Dynamic Facet Ordering for Faceted Product Search Engines (Extended Abstract)

2018 ◽  
Author(s):  
Damir Vandic ◽  
Steven S. Aanen ◽  
Flavius Frasincar ◽  
Uzay Kaymak
Keyword(s):  
Search Engines ◽  
Product Search ◽  
Product Search Engines

Dynamic Facet Ordering for Faceted Product Search Engines

2017 ◽  
Vol 29 (5) ◽  
pp. 1004-1016 ◽  
Author(s):  
Damir Vandic ◽  
Steven Aanen ◽  
Flavius Frasincar ◽  
Uzay Kaymak
Keyword(s):  
Search Engines ◽  
Product Search ◽  
Product Search Engines

scholarly journals Dynamic Facet Ordering for Faceted Product Search Engines

NCICCNDA ◽  
2018 ◽  
Author(s):  
Bhagyashree S ◽  
Bindu S ◽  
Meghana K ◽  
Nisha H N ◽  
Manjunath S
Keyword(s):  
Search Engines ◽  
Product Search ◽  
Product Search Engines

scholarly journals Deep Neural Network and Boosting Based Hybrid Quality Ranking for e-Commerce Product Search

2021 ◽  
Vol 5 (3) ◽  
pp. 35
Author(s):  
Mourad Jbene ◽  
Smail Tigani ◽  
Saadane Rachid ◽  
Abdellah Chehri
Keyword(s):  
Quality Indicators ◽  
Search Engines ◽  
Deep Neural Network ◽  
Information Overload ◽  
Context Aware ◽  
Ranking Methods ◽  
Exact Match ◽  
Product Search ◽  
Product Search Engines ◽  
Quality Ranking

In the age of information overload, customers are overwhelmed with the number of products available for sale. Search engines try to overcome this issue by filtering relevant items to the users’ queries. Traditional search engines rely on the exact match of terms in the query and product meta-data. Recently, deep learning-based approaches grabbed more attention by outperforming traditional methods in many circumstances. In this work, we involve the power of embeddings to solve the challenging task of optimizing product search engines in e-commerce. This work proposes an e-commerce product search engine based on a similarity metric that works on top of query and product embeddings. Two pre-trained word embedding models were tested, the first representing a category of models that generate fixed embeddings and a second representing a newer category of models that generate context-aware embeddings. Furthermore, a re-ranking step was performed by incorporating a list of quality indicators that reflects the utility of the product to the customer as inputs to well-known ranking methods. To prove the reliability of the approach, the Amazon reviews dataset was used for experimentation. The results demonstrated the effectiveness of context-aware embeddings in retrieving relevant products and the quality indicators in ranking high-quality products.

Swift Trust in Web Vendors

2011 ◽  
pp. 102-122
Author(s):  
Xin Li ◽  
Guang Rong ◽  
Michelle Carter ◽  
Jason Bennett Thatcher
Keyword(s):  
Research Design ◽  
Experimental Research ◽  
Social Presence ◽  
Third Party ◽  
Effort Expectancy ◽  
Quasi Experimental ◽  
Product Search ◽  
Swift Trust ◽  
And Performance ◽  
Product Search Engines

With the growth of product search engines such as pricegrabber.com, web vendors have many more casual visitors. This research examines how web vendors may foster “swift trust” as a means to convert casual visitors to paying customers. We examine whether perceptions of website’s appearance features (normality, social presence and third-party links) and functionality features (security, privacy, effort expectancy and performance expectancy) positively relate to swift trust in a web vendor. Using a quasi-experimental research design, we empirically test the proposed relationships. Based on an analysis of 224 respondents, we found appearance and functionality features explained 61% of the variance in swift trust. The paper concludes with a discussion of findings and implications.

Swift Trust in Web Vendors

2011 ◽  
pp. 1207-1227
Author(s):  
Xin Li ◽  
Guang Rong ◽  
Jason B. Thatcher
Keyword(s):  
Experimental Research ◽  
Social Presence ◽  
Web Sites ◽  
Third Party ◽  
Effort Expectancy ◽  
Quasi Experimental ◽  
Product Search ◽  
Swift Trust ◽  
And Performance ◽  
Product Search Engines

With the growth of product search engines such as pricegrabber.com, Web vendors have many more casual visitors. This research examines how Web vendors may foster “swift trust” as a means to convert casual visitors to paying customers. We examine whether perceptions of Web sites’ appearance features (normality, social presence and third-party links) and functionality features (security, privacy, effort expectancy and performance expectancy) positively relate to swift trust in a Web vendor. Using a quasi-experimental research design, we empirically test the proposed relationships. Based on an analysis of 224 respondents, we found appearance and functionality features explained 61% of the variance in swift trust. The article concludes with a discussion of findings and implications.

Classification of means and methods of the Web semantic retrieval

2017 ◽  
pp. 030-050
Author(s):  
J.V. Rogushina ◽  
Keyword(s):  
Search Engines ◽  
Domain Knowledge ◽  
Information Needs ◽  
Web Search ◽  
User Interaction ◽  
Query Languages ◽  
Semantic Search ◽  
Semantic Retrieval ◽  
The Web

Problems associated with the improve ment of information retrieval for open environment are considered and the need for it’s semantization is grounded. Thecurrent state and prospects of development of semantic search engines that are focused on the Web information resources processing are analysed, the criteria for the classification of such systems are reviewed. In this analysis the significant attention is paid to the semantic search use of ontologies that contain knowledge about the subject area and the search users. The sources of ontological knowledge and methods of their processing for the improvement of the search procedures are considered. Examples of semantic search systems that use structured query languages (eg, SPARQL), lists of keywords and queries in natural language are proposed. Such criteria for the classification of semantic search engines like architecture, coupling, transparency, user context, modification requests, ontology structure, etc. are considered. Different ways of support of semantic and otology based modification of user queries that improve the completeness and accuracy of the search are analyzed. On base of analysis of the properties of existing semantic search engines in terms of these criteria, the areas for further improvement of these systems are selected: the development of metasearch systems, semantic modification of user requests, the determination of an user-acceptable transparency level of the search procedures, flexibility of domain knowledge management tools, increasing productivity and scalability. In addition, the development of means of semantic Web search needs in use of some external knowledge base which contains knowledge about the domain of user information needs, and in providing the users with the ability to independent selection of knowledge that is used in the search process. There is necessary to take into account the history of user interaction with the retrieval system and the search context for personalization of the query results and their ordering in accordance with the user information needs. All these aspects were taken into account in the design and implementation of semantic search engine "MAIPS" that is based on an ontological model of users and resources cooperation into the Web.

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