scholarly journals Report on the SIGIR 2019 Workshop on eCommerce (ECOM19)

2019 ◽  
Vol 53 (2) ◽  
pp. 11-19
Author(s):  
Jon Degenhardt ◽  
Surya Kallumadi ◽  
Utkarsh Porwal ◽  
Andrew Trotman
Keyword(s):  
Information Retrieval ◽  
Real World ◽  
Poster Session ◽  
Panel Discussion ◽  
Product Search ◽  
Full Day

The SIGIR 2019 Workshop on eCommerce (ECOM19), was a full day workshop that took place on Thursday, July 25, 2019 in Paris, France. The purpose of the workshop was to serve as a platform for publication and discussion of Information Retrieval and NLP research and their applications in the domain of eCommerce. The workshop program was designed to bring together practitioners and researchers from academia and industry to discuss the challenges and approaches to product search and recommendation in the eCommerce domain. A second goal was to run a data challence on real-world eCommerce data. The workshop drew contributions from both industry as well as academia, in total the workshop received 38 submissions, and accepted 24 (63%). There were two keynotes by invited speakers, a poster session where all the accepted submissions were presented, a panel discussion, and three short talks by invited speakers.

Genetic-Fuzzy Programming Based Linkage Rule Miner (GFPLR-Miner) for Entity Linking in Semantic Web

2021 ◽  
pp. 447-481
Author(s):  
Amit Singh ◽  
Aditi Sharan
Keyword(s):  
Fuzzy Logic ◽  
Information Retrieval ◽  
Semantic Web ◽  
Real World ◽  
Fuzzy Programming ◽  
Data Sources ◽  
Fuzzy Approach ◽  
Entity Linking ◽  
Efficient Information ◽  
Domain Independent

This article describes how semantic web data sources follow linked data principles to facilitate efficient information retrieval and knowledge sharing. These data sources may provide complementary, overlapping or contradicting information. In order to integrate these data sources, the authors perform entity linking. Entity linking is an important task of identifying and linking entities across data sources that refer to the same real-world entities. In this work, they have proposed a genetic fuzzy approach to learn linkage rules for entity linking. This method is domain independent, automatic and scalable. Their approach uses fuzzy logic to adapt mutation and crossover rates of genetic programming to ensure guided convergence. The authors' experimental evaluation demonstrates that our approach is competitive and make significant improvements over state of the art methods.

A Database Project in a Small Company (or How the Real World Doesn't Always Follow the Book)

2009 ◽  
pp. 468-483
Author(s):  
Efrem Mallach
Keyword(s):  
Information Retrieval ◽  
Real World ◽  
Retrieval System ◽  
Information Retrieval System ◽  
Small Company ◽  
The Real ◽  
Design And Implementation ◽  
Development Methods

The case study describes a small consulting company’s experience in the design and implementation of a database and associated information retrieval system. Their choices are explained within the context of the firm’s needs and constraints. Issues associated with development methods are discussed, along with problems that arose from not following proper development disciplines.

SEMANTIC FIELD: A THEORETICAL PERSPECTIVE OF MODELING INFORMATION RETRIEVAL

2009 ◽  
Vol 18 (06) ◽  
pp. 825-851
Author(s):  
KUN YUE ◽  
WEI-YI LIU
Keyword(s):  
Information Retrieval ◽  
Theoretical Model ◽  
Electrostatic Field ◽  
Real World ◽  
Field Model ◽  
Theoretical Perspective ◽  
Keyword Extraction ◽  
Semantic Field ◽  
Novel Method ◽  
Interpretable Model

Information retrieval has been paid much attention and it is widely studied and applied in real world paradigms. For various aspects of information retrieval, various approaches have been proposed from various perspectives. It is necessary to provide a formally-unified and physically-interpretable model for classical problems in information retrieval (e.g., document classification, authority-page selection, and keyword extraction, etc.). In this paper we propose a theoretical model, called semantic field, inspired by the theories of lexical semantics and electrostatic field. Based on this physical model, information retrieval can be viewed from a theoretical perspective and interpreted by people's physical intuitions and natural heuristics. Centered on the concept of semantic field, we give some relevant properties, including semantic affinity, semantic coacervation degree and radiation of a semantic source. As the representative application of the proposed semantic field model, a novel method for automatic keyword extraction is discussed, and the feasibility is verified by corresponding experiments.

An Empirical Study of Paid Listings in Product Search and Purchase

2005 ◽  
Vol 49 (15) ◽  
pp. 1429-1433
Author(s):  
Marc L. Resnick ◽  
Bernard J. Jansen
Keyword(s):  
Information Retrieval ◽  
Empirical Study ◽  
User Interaction ◽  
Objective Measures ◽  
The Internet ◽  
Difficult Decision ◽  
Online Retailers ◽  
Paid Search ◽  
Product Search ◽  
Future Business

Pay-for-placement search has been described contradictorily as either the future business model of information retrieval on the Internet or as a deceptive nuisance for unsuspecting Web surfers. This study investigated user interaction with paid search listings during a set of naturalistic product search and purchase tasks. The study compared objective measures of how often these listings are used and subjective user opinions both during and after the task. Participants were more likely to view and select organic listings than paid listings. They also rated the organic results as more relevant for the shopping tasks and expressed suspicion of the paid listings. However, some users did use the paid listings and when the content was relevant there was no difference in the relevance ratings of the content pages themselves. A lack of neutral ratings for paid listings suggests that users will respond either negatively or positively to the paid listings, creating a difficult decision for online retailers and product search companies when considering whether to support paid search listings.

scholarly journals Hashtag2Vec: Learning Hashtag Representation with Relational Hierarchical Embedding Model

2018 ◽  
Author(s):  
Jie Liu ◽  
Zhicheng He ◽  
Yalou Huang
Keyword(s):  
Social Networks ◽  
Information Retrieval ◽  
Social Network ◽  
Real World ◽  
Heterogeneous Network ◽  
Event Analysis ◽  
Short Text ◽  
Theme Discovery ◽  
Real World Datasets ◽  
Text Content

Hashtags have always been important elements in many social network platforms and micro-blog services. Semantic understanding of hashtags is a critical and fundamental task for many applications on social networks, such as event analysis, theme discovery, information retrieval, etc. However, this task is challenging due to the sparsity, polysemy, and synonymy of hashtags. In this paper, we investigate the problem of hashtag embedding by combining the short text content with the various heterogeneous relations in social networks. Specifically, we first establish a network with hashtags as its nodes. Hierarchically, each of the hashtag nodes is associated with a set of tweets and each tweet contains a set of words. Then we devise an embedding model, called Hashtag2Vec, which exploits multiple relations of hashtag-hashtag, hashtag-tweet, tweet-word, and word-word relations based on the hierarchical heterogeneous network. In addition to embedding the hashtags, our proposed framework is capable of embedding the short social texts as well. Extensive experiments are conducted on two real-world datasets, and the results demonstrate the effectiveness of the proposed method.

Invited panel discussion: Issues and challenges in uncertainty representation and management with applications to real-world problems

2007 ◽  
Author(s):  
Ivan Kadar ◽  
Enrique H. Ruspini ◽  
Anne-Laure Jousselme ◽  
Patrick Maupin ◽  
Ronald Mahler ◽  
...  
Keyword(s):  
Real World ◽  
Panel Discussion ◽  
Uncertainty Representation ◽  
Real World Problems
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