scholarly journals Location Based Business Recommendation Using Spatial Demand

2020 ◽  
Vol 12 (10) ◽  
pp. 4124 ◽  
Author(s):  
Ashok Kumar P ◽  
Shiva Shankar G ◽  
Praveen Kumar Reddy Maddikunta ◽  
Thippa Reddy Gadekallu ◽  
Abdulrahman Al-Ahmari ◽  
...  
Keyword(s):  
Service Time ◽  
Search Engines ◽  
Web Search ◽  
High Demand ◽  
Business Location ◽  
Benchmark Datasets ◽  
Web Search Engines ◽  
Spatial Demand ◽  
Customer Need

Business locations is most important factor to consider before starting a business because the best location attracts more number of people. With the help of web search engines, the customers can search the nearest business location before visiting the business. For example, if a customer need to buy some jewel, he makes use of search engines to find the nearest jewellery shop. If some entrepreneur wants to start a new jewellery shop, he needs to find a best area where there is no jewellery shop nearby and there are more customers in need of jewel. In this paper, we propose an algorithm to find the best place to start a business where there is high demand and no (or very few supply). We measure the quality of recommendation in terms of average service time, customer-business ratio of our new algorithm by implementing in benchmark datasets and the results prove that our algorithm is more efficient than the existing kNN algorithm.

Improving the Quality of Web Search

2011 ◽  
pp. 463-480
Author(s):  
Mohamed Salah Hamdi
Keyword(s):  
Search Engines ◽  
Web Search ◽  
Resource Constraints ◽  
Post Processing ◽  
Self Organizing Maps ◽  
High Recall ◽  
Additional Resource ◽  
Web Search Engines ◽  
Ranked List

Conventional Web search engines return long lists of ranked documents that users are forced to sift through to find relevant documents. The notoriously-low precision of Web search engines coupled with the ranked list presentation make it hard for users to find the information they seek. Developing retrieval techniques that will yield high recall and high precision is desirable. Unfortunately, such techniques would impose additional resource demands on the search engines which are already under severe resource constraints. A more productive approach, however, seems to enhance post-processing of the retrieved set. If such value-adding processes allow the user to easily identify relevant documents from a large retrieved set, queries that produce low precision/high recall results will become more acceptable. We propose improving the quality of Web search by combining meta-search and self-organizing maps. This can help users both in locating interesting documents more easily and in getting an overview of the retrieved document set.

A branding model for web search engines

2012 ◽  
Vol 7 (3) ◽  
pp. 195 ◽  
Author(s):  
Lu Zhang ◽  
Bernard J. Jansen ◽  
Anna S. Mattila
Keyword(s):  
Search Engines ◽  
Web Search ◽  
Web Search Engines

scholarly journals Web Search Engines - Not Yet a Reliable Replacement for Bibliographic Databases

2018 ◽  
Vol 13 (3) ◽  
pp. 85-87
Author(s):  
Emma Hughes
Keyword(s):  
Social Sciences ◽  
Social Services ◽  
Search Engines ◽  
English Language ◽  
Web Search ◽  
Boolean Logic ◽  
Bibliographic Databases ◽  
Quality Of Reporting ◽  
Health And Social Care ◽  
Web Search Engines

A Review of: Bates, J., Best, P., McQuilkin, J., & Taylor, B. (2017) Will web search engines replace bibliographic databases in the systematic identification of research? The Journal of Academic Librarianship, 43(1), 8-17. https://doi.org/10.1016/j.acalib.2016.11.003 Abstract Objective - To explore whether web search engines could replace bibliographic databases in retrieving research. Design - Systematic review. Setting - English language articles in health and social care; comparing bibliographic databases and web search engines for retrieving research published between January 2005 and August 2015, in peer-reviewed journals and available in full-text. Subjects - Eight bibliographic databases: ASSIA (Applied Social Sciences Index and Abstracts), CINAHL Plus (Cumulative Index to Nursing and Allied Health Literature), LISA (Library and Information Science Abstracts), Medline, PsycInfo, Scopus, SSA (Social Services Abstracts), and SSCI (Social Sciences Citation Index) and five web search engines: Ask, Bing, Google, Google Scholar, Yahoo. Methods - A literature search via the above bibliographic databases and web search engines. The retrieved results were independently appraised by two researchers, using a combination of tools and checklists, including the PRESS checklist (McGowan et al., 2016) and took guidance on developing search strategies from the Centre for Reviews and Dissemination (2009). Main Results - Sixteen papers met the appraisal requirements. Each paper compared at least one bibliographic database against one web-search engine. The authors also discuss findings from their own search process. Precision and sensitivity scores from each paper were compared. The results highlighted that web search engines do not necessarily use Boolean logic and in general have limited functionality compared to bibliographic databases. There were variances in the way precision scores were calculated between papers, but when based on the first 100 results, web search engines were similar to some databases. However, their sensitivity scores were much weaker. Conclusion - Whilst precision scores were strong for web search engines, sensitivity was lacking; therefore web search engines cannot be seen as a replacement for bibliographic databases at this time. The authors recommend improving the quality of reporting in studies regarding literature searching in academia in order for reliable comparisons to be made.

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