Technology

Technology Overview

Predicting User Intent

Dating back to early 2000, the Kontera founding team sought to answer a fundamental question – how could we provide users relevant and useful information without the user needing to navigate to a search destination, nor needing to type search queries to find it? This was and still is the fundamental quest behind Kontera’s vision: connecting the web’s information.

The answer to this question is what we call today the Third Generation of Online Information Discovery. The first generation was developed using human mapped categorical directories. The second generation uses search engines where users type keywords that represent the information they are seeking in order to receive links to sites that relate to those keywords. Both methods require active thought and effort in order to find the information that would satisfy the user’s quest.

Kontera delivers Third Generation Information Discovery solutions using Kontera’s Synapse Platform as the foundation.

This platform delivers relevant information to web users, as they visit websites, by performing several unique functions, in real-time, as web-pages are served to users:

Synapse Platform:

1. Real-Time Semantic Analysis: Understanding the true and detailed meaning of web content that any given user is reading, by using sophisticated, real-time semantic analysis.
2. Audience Reaction Engine Predicting users’ intent and interests while they interact with the content, continually testing different hypotheses to validate and modify it’s predictions.
3. Identifying overall consumer trends and interests by examining how users are interacting with similar content across the entire web, on thousands of web-sites.
4. Topical Clustering and Targeting: Kontera Synapse automatically identifies thousands of concepts and expanded phrases that are relevant to users’ topics of interest. Topical-targeting provides a platform for a new class of related information and related search applications.

There are three key “engines” that utilize the Kontera’s Synapse analysis:

• Data Engine - Retrieves key information about users’ interests and what information they are currently seeking.
• Engagement Engine – Delivers relevant information and engages users with and within web-content, video-content, social context, and mobile environments.
• Monetization Engine: Maximizing Advertiser results and Publisher yield from their content

Kontera’s Synapse Platform also includes a full suite of brand safety controls. These controls ensure that advertisers are not proximate to content that is harmful to their brand.

Relevance - Accuracy - Interest

The Kontera Synapse Platform performs the following process, in a split of a second, for every page:

• Extraction: A typical contextual analysis process begins by extracting all the relevant publisher and page content and attributes, including: text, HTML properties, structure, location on page, URL, Title, Meta tags, custom Meta tags, etc. Every such feature has a weight used by the machine learning algorithms that analyze the data.
• Discovery: using Natural Language Processing, Machine Learning, and other proprietary linguistic, semantic, and statistical algorithms, keyword phrases are discovered and classified based on semantic meaning and potential semantic relationships.
• Page classification: using a proprietary Dynamic Taxonomy, that continues to expand and refine autonomously, Topical classes and Clusters are dynamically computed for the given page. In addition, the page sensitivity, sentiment and commercial value are analyzed.
• Information Clustering: Kontera Synapse uses several proprietary content extraction and classification engines that scour the web continuously for the most up to date relevant content, information, and contextual ads. Each information type, such as articles, blog posts, videos, ads, etc., is analyzed differently in order to ensure maximum relevancy. The potential matches are scored relatively to the page and the keywords phrases that were discovered on the page.
• Selection: Out of a potential pool of tens of keyword phrases and hundreds of ads and other related content objects, typically three to five keyword phrases are selected together with the best matching information and relevant ads. This selection will rotate automatically over time due to the dynamic nature of online content and the system’s self-learning optimization algorithms.
• Online Learning & Optimization: The online learning and optimization module automatically performs yield management, optimization and tuning. This real-time analysis of users’ interaction with specific keywords, contextual advertising, and information as they relate to specific web sites, pages and topics is used to increase relevancy, user satisfaction, publisher yield, and advertiser results from Kontera’s different products..

Kontera's research, software development, user experience, design, and engineering teams continue to develop advanced information solutions that combine text analysis algorithms using natural language processing, machine learning, and predictive models with cutting edge design and user interaction elements. One of the interesting innovations that was developed as a by-product of Kontera's content classification and meaning analysis was the Real Time Interest Index that dynamically discovers and surfaces the most interesting concepts online. Another exciting development are Kontera's mobile solutions for delivering relevant information to mobile users who are consuming web content on the go.