Distributed Technologies for Personalized Advertisement Delivery

2011 ◽  
pp. 233-261 ◽  
Author(s):  
Dorothea Tsatsou ◽  
Symeon Papadopoulos ◽  
Ioannis Kompatsiaris ◽  
Paul C. Davis
Keyword(s):  
User Study ◽  
Semantic Representation ◽  
User Preferences ◽  
User Profiles ◽  
Contextual Data ◽  
Uniform Machine ◽  
Textual Data ◽  
Personalized Advertisement ◽  
Distributed Technologies ◽  
The Web

This chapter provides an overview on personalized advertisement delivery paradigms on the web with a focus on the recommendation of advertisements expressed in or accompanied by text. Different methods of online targeted advertising will be examined, while justifying the need for channeling the appropriate ads to the corresponding users. The aim of the work presented here is to illustrate how the semantic representation of ads and user preferences can achieve optimal and unobtrusive ad delivery. We propose a set of distributed technologies that efficiently handles the lack of textual data in ads by enriching ontological knowledge with statistical contextual data in order to classify ads and generic content under a uniform, machine-understandable vocabulary. This classification is used to construct lightweight semantic user profiles, matched with semantic ad descriptions via fuzzy semantic reasoning. A real world user study, as well as an evaluative exploration of framework alternatives validate the system’s effectiveness to produce high quality ad recommendations.

scholarly journals Implementation of a Location Services Based Android Application and Accompanying Server Backend

2021 ◽  
Vol 15 (3) ◽  
pp. 310-317
Author(s):  
Kristijan Lukaček ◽  
Matija Mikac ◽  
Miroslav Horvatić
Keyword(s):  
Mobile Applications ◽  
Mobile Application ◽  
Web Application ◽  
Smart Phone ◽  
Location Information ◽  
Android Application ◽  
Location Services ◽  
Textual Data ◽  
Android Operating System ◽  
The Web

This paper is focused on the usage of location services in mobile applications that were developed for the purpose of reporting different events that are based on their location. The event that is intended to be generic and universal can, as in examples used in this paper, be the reporting of some occurrence to a city’s communal affairs office. Such a generic event can include both multimedia and textual data, in addition to location information obtained using mobile device running the app. The software solution that is described in this paper consists of a mobile application that was developed for the Android operating system and a web application that includes a series of PHP scripts that run on a dedicated server. The web application consists of a backend scripts that facilitate the communication of a smart phone and the server and frontend related scripts used by users and administrators to access and check the data and process the reported events.

Category-Based Interfaces for Mobile Search

2015 ◽  
pp. 75-104
Author(s):  
Tomi Heimonen
Keyword(s):  
User Study ◽  
Information Access ◽  
Future Research ◽  
Information Need ◽  
Research Directions ◽  
Search Results ◽  
Future Research Directions ◽  
Mobile Context ◽  
The Web

One of the challenges with designing effective mobile search interfaces is how to present and explore the search results. Category-based result organization and presentation techniques have been suggested in literature as a complement to the traditional ranked result list. In the mobile context categories can facilitate information access by providing an overview of the result set, by reducing the need for keyword entry and by providing means to filter the results. This chapter includes a review of recent research on category-based interfaces for mobile search. The chapter also addresses the challenges of evaluating mobile search in situ and presents a longitudinal user study that investigated how a mobile clustering interface is used to search the Web. Results from the study show that category-based interaction can be situationally useful, for example when users have problems describing their information need or wish to retrieve a subset of results. In summary, the chapter proposes future research directions for category-based mobile search interfaces.

Application of Sentiment Analysis in Movie reviews

2019 ◽  
pp. 77-90 ◽  
Author(s):  
ThippaReddy Gadekallu ◽  
Akshat Soni ◽  
Deeptanu Sarkar ◽  
Lakshmanna Kuruva
Keyword(s):  
Sentiment Analysis ◽  
Experimental Work ◽  
Opinion Mining ◽  
Rating Systems ◽  
Domain Specific ◽  
Analysis Process ◽  
The People ◽  
Textual Data ◽  
Feature Based ◽  
The Web

Sentiment analysis is a sub-domain of opinion mining where the analysis is focused on the extraction of emotions and opinions of the people towards a particular topic from a structured, semi-structured, or unstructured textual data. In this chapter, the authors try to focus the task of sentiment analysis on IMDB movie review database. This chapter presents the experimental work on a new kind of domain-specific feature-based heuristic for aspect-level sentiment analysis of movie reviews. The authors have devised an aspect-oriented scheme that analyzes the textual reviews of a movie and assign it a sentiment label on each aspect. Finally, the authors conclude that incorporating syntactical information in the models is vital to the sentiment analysis process. The authors also conclude that the proposed approach to sentiment classification supplements the existing rating movie rating systems used across the web and will serve as base to future researches in this domain.

The Next Generation of Personalization Techniques

2011 ◽  
pp. 72-92
Author(s):  
Gulden Uchyigit
Keyword(s):  
Knowledge Representation ◽  
Traditional Method ◽  
Domain Knowledge ◽  
Information Overload ◽  
State Of The Art ◽  
User Preferences ◽  
Future Trends ◽  
User Profiles ◽  
User Interests ◽  
Key Concepts

Coping with today’s unprecedented information overload problem necessitates the deployment of personalization services. Typical personalization approaches model user preferences and store them in user profiles, used to deliver personalized content. A traditional method for profile representation is the so called keyword-based representation, where the user interests are modelled using keywords which are selected from the contents of the items which the user has rated. Although, keyword based approaches are simple and are extensively used for profile representation they fail to represent semantic-based information, this information is lost during the pre-processing phase. Future trends in personalization systems necessitate more innovative personalization techniques that are able to capture rich semanticbased information during the representation, modelling and learning phases. In recent years ontologies (key concepts and along with their interrelationships) to express semantic-based information have been very popular in domain knowledge representation. The primary goal of this chapter is to present an overview of the state-of-the art techniques and methodologies which aim to integrate personalization technologies with semantic-based information.

Enhancing Web Search through Web Structure Mining

2011 ◽  
pp. 443-447
Author(s):  
Ji-Rong Wen
Keyword(s):  
Information Retrieval ◽  
Web Search ◽  
Product Information ◽  
Semantic Representation ◽  
Web Pages ◽  
Search Performance ◽  
Information Display ◽  
Web Structure Mining ◽  
Free Environment ◽  
The Web

The Web is an open and free environment for people to publish and get information. Everyone on the Web can be either an author, a reader, or both. The language of the Web, HTML (Hypertext Markup Language), is mainly designed for information display, not for semantic representation. Therefore, current Web search engines usually treat Web pages as unstructured documents, and traditional information retrieval (IR) technologies are employed for Web page parsing, indexing, and searching. The unstructured essence of Web pages seriously blocks more accurate search and advanced applications on the Web. For example, many sites contain structured information about various products. Extracting and integrating product information from multiple Web sites could lead to powerful search functions, such as comparison shopping and business intelligence. However, these structured data are embedded in Web pages, and there are no proper traditional methods to extract and integrate them. Another example is the link structure of the Web. If used properly, information hidden in the links could be taken advantage of to effectively improve search performance and make Web search go beyond traditional information retrieval (Page, Brin, Motwani, & Winograd, 1998, Kleinberg, 1998).

Summarizing Opinions with Sentiment Analysis from Multiple Reviews on Travel Destinations

2018 ◽  
Vol 9 (2) ◽  
pp. 111-120
Author(s):  
Argha Roy ◽  
Shyamali Guria ◽  
Suman Halder ◽  
Sayani Banerjee ◽  
Sourav Mandal
Keyword(s):  
Social Media ◽  
Sentiment Analysis ◽  
Efficient Method ◽  
Text Analysis ◽  
Human Life ◽  
Analysis Techniques ◽  
Classification Technique ◽  
Textual Data ◽  
The Web

Recently, the web has been crowded with growing volumes of various texts on every aspect of human life. It is difficult to rapidly access, analyze, and compose important decisions using efficient methods for raw textual data in the form of social media, blogs, feedback, reviews, etc., which receive textual inputs directly. It proposes an efficient method for summarization of various reviews of tourists on a specific tourist spot towards analyzing their sentiments towards the place. A classification technique automatically arranges documents into predefined categories and a summarization algorithm produces the exact condensed input such that output is most significant concepts of source documents. Finally, sentiment analysis is done in summarized opinion using NLP and text analysis techniques to show overall sentiment about the spot. Therefore, interested tourists can plan to visit the place do not go through all the reviews, rather they go through summarized documents with the overall sentiment about target place.

scholarly journals ML-CB: Machine Learning Canvas Block

2021 ◽  
Vol 2021 (3) ◽  
pp. 453-473
Author(s):  
Nathan Reitinger ◽  
Michelle L. Mazurek
Keyword(s):  
Machine Learning ◽  
Supervised Learning ◽  
Semantic Representation ◽  
Source Code ◽  
Online Privacy ◽  
Learning Approach ◽  
Learning Models ◽  
One Step ◽  
The Web ◽  
Machine Learning Models

Abstract With the aim of increasing online privacy, we present a novel, machine-learning based approach to blocking one of the three main ways website visitors are tracked online—canvas fingerprinting. Because the act of canvas fingerprinting uses, at its core, a JavaScript program, and because many of these programs are reused across the web, we are able to fit several machine learning models around a semantic representation of a potentially offending program, achieving accurate and robust classifiers. Our supervised learning approach is trained on a dataset we created by scraping roughly half a million websites using a custom Google Chrome extension storing information related to the canvas. Classification leverages our key insight that the images drawn by canvas fingerprinting programs have a facially distinct appearance, allowing us to manually classify files based on the images drawn; we take this approach one step further and train our classifiers not on the malleable images themselves, but on the more-difficult-to-change, underlying source code generating the images. As a result, ML-CB allows for more accurate tracker blocking.

scholarly journals Learning Uniform Semantic Features for Natural Language and Programming Language Globally, Locally and Sequentially

2019 ◽  
Vol 33 ◽  
pp. 5845-5852
Author(s):  
Yudong Zhang ◽  
Wenhao Zheng ◽  
Ming Li
Keyword(s):  
Natural Language ◽  
Programming Language ◽  
Semantic Representation ◽  
Feature Learning ◽  
Feature Space ◽  
General Purpose ◽  
Semantic Features ◽  
Textual Data ◽  
Software Mining ◽  
Sequential Information

Semantic feature learning for natural language and programming language is a preliminary step in addressing many software mining tasks. Many existing methods leverage information in lexicon and syntax to learn features for textual data. However, such information is inadequate to represent the entire semantics in either text sentence or code snippet. This motivates us to propose a new approach to learn semantic features for both languages, through extracting three levels of information, namely global, local and sequential information, from textual data. For tasks involving both modalities, we project the data of both types into a uniform feature space so that the complementary knowledge in between can be utilized in their representation. In this paper, we build a novel and general-purpose feature learning framework called UniEmbed, to uniformly learn comprehensive semantic representation for both natural language and programming language. Experimental results on three real-world software mining tasks show that UniEmbed outperforms state-of-the-art models in feature learning and prove the capacity and effectiveness of our model.

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