A computerized method and system operable to build a device identifier similarity model with online event signals and determine similar network device identifiers. A processing circuit receives a first set of network device identifiers. The processing circuit represents each network device identifier of the first set by feature data associated with each network device identifier's network activity, where the feature data is associated with the content clicked-on or converted-on. The processing circuit applies abstractions on the feature data to form concepts. The processing circuit derives at least one hierarchy of feature data based on the keywords and concepts of the feature data. The processing circuit expands the feature data based on the derived at least one hierarchy of feature data and generates the device identifier similarity model based on the expanded feature data. The processing circuit is also capable of determining long-term and short-term history events.

Device identifier similarity models derived from online event signals

A method of setting a threshold similarity score value for a first plurality of network user identifiers. The first plurality of network user identifiers, a second plurality of network user identifiers and characteristic data associated with the network user identifiers is received. A performance target and an experimental threshold similarity score value are designated. A similarity score between the first and second plurality of network user identifiers is calculated. Performance statistics data for each of the second plurality of network user identifiers having a similarity score greater than or equal to the experimental threshold similarity score value is collected and compared to the similarity score of the network user identifier. Based on the comparison, the experimental threshold similarity score value is adjusted to a similarity score value that achieves the performance target and the threshold similarity score value is set to the adjusted experimental threshold similarity score value.

Adjust similar users identification based on performance feedback

A computerized method of creating a classifier model to determine whether a network user should be added to a list of similar network users. A first list of network users, a second list of network users who are not on the first list of network users and characteristic data associated with the network users' network activity are received. A time period is designated. A first category of network activities performed by a network user on the first list within the designated time period is identified. A second category of network activities performed at any time by network users on the second list is identified. A third category of network activities performed by network users on the first list at a time outside of the designated time period is identified. The classifier model is generated based on the first, second and third categories of network activities.

Creating a classifier model to determine whether a network user should be added to a list

A computerized method, system for, and computer-readable medium operable to select an algorithm for generating models configured to identify similar user identifiers. A first plurality of models generated by a first algorithm is received. A plurality of lists of similar user identifiers is generated. User queries associated with user identifiers on the plurality of lists of similar user identifiers are identified. Predicted click-through rates for the user queries is received. An average predicted click-through rate is computed for each model based on the predicted click-through rates. A weighted average predicted click-through rate associated with the first plurality of models is computed. The weighted average predicted click-through rate for the first plurality of models can be compared to a weighted average predicted click-through rate for a second plurality of models generated by a second algorithm. The algorithm for generating models is selected based on the comparison.

Selecting an algorithm for identifying similar user identifiers based on predicted click-through-rate

Statistical method, statistical system and statistical device for transmission health degree

A method and a system evaluate a company website's online marketing efficiency on various advertising websites by evaluating both access activities and business activities of users originating from the advertising websites. For each user visiting the company website, the method determines the originating website from which the user enters the company website, and obtains the access activities and the business activities of the user on the company website. The method quantifies the access activities and the business activities of the users who enter the present website from the same originating website to generate evaluation data corresponding to the originating website, and evaluates the company website's online marketing efficiency on the originating website based on the evaluation data of the originating website. Using the disclosed method and system, online marketing efficiency can be evaluated more comprehensively and accurately.

Evaluating online marketing efficiency

The invention discloses a method and a system for measuring the network marketing effect, which aim at solving the problem of incomplete and inaccurate measurement of the network marketing effect in the prior art. The method comprises the steps: determining that a user enters a source website of a current website, capturing access operation behaviors and business operation behaviors of the user onthe current website; quantifying the access operation behaviors and the business operation behaviors of the user into assessment data corresponding to the user and the source website thereof; and measuring network marketing effects of the current website on different source websites according to the assessment data of all users entering the current website from different source websites. By adopting the invention, the network marketing effect can be more completely and accurately measured.

Method and system for measuring network marketing effect

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Bing Yang Hua

Author Tools

Chat about Author

Papers

Evaluating online marketing efficiency

Method and system for measuring network marketing effect