be chosen as one of the target items
maxCount50Item has a rating count smaller that maxCount may be chosen as one of the target items
outputDir./data/ User profiles and labels will be output here 
Implemented Methods
AlgorithmPaper
DegreeSAD李文涛，等，一种基于流行度分类特征的托攻击检测算法， 自动化学报
About
A Python library used to collect shilling detection methods. (for academic research)
Resources
Readme
GPL-3.0 license
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injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
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Skip to content
BranchesTags
Repository files navigation
SDLib
SDLib: A Python library used to collect shilling detection methods. (for academic research)
How to Run it
  • 1.Configure the **xx.conf** file in the directory named config. (xx is the name of the method you want to run)
  • 2.Run the **main.py** in the project, and then input following the prompt.
How to Configure the Detection Method
Essential Options
 EntryExampleDescription
ratings../dataset/averageattack/ratings.txtSet the path to the dirty recommendation dataset. Format: each row separated by empty, tab or comma symbol. 
label../dataset/averageattack/labels.txtSet the path to labels (for users). Format: each row separated by empty, tab or comma symbol. 
ratings.setup-columns 0 1 2-columns: (user, item, rating) columns of rating data are used;
-header: to skip the first head line when reading data
MethodNameDegreeSAD/PCASelect/etc.The name of the detection method
evaluation.setup-testSet ../dataset/testset.txtMain option: -testSet, -ap, -cv 

-testSet path/to/test/file (need to specify the test set manually)

-ap ratio (ap means that the user set (including items and ratings) are automatically partitioned into training set and test set, the number is the ratio of test set. e.g. -ap 0.2)

-cv k (-cv means cross validation, k is the number of the fold. e.g. -cv 5)
output.setupon -dir ./Results/Main option: whether to output recommendation results

-dir path: the directory path of output results.

How to extend it
  • 1.Make your new algorithm generalize the proper base class.
  • 2.Rewrite some of the following functions as needed.

- readConfiguration()

- printAlgorConfig()

- initModel()

- buildModel()

- saveModel()

- loadModel()

- predict()
How to generate spammers
  • 1.Configure the **xx.conf** file in shillingmodels/config/. 
  • 2.Modify /shillingmodels/generateData.py as needed and run it.
How to Configure the Shilling Model
Essential Options
 EntryExampleDescription
ratings../dataset/averageattack/ratings.txtSet the path to the recommendation dataset. Format: each row separated by empty, tab or comma symbol. 
ratings.setup-columns 0 1 2-columns: (user, item, rating) columns of rating data are used;
-header: to skip the first head line when reading data
attackSize0.01The ratio of the injected spammers to genuine users
fillerSize0.01The ratio of the filler items to all items 
selectedSize0.001The ratio of the selected items to all items 
targetCount20The count of the targeted items 
targetScore5.0The score given to the target items
threshold3.0Item has an average score lower than threshold may be chosen as one of the target items
minCount3Item has a ratings count larger than minCount may be chosen as one of the target items
maxCount50Item has a rating count smaller that maxCount may be chosen as one of the target items
outputDir./data/ User profiles and labels will be output here 
Implemented Methods
AlgorithmPaper
DegreeSAD李文涛，等，一种基于流行度分类特征的托攻击检测算法， 自动化学报
About
A Python library used to collect shilling detection methods. (for academic research)
Resources
Readme
GPL-3.0 license
Activity
Stars
0 stars
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors
Languages