Real-Time Advertising Based On Web Browsing In Telecom Domain

The following section describes Telco Domain Real-time advertising based on browsing use case in terms of :

  • Potential business benefits to earn.
  • Functional use case architecture depicted.
  • Data sources (attributes required).
  • Analytic to be performed,
  • Output to be provided and target systems to be integrated with.

This use case is part of the monetization category. The goal of the use case is to provide a kind of DataMart to either Telecom business parties or external third parties sufficient, relevant and customized information to produce real-time advertising to Telecom end users. The customer targets are all Telecom network end-users.

The customization information to be delivered to advertise are based on several dimensions:

  • Customer characteristics: demographic, telco profile.
  • Customer usage: Telco products or any other interests.
  • Customer time/space identification: location, zoning areas, usage time windows.

Use case requirements are detailed in the description below as “ Targeting method”

  1. Search Engine Targeting:

The telco will use users web history to track what users are looking at and to gather information about them. When a user goes onto a website, their web browsing history will show information of the user, what he or she searched, where they are from, found by the ip address, and then build a profile around them, allowing Telco to easily target ads to the user more specifically.

  1. Content and Contextual Targeting:

This is when advertisers can put ads in a specific place, based on the relative content present. This targeting method can be used across different mediums, for example in an article online, about purchasing homes would have an advert associated with this context, like an insurance ad. This is achieved through an ad matching system which analyses the contents on a page or finds keywords and presents a relevant advert, sometimes through pop-ups.

  1. Technical Targeting

This form of targeting is associated with the user’s own software or hardware status. The advertisement is altered depending on the user’s available network bandwidth, for example if a user is on their mobile phone that has a limited connection, the ad delivery system will display a version of the ad that is smaller for a faster data transfer rate.

  1. Time Targeting:

This type of targeting is centered around time and focuses on the idea of fitting in around people’s everyday lifestyles. For example, scheduling specific ads at a timeframe from 5-7pm, when the

  1. Sociodemographic Targeting:

This form of targeting focuses on the characteristics of consumers, including their age, gender, and nationality. The idea is to target users specifically, using this data about them collected, for example, targeting a male in the age bracket of 18-24. The telco will use this form of targeting by showing advertisements relevant to the user’s individual demographic profile. this can show up in forms of banner ads, or commercial videos.

  1. Geographical and Location-Based Targeting:

This type of advertising involves targeting different users based on their geographic location. IP addresses can signal the location of a user and can usually transfer the location through different cells.

  1. Behavioral Targeting:

This form of targeted advertising is centered around the activity/actions of users and is more easily achieved on web pages. Information from browsing websites can be collected, which finds patterns in users search history.

  1. Retargeting:

Is where advertising uses behavioral targeting to produce ads that follow you after you have looked or purchased are a particular item. Retargeting is where advertisers use this information to ‘follow you’ and try and grab your attention so you do not forget.

  1. Opinions, attitudes, interests, and hobbies:

Psychographic segmentation also includes opinions on gender and politics, sporting and recreational activities, views on the environment and arts and cultural issues.

 
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Outline/Structure of the Case Study

Business benefits:

Functional Use case description:

Data Sources:

Demographic Attributes:

Zain customer attributes:

Telco Service/Product category Profiling attributes:

Interests & Usage Attributes:

Location-Based profiling:

Analytics:

Analytic Models/segmentation/profiling details:

Demographic characteristics segmentation:

Telco customer profiling:

Telco product/Service profiling:

Customer Usage/Interests segmentation:

Current Handset/Network configuration:

Time/Geo-Spatial dimensions segmentation:

Specific targets analytics:

1.Search Engine Targeting:

3. Content and Contextual Targeting:

4. Technical (general) Targeting:

5. Time Targeting:

6. Sociodemographic Targeting:

7.Geographical and Location-Based Targeting:

8. Behavioral Targeting:

9. Retargeting:

Output

Output formats, protocols to be delivered:

Target systems:

Output content for specific targets required:

Learning Outcome

The audience is expected to receive the following pieces of information

1.Use of Data Science in Telecom

2.Use of Data Science in improving overall Telecom operations

Target Audience

People having basic knowledge of Machine Learning algorithm &Telecom Domain

Prerequisites for Attendees

Familiarity with machine learning and Telecom domain.

schedule Submitted 2 months ago

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  • Liked Raunak Bhandari
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    Raunak Bhandari / Ankit Desai / Usha Rengaraju - Knowledge Graph from Natural Language: Incorporating order from textual chaos

    90 Mins
    Workshop
    Advanced

    Intro

    What If I told you that instead of the age-old saying that "a picture is worth a thousand words", it could be that "a word is worth a thousand pictures"?

    Language evolved as an abstraction of distilled information observed and collected from the environment for sophisticated and efficient interpersonal communication and is responsible for humanity's ability to collaborate by storing and sharing experiences. Words represent evocative abstractions over information encoded in our memory and are a composition of many primitive information types.

    That is why language processing is a much more challenging domain and witnessed a delayed 'imagenet' moment.

    One of the cornerstone applications of natural language processing is to leverage the language's inherent structural properties to build a knowledge graph of the world.

    Knowledge Graphs

    Knowledge graph is a form of a rich knowledge base which represents information as an interconnected web of entities and their interactions with each other. This naturally manifests as a graph data structure, where nodes represent entities and the relationship between them are the edges.

    Automatically constructing and leveraging it in an intelligent system is an AI-hard problem, and an amalgamation of a wide variety of fields like natural language processing, information extraction and retrieval, graph algorithms, deep learning, etc.

    It represents a paradigm shift for artificial intelligence systems by going beyond deep learning driven pattern recognition and towards more sophisticated forms of intelligence rooted in reasoning to solve much more complicated tasks.

    To elucidate the differences between reasoning and pattern recognition: consider the problem of computer vision: the vision stack processes an image to detect shapes and patterns in order to identify objects - this is pattern recognition, whereas reasoning is much more complex - to associate detected objects with each other in order to meaningfully describe a scene. For this to be accomplished, a system needs to have a rich understanding of the entities within the scene and their relationships with each other.

    To understand a scene where a person is drinking a can of cola, a system needs to understand concepts like people, that they drink certain liquids via their mouths, liquids can be placed into metallic containers which can be held within a palm to be consumed, and the generational phenomenon that is cola, among others. A sophisticated vision system can then use this rich understanding to fetch details about cola in-order to alert the user of their calorie intake, or to update preferences for a customer. A Knowledge Graph's 'awareness' of the world phenomenons can thus be used to augment a vision system to facilitate such higher order semantic reasoning.

    In production systems though, reasoning may be cast into a pattern recognition problem by limiting the scope of the system for feasibility, but this may be insufficient as the complexity of the system scales or we try to solve general intelligence.

    Challenges in building a Knowledge Graph

    There are two primary challenges towards integrating knowledge graphs in systems: acquisition of knowledge and construction of the graph and effectively leveraging it with robust algorithms to solve reasoning tasks. Creation of the knowledge graph can vary widely depending on the breadth and complexity of the domain - from just manual curation to automatically constructing it by leveraging unstructured/semi-structured sources of knowledge, like books and Wikipedia.

    Many natural language processing tasks are precursors towards building knowledge graphs from unstructured text, like syntactic parsing, information extraction, entity linking, named entity recognition, relationship extraction, semantic parsing, semantic role labeling, entity disambiguation, etc. Open information extraction is an active area of research on extracting semantic triplets of object ('John'), predicate ('eats'), subject ('burger') from plain text, which are used to build the knowledge graph automatically.

    A very interesting approach to this problem is the extraction of frame semantics. Frame semantics relates linguistic semantics to encyclopedic knowledge and the basic idea is that the meaning of a word is linked to all essential knowledge that relates to it, for eg. to understand the word "sell", it's necessary to also know about commercial transactions, which involve a seller, buyer, goods, payment, and the relations between these, which can be represented in a knowledge graph.

    This workshop will focus on building such a knowledge graph from unstructured text.

    Learn good research practices like organizing code and modularizing output for productive data wrangling to improve algorithm performance.

    Knowledge Graph at Embibe

    We will showcase how Embibe's proprietary Knowledge Graph manifests and how it's leveraged across a multitude of projects in our Data Science Lab.

  • Liked Amit  Baldwa
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    Amit Baldwa - PREDICTING AND BEATING THE STOCK MARKET WITH MACHINE LEARNING AND TECHNICAL ANALYSIS

    Amit  Baldwa
    Amit Baldwa
    Director
    Finastra Financial Software
    schedule 2 months ago
    Sold Out!
    45 Mins
    Demonstration
    Intermediate

    Machine learning provides systems the ability to automatically learn and improve from experience without being explicitly programmed.

    Technical analysis shows in graphic form investor sentiment, both greed and fear. Technical analysis attempts to use past stock price and volume information to predict future price movements. Technical analysis of various indicators has been a time-tested strategy for seasoned traders and hedge funds, who have used these techniques to effective turn our profits in Securities Industry.

    Some researchers claim that stock prices conform to the theory of random walk, which is that the future path of the price of a stock is not more predictable than random numbers. However, Stock prices do not follow random walks.

    We will evaluate whether stock returns can be predicted based on historical information.

    Coupled with Machine Learning, we further try to decipher the correlation between the various indicators and identify the set of indicators which appropriately predict the value

  • Liked Siboli mukherjee
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    Siboli mukherjee - Real time Anomaly Detection in Network KPI using Time Series

    20 Mins
    Experience Report
    Intermediate

    Abstract:

    How to accurately detect Key Performance Indicator (KPI) anomalies is a critical issue in cellular network management. In this talk I shall introduce CNR(Cellular Network Regression) a unified performance anomaly detection framework for KPI time-series data. CNR realizes simple statistical modelling and machine-learning-based regression for anomaly detection; in particular, it specifically takes into account seasonality and trend components as well as supports automated prediction model retraining based on prior detection results. I demonstrate here how CNR detects two types of anomalies of practical interest, namely sudden drops and correlation changes, based on a large-scale real-world KPI dataset collected from a metropolitan LTE network. I explore various prediction algorithms and feature selection strategies, and provide insights into how regression analysis can make automated and accurate KPI anomaly detection viable.

    Index Terms—anomaly detection, NPAR (Network Performance Analysis)

    1. INTRODUCTION

    The continuing advances of cellular network technologies make high-speed mobile Internet access a norm. However, cellular networks are large and complex by nature, and hence production cellular networks often suffer from performance degradations or failures due to various reasons, such as back- ground interference, power outages, malfunctions of network elements, and cable disconnections. It is thus critical for network administrators to detect and respond to performance anomalies of cellular networks in real time, so as to maintain network dependability and improve subscriber service quality. To pinpoint performance issues in cellular networks, a common practice adopted by network administrators is to monitor a diverse set of Key Performance Indicators (KPIs), which provide time-series data measurements that quantify specific performance aspects of network elements and resource usage. The main task of network administrators is to identify any KPI anomalies, which refer to unexpected patterns that occur at a single time instant or over a prolonged time period.

    Today’s network diagnosis still mostly relies on domain experts to manually configure anomaly detection rules such a practice is error-prone, labour intensive, and inflexible. Recent studies propose to use (supervised) machine learning for anomaly detection in cellular networks . ellular networks, a common practice adopted by network administrators is to monitor a diverse set of Key Performance Indicators (KPIs), which provide time-series data measurements that quantify specific performance aspects of network elements and resource usage. The main task of network administrators is to identify any KPI anomalies, which refer to unexpected patterns that occur at a single time instant or over a prolonged time period.

    Today’s network diagnosis still mostly relies on domain experts to manually configure anomaly detection rules such a practice is error-prone, labour intensive, and inflexible. Recent studies propose to use (supervised) machine learning for anomaly detection in cellular networks .

  • Liked Joy Mustafi
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    Joy Mustafi / Aditya Bhattacharya - Person Identification via Multi-Modal Interface with Combination of Speech and Image Data

    90 Mins
    Workshop
    Intermediate

    Multi-Modalities

    Having multiple modalities in a system gives more affordance to users and can contribute to a more robust system. Having more also allows for greater accessibility for users who work more effectively with certain modalities. Multiple modalities can be used as backup when certain forms of communication are not possible. This is especially true in the case of redundant modalities in which two or more modalities are used to communicate the same information. Certain combinations of modalities can add to the expression of a computer-human or human-computer interaction because the modalities each may be more effective at expressing one form or aspect of information than others. For example, MUST researchers are working on a personalized humanoid built and equipped with various types of input devices and sensors to allow them to receive information from humans, which are interchangeable and a standardized method of communication with the computer, affording practical adjustments to the user, providing a richer interaction depending on the context, and implementing robust system with features like; keyboard; pointing device; touchscreen; computer vision; speech recognition; motion, orientation etc.

    There are six types of cooperation between modalities, and they help define how a combination or fusion of modalities work together to convey information more effectively.

    • Equivalence: information is presented in multiple ways and can be interpreted as the same information
    • Specialization: when a specific kind of information is always processed through the same modality
    • Redundancy: multiple modalities process the same information
    • Complimentarity: multiple modalities take separate information and merge it
    • Transfer: a modality produces information that another modality consumes
    • Concurrency: multiple modalities take in separate information that is not merged

    Computer - Human Modalities

    Computers utilize a wide range of technologies to communicate and send information to humans:

    • Vision - computer graphics typically through a screen
    • Audition - various audio outputs

    Project Features

    Adaptive: They MUST learn as information changes, and as goals and requirements evolve. They MUST resolve ambiguity and tolerate unpredictability. They MUST be engineered to feed on dynamic data in real time.

    Interactive: They MUST interact easily with users so that those users can define their needs comfortably. They MUST interact with other processors, devices, services, as well as with people.

    Iterative and Stateful: They MUST aid in defining a problem by asking questions or finding additional source input if a problem statement is ambiguous or incomplete. They MUST remember previous interactions in a process and return information that is suitable for the specific application at that point in time.

    Contextual: They MUST understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulation, user profile, process, task and goal. They may draw on multiple sources of information, including both structured and unstructured digital information, as well as sensory inputs (visual, gestural, auditory, or sensor-provided).

    Project Demos

    Multi-Modal Interaction: https://www.youtube.com/watch?v=jQ8Gq2HWxiA

    Gesture Detection: https://www.youtube.com/watch?v=rDSuCnC8Ei0

    Speech Recognition: https://www.youtube.com/watch?v=AewM3TsjoBk

    Assignment (Hands-on Challenge for Attendees)

    Real-time multi-modal access control system for authorized access to work environment - All the key concepts and individual steps will be demonstrated and explained in this workshop, and the attendees need to customize the generic code or approach for this assignment or hands-on challenge.