Architecture of a Commercialized Search Engine Using Mobile Agents

Updata:01-01-1970

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DOI: https://doi.org/10.30564/aia.v1i1.688

Abstract:

Shopping Search Engine (SSE) implies a unique challenge for validating distinct items available online in market place. For sellers, having a user finding relevant search results on top is very difficult. Buyers tend to click on and buy from the listings which appear first. Search engine optimization devotes that goal to influence such challenges. In current shopping search platforms, lots of irrelevant items retrieved from their indices; e.g. retrieving accessories of exact items rather than retrieving the items itself, regardless the price of item were considered or not. Also, users tend to move from shoppers to another searching for appropriate items where the time is crucial for consumers. In our proposal, we exploit the drawbacks of current shopping search engines, and the main goal of this research is to combine and merge multiple search results retrieved from some highly professional shopping sellers in the commercial market. Experimental results showed that our approach is more efficient and robust for retrieving a complete list of desired and relevant items with respect to all query space.

CCS CONCEPTS

Information systems - Commercial-specific retrieval

References:

[1] Liyi Zhang, Mingzhu Zhu, and Wei Huang. “A Framework for an Ontology-Based E-Commerce Product Information Retrieval System”, School of Information Management, Wuhan University – China, Journal of Computers, 2009, 4(6). [2] Rainer Olbrich, Carsten D.. Schultz “Search Engine Marketing and Click Fraud”. Department of Business Administration and Economics, Research Paper, the Chair of Marketing, 2004. [3] James Christopher. “E-commerce: Comparison of online Shopping Trends and Preferences against a selected survey of women”. Research Paper, the Chair of Marketing, 2004. [4] Sandip Sen, Partha Sarathi Dutta, and Sandip Debnath. “CHAYANI: a shopper's assistant”. Journal of Intelligent Systems, 2005. DOI: https://doi.org/10.1515/JISYS.2005.14.1.3 [5] Mario J. Miranda, László Kónya, Inka Havrila, "Shoppers' satisfaction levels are not the only key to store loyalty". Marketing Intelligence & Planning, 2005, 23(2): 220-232. DOI: https://doi.org/10.1108/02634500510589958 [6] Borgo, S., Guarino, N., Masolo, C. and Vetere, G. “Using a large linguistic ontology for internet based retrieval of object-oriented components”. In proceedings of the International Conference on Software Engineering and Knowledge engineering, 1997. [7] Bryan, D. and Gershman, A. Opportunistic exploration of large consumer product paces. In Proceedings of the First ACM Conference on Electronic Commerce, ACM Press, New York: NY, 1999: 41-47. [8] Guarino, N., Masolo, C., Vetere, G., OntoSeek. “Content-Based Access to the Web”. IEEE Intelligent Systems”, 1999: 70-80. [9] TONG SUN & ANDR´E TRUDEL. “An implemented e-commerce shopping system which makes personal recommendations”. Jodrey School of Computer Science, Acadia University, 2010. [10] Andrew Trotman, Jon Degenhardt, and Surya Kallumadi. “The Architecture of eBay Search”. In Proceedings of ACM SIGIR Workshop on eCommerce, SIGIR, 2017. [11] Fan Yang, Ajinkya Kale, Yury Bubnov, Leon Stein, Qiaosong Wang, Hadi Kiapour, and Robinson Piramuthu. ” Visual Search at eBay”. In Proceeding of KDD conference, ACM. ISBN, 2017, 978-1-4503-4887. [12] Kevin Lin, Huei-fang Yang, Jen-hao Hsiao, and Chu-song Chen. “Deep Learning of Binary Hash Codes for Fast Image Retrieval”. In IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2015: 27–35. [13] Chen Hao, Tao Chuanqi, and Jerry Gao. “A Quality Evaluation Approach to Search Engines of Shopping Platforms”. IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService), 2017. [14] Nanda Kumar, Karl R. Lang and Qian Peng. “Consumer Search Behavior in Online Shopping Environments”. In Proceedings of the 38th Hawaii International Conference on System Sciences, 2005. [15] Yuan Weijing. “End-User Searching Behavior in Information Retrieval: A Longitudinal Study.” Journal of the American Society for Information Science, 1997,48 (3): 218-234. [16] Ravi Sen, Subhajyoti Bandyopadhyay, James D. Hess, and Jeevan Jaisingh. “PRICING PAID PLACEMENTS ON SEARCH ENGINES”. Journal of Electronic Commerce Research, 2008, 9(1). [17] Sumit Mene, Arshey Dhangekar, Abhijeet Gurav, Harshal R. Randad, Rupali Tornekar and Vijay Gaikwad. (2017). International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169, 2017, 5(12): 62 – 66. [18] Bernard J. Jansen and Paulo R. Molina. “The effectiveness of Web search engines for retrieving relevant ecommerce links “. Information Processing and Management, 2006, 42: 1075–1098. [19] Cristian Duda, Gianni Frey, Donald Kossmann, and Chong Zhou. “AJAXSearch: Crawling, Indexing and Searching Web 2.0 Applications”, 2008. ACM 978-1-60558-306-8/08/08. [20] Franklin, Curt. (2002). “How Internet Search Engines Work”, 2002. https://www.howstuffworks.com [21] Trilok Gupta and Archana Sharma. (2014).”Anatomy of Web Search Engines”. International Journal of Advanced Research in Computer and Communication Engineering, 2014, 3(4). [22] Animesh Animesh, Vandana Ramachandran, and Siva Viswanathan. “Quality Uncertainty and Adverse Selection in Sponsored Search Markets”. Decision and Information Technologies, University of Maryland, 2010. [23] Ellen M. Voorhees. “Variations in relevance judgments and the measurement of retrieval effectiveness”. Information Processing and Management Journal, 2000: 697-716. [24] Al-akashi, F. “Using Wikipedia Knowledge and Query Types in a New Indexing Approach for Web Search Engines. PhD Thesis, University of Ottawa, 2014.  DOI: http://dx.doi.org/10.20381/ruor-6304

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