E-commerce Search Engines Research Articles

VETE: improving visual embeddings through text descriptions for eCommerce search engines

VETE: improving visual embeddings through text descriptions for eCommerce search engines

Study, Analysis and Comparison between Amazon A10 and A11 Search Algorithm

The entirety of Amazon’s sales being powered by Amazon Search, one of the leading e-commerce platforms around the globe. As a result, even slight boosts in appropriateness can have a major impact on profits as well as the shopping experience of millions of users. Throughout the beginning, Amazon’s product search engine was made up of a number of manually adjusted ranking processes that made use of a limited number of input features. Since that time, a significant amount has transpired. Many people overlook the fact that Amazon is a search engine, and even the biggest one for e-commerce. It is indeed time to begin treating Amazon truly as the top e-commerce search engine across the globe because it currently serves 54% of all product queries. In this paper, the authors have considered two most important Amazon search engine algorithms viz. A10 and A11 and comparative study has been discussed.

Quality metrics for search engine deterministic sort orders

eCommerce search engines such as eBay and Amazon often allow the user to order their search results on deterministic features such as price, time, or distance from the seller. Using metrics such as precision at 10 documents (P@10), others have already shown that the quality of deterministically sorted results is lower than that of best-match (or relevance) sorted results, and that work is needed in order to improve result quality. But metrics such as P@10 are based purely on relevance, and do not reflect the order-feature: cost (be it price, time, or otherwise) — and it is hard to see how to improve a system without a metric that reflects the quality of the ordering. In this contribution we introduce a set of metrics that, using relevance and cost, measure the quality of deterministically sorted search engine results. We examine metrics from the perspective of the buyer, the seller, and the systems engineer. Using our new metrics (buying power (bp), buying power for K (bp4k), selling power (sp), and cheapest precision (Pc)) we re-evaluate the results of the “eBay SIGIR 2019 eCommerce Search Challenge: High Accuracy Recall Task” and demonstrate how to include cost in a metric designed to evaluate the quality of deterministically ordered lists of results.

Multi-objective cluster based bidding algorithm for E-commerce search engine marketing system.

Search engine marketing (SEM) is an important channel for the success of e-commerce. With the increasing scale of catalog items, designing an efficient modern industrial-level bidding system usually requires overcoming the following hurdles: 1. the relevant bidding features are of high sparsity, preventing an accurate prediction of the performances of many ads. 2. the large volume of bidding requests induces a significant computation burden to offline and online serving. In this article, we introduce an end-to-end structure of a multi-objective bidding system for search engine marketing for Walmart e-commerce, which successfully handles tens of millions of bids each day. The system deals with multiple business demands by constructing an optimization model targeting a mixture of metrics. Moreover, the system extracts the vector representations of ads via the Transformer model. It leverages their geometric relation to building collaborative bidding predictions via clustering to address performance features' sparsity issues. We provide theoretical and numerical analyzes to discuss how we find the proposed system as a production-efficient solution.

Analyzing E-shop onsite optimization factors in the context of E-commerce search engine optimization

Search engine optimization of e-shops by using onsite optimization factors is an important part of the search engine marketing process and in particular of SEO category. Onsite optimization process is used by e-shops to improve their ranking in search engine results. Onsite optimization techniques convert and upgrade e-shops, make them more competitive and increase the chances of attracting internet users and converting leads into customers. This study focuses on the appropriate factors to be carried out within webpages code and content management systems of e-shops in order each webpage to achieve higher ranking position on search engine result pages. Search engine optimization is a major pillar of digital marketing and in this context the paper reveal and underline all important factors any e-shop has to implement so as to take viable ranking position on search engine results which lead to visiting of appropriate target groups and convert them into customers. In this paper researchers defined the term onsite optimization, explains its role in the ranking of search engines, reviewed previous studies on using techniques onsite optimization and stated recommendations for better use of these factors. The literature review indicated that the effective implement of onsite optimization improves overall search engine optimization strategy of e-shop which reflects higher ranking position and better cash flows.

Indicators of Website Features in the User Experience of E-Tourism Search and Metasearch Engines

The continuous growth of e-commerce, combined with new trends in the tourism sector, creates an imperative to conduct analyses and establish suitable models for improving the experience of users who seek tourism products online, using search and metasearch engines. However, few studies analyze web design variables and their impacts on the user experience. Therefore, the present study aims to investigate the influence of content, usability, functionality, and branding for determining user experiences with search engines and metasearch engines dedicated to tourism. The methodology used in this research followed a mixed approach to fulfill the proposed dual perspective, that is, to collect data from websites and evaluate the user experience. To determine the variables to be modeled, the authors use a General Additive Model. Main results reveal two determinant factors that enhance the user experience: usability and branding. With this foundation, this study proposes basic premises of the digital strategy that tourism platforms should follow. As implications for the tourism sector, suggest that e-commerce search and metasearch engines in the tourism industry should devote substantial efforts to implementing interactivity, memorability, personalization, privacy, and security.

Querying subjective data

Online users are constantly seeking experiences, such as a hotel with clean rooms and a lively bar, or a restaurant for a romantic rendezvous. However, e-commerce search engines only support queries involving objective attributes such as location, price, and cuisine, and any experiential data is relegated to text reviews. In order to support experiential queries, a database system needs to model subjective data. Users should be able to pose queries that specify subjective experiences using their own words, in addition to conditions on the usual objective attributes. This paper introduces OpineDB, a subjective database system that addresses these challenges. We introduce a data model for subjective databases. We describe how OpineDB translates subjective queries against the subjective database schema, which is done by matching the user query phrases to the underlying schema. We also show how the experiential conditions specified by the user can be combined and the results aggregated and ranked. We demonstrate that subjective databases satisfy user needs more effectively and accurately than alternative techniques through experiments with real data of hotel and restaurant reviews.

Categorizing Multi-Label Product Questionnaires through SVM Based Click stream

In this paper, Question Categorization (QC) has been studied most primarily in order to understand customers' search intention. In both of these searches, the items in the question list relate to the category label belonging to the taxonomy tree that is being examined. Despite this, search queries about the product usually vary depending on what is vague, and introduce new products over time, seasonal trends and narrow. Traditional supervised approaches to E-Commerce QC are not possible due to the high volume of traffic and high cost for manual annotation in E-Commerce search engines. Here, clickstream data is utilized to determine the effectiveness of a channel's marketplace. So, using the customer's click concept, to collect large-scale question categorization data, this paper uses unsupervised methods that means SVM algorithm is mainly used in this system. Here the data is in the multiclass and multi-label classifier is used to classify them. This paper gets on a large multi-label data set with specific and individual queries from a specific category. In this paper, a comparison of different sophisticated text classifiers is viewed. This paper calculates the micro-F1 scores of top and leaf, which are considered to be a linear SVM-ensemble.

Toward a Semantic search engine for E-Commerce

Toward a Semantic search engine for E-Commerce

Subjective databases

Online users are constantly seeking experiences, such as a hotel with clean rooms and a lively bar, or a restaurant for a romantic rendezvous. However, e-commerce search engines only support queries involving objective attributes such as location, price, and cuisine, and any experiential data is relegated to text reviews. In order to support experiential queries, a database system needs to model subjective data. Users should be able to pose queries that specify subjective experiences using their own words, in addition to conditions on the usual objective attributes. This paper introduces OpineDB, a subjective database system that addresses these challenges. We introduce a data model for subjective databases. We describe how OpineDB translates subjective queries against the subjective database schema, which is done by matching the user query phrases to the underlying schema. We also show how the experiential conditions specified by the user can be combined and the results aggregated and ranked. We demonstrate that subjective databases satisfy user needs more effectively and accurately than alternative techniques through experiments with real data of hotel and restaurant reviews.

A semantic framework for ecommerce search engine optimization

Web search engines assist users to discover useful information on the World Wide Web. However, when the same query is submitted by different users, typical search engines provide the identical results regardless of who submitted the query. The search engines usually provide search results without considering user context or interests. Ecommerce web search personalization is an emerging area in the research which has already been achieved the curiosity. Anticipating the web user needs is one of the key factors of this framework and provides the qualitative results based on web user behavior. In this manuscript; we propose an approach for personalizing the web search using techniques, applications and opportunities of Data mining and web mining. Personalization of ecommerce has become a trend of current business scenario to overcome the problem of product overload, to appease the customers with special treatment offerings. Over the last 10 years it has been adopted as a skillful strategy to give enhanced service to the web clients and sustain in the market.

The Role of the Internet as a Tool to Search for Tourist Information

The internet has become the main tool to search for information. This is particularly the case for the tourism industry, where online searches are becoming increasingly popular. The ongoing growth of e-commerce has ruptured the existing relationship with the old management information systems and products. Given the vast range of available information, search and meta-search engines have become key elements in gaining a competitive advantage and establishing differentiation strategies within the sector. This paper aims to compare tourism e-commerce search engines and meta-search engines based on website structure and content. It analyses the main attributes and categories found to create clusters and a ranking from which to compare the position of search and meta-search engines from the perspective of the supplier, as opposed to that of the traditional consumer market preference. This paper will contribute to the industry by providing valuable information for best management practices about the tourism website's structure and offering, and determining factor in implementing successful strategies.

Perencanaan Search Engine E-commerce dengan Metode Latent Semantic Indexing Berbasis Multiplatform

E-commerce is a sale and purchase transactions that occur through electronic systems such as the Internet, WWW, or other computer networks. E-commerce involves electronic data interchange and automated data collection systems. In all e-commerce search engine provided a column for the search items desired by the user. In e-commerce such as Tokopedia, Lazada, MatahariMall, Amazon, and other search engines that provided just use a regular search engine technology. In the usual search engines getting longer sentences from the input or output of goods search results will be more extensive and more. However, by utilizing the semantic indexing technology, the longer and clear input desired goods, the number of searches will be few and accurately in accordance with the input that helps the user in decision making. In this study discussed how to build a search engine on the web e-commerce by using Latent Semantic Indexing. The first starts from the use of Text Mining methods for word processing, and the method Levenshtein Distance to repair automatic word and the last Latent Semantic Indexing for information processing and input expenditure.

Clustering e-commerce search engines based on their search interface pages using WISE-Cluster

In this paper, we propose a new approach to clustering e-commerce search engines (ESEs) on the Web. Our approach utilizes the features available on the interface page of each ESE, including the label terms and value terms appearing in the search form, the number of images, normalized price terms as well as other terms. The experimental results based on more than 400 ESEs indicate that the proposed approach has good clustering accuracy. The importance of different types of features is analyzed and the terms in the search form are the most important feature in obtaining quality clusters.

The effectiveness of Web search engines for retrieving relevant ecommerce links

Ecommerce is developing into a fast-growing channel for new business, so a strong presence in this domain could prove essential to the success of numerous commercial organizations. However, there is little research examining ecommerce at the individual customer level, particularly on the success of everyday ecommerce searches. This is critical for the continued success of online commerce. The purpose of this research is to evaluate the effectiveness of search engines in the retrieval of relevant ecommerce links. The study examines the effectiveness of five different types of search engines in response to ecommerce queries by comparing the engines’ quality of ecommerce links using topical relevancy ratings. This research employs 100 ecommerce queries, five major search engines, and more than 3540 Web links. The findings indicate that links retrieved using an ecommerce search engine are significantly better than those obtained from most other engines types but do not significantly differ from links obtained from a Web directory service. We discuss the implications for Web system design and ecommerce marketing campaigns.