Evaluating the retrieval effectiveness of web search engines using a representative query sample

Lewandowski, Dirk

doi:10.1002/asi.23304

Cited by 86 publications

(62 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Eighty percent (80%) of the income generated by clicks on these ads is donated to programs for reforestation [19]. The remaining 20% is used to pay for necessary costs such as salaries, servers, domains, or marketing.…”

Section: The Search Engine Ecosystemmentioning

confidence: 99%

“…The projects that Ecosia finances are for planting trees, mainly in the African and Brazilian continents, and therefore we can say that Ecosia is an example of a social business model in the search engine market [19].…”

Section: The Search Engine Ecosystemmentioning

confidence: 99%

“…Likewise, large forests can even create clouds that in turn reflect sunlight and cool our planet, thus helping to mitigate the effects of global warming. Biodiversity is also increased by the forests created with the trees, as they produce diverse, sustainable wildlife systems for plants and animals to live in [18,19].…”

Section: The Search Engine Ecosystemmentioning

confidence: 99%

See 2 more Smart Citations

The Effect of Internet Searches on Afforestation: The Case of a Green Search Engine

Palos-Sánchez

Saura

2018

Forests

View full text Add to dashboard Cite

Abstract:Ecosia is an Internet search engine that plants trees with the income obtained from advertising. This study explored the factors that affect the adoption of Ecosia.org from the perspective of technology adoption and trust. This was done by using the Unified Theory of Acceptance and Use of Technology (UTAUT2) and then analyzing the results with PLS-SEM (Partial Least Squares-Structural Equation Modeling). Subsequently, a survey was conducted with a structured questionnaire on search engines, which yielded the following results: (1) the idea of a company helping to mitigate the effects of climate change by planting trees is well received by Internet users. However, few people accept the idea of changing their habits from using traditional search engines; (2) Ecosia is a search engine believed to have higher compatibility rates, and needing less hardware resources, and (3) ecological marketing is an appropriate and future strategy that can increase the intention to use a technological product. Based on the results obtained, this study shows that a search engine or other service provided by the Internet, which can be audited (visits, searches, files, etc.), can also contribute to curb the effects of deforestation and climate change. In addition, companies, and especially technological start-ups, are advised to take into account that users feel better using these tools. Finally, this study urges foundations and non-governmental organizations to fight against the effects of deforestation by supporting these initiatives. The study also urges companies to support technological services, and follow the behavior of Ecosia.org in order to positively influence user satisfaction by using ecological marketing strategies.

show abstract

Section: The Search Engine Ecosystemmentioning

confidence: 99%

Section: The Search Engine Ecosystemmentioning

confidence: 99%

See 1 more Smart Citation

The Effect of Internet Searches on Afforestation: The Case of a Green Search Engine

Palos-Sánchez

Saura

2018

Forests

View full text Add to dashboard Cite

show abstract

“…After studying the impacts of these factors, they proposed enhanced from of vector space model to improve efficiency. In [33], Lewandowski et al take randomly selected 1000 informational and 1000 navigational queries for a major German SE and compare their performance with Google and Bing. According to their results, Google is able to establish the precise responses in 95.3% of cases, while Bing only yields the right response 76.6% of the time for navigational queries.…”

Section: Related Workmentioning

confidence: 99%

Evaluating Retrieval Effectiveness by Sustainable Rank List

et al. 2017

View full text Add to dashboard Cite

Abstract:The Internet of Things (IoT) and Big Data are among the most popular emerging fields of computer science today. IoT devices are creating an enormous amount of data daily on a different scale; hence, search engines must meet the requirements of rapid ingestion and processing followed by accurate and fast extraction. Researchers and students from the field of computer science query the search engines on these topics to reveal a wealth of IoT-related information. In this study, we evaluate the relative performance of two search engines: Bing and Yandex. This work proposes an automatic scheme that populates a sustainable optimal rank list of search results with higher precision for IoT-related topics. The proposed scheme rewrites the seed query with the help of attribute terms extracted from the page corpus. Additionally, we use newness and geo-sensitivity-based boosting and dampening of web pages for the re-ranking process. To evaluate the proposed scheme, we use an evaluation matrix based on discounted cumulative gain (DCG), normalized DCG (nDCG), and mean average precision (MAP n ). The experimental results show that the proposed scheme achieves scores of MAP@5 = 0.60, DCG 5 = 4.43, and nDCG 5 = 0.95 for general queries; DCG 5 = 4.14 and nDCG 5 = 0.93 for time-stamp queries; and DCG 5 = 4.15 and nDCG 5 = 0.96 for geographical location-based queries. These outcomes validate the usefulness of the suggested system in helping a user to access IoT-related information.

show abstract

“…Despite the fact that essential results have already been achieved to build search engines, there is still an obvious need for new approaches to large-scale search. Among others, Marz et al [5], Cambazoglu and Baeza-Yates [6] focus on the scalability problem related to the high computational costs of storing and processing large volumes of distributed data, Lewandowski [7] deals with providing fast access to large amounts of information and effectiveness providing relevant search results and optimizing the search process for a user.…”

Section: Introductionmentioning

confidence: 99%

TB-Structure: Collective Intelligence for Exploratory Keyword Search

Terziyan

Golovianko

Cochez

2017

Semantic Keyword-Based Search on Structured Data Sources

View full text Add to dashboard Cite

Abstract. In this paper we address an exploratory search challenge by presenting a new (structure-driven) collaborative filtering technique. The aim is to increase search effectiveness by predicting implicit seeker's intents at an early stage of the search process. This is achieved by uncovering behavioral patterns within large datasets of preserved collective search experience. We apply a specific tree-based data structure called a TB (There-and-Back) structure for compact storage of search history in the form of merged query trails -sequences of queries approaching iteratively a seeker's goal. The organization of TB-structures allows inferring new implicit trails for the prediction of a seeker's intents. We used experiments to demonstrate both: the storage compactness and inference potential of the proposed structure.

show abstract

Evaluating the retrieval effectiveness of web search engines using a representative query sample

Cited by 86 publications

References 47 publications

The Effect of Internet Searches on Afforestation: The Case of a Green Search Engine

The Effect of Internet Searches on Afforestation: The Case of a Green Search Engine

Evaluating Retrieval Effectiveness by Sustainable Rank List

TB-Structure: Collective Intelligence for Exploratory Keyword Search

Contact Info

Product

Resources

About