Towards improving the online shopping experience: A client-based platform for post-processing Web search results
Pages 209 - 231
Abstract
The quality of results to Web search queries is substantially limited because of the cost and short processing times allowed at search engine's data center to retrieve relevant pages, augment ads, and present them to the end-user. We tackle such an issue by proposing a radically different system, where the search engine replies to a query with a large list of relevant URLs. Our client-side platform then proceeds to download the target HTML files only, parse them, understand their content, and present a summary to the user. Different types of summaries can be created by using plug-ins attached to the base platform. Each plug-in provides the required functionality according to the type of summary desired by the user. With this novel mechanism, our system offers increased computational power for post-processing search results and consequently improves and personalizes the user's search experience while maintaining constant workload at the search engine. We present prototype implementations of the proposed search assistant and an associated shopping plug-in capable of detecting whether a Web-page encapsulates a direct commercial offering. We also review measurements related to projected system performance and demonstrate its applicability to different scenarios.
References
[1]
A.A. Achenbaum and F.K. Mitchel, Pulling away from push marketing, Harvard Business Review , (May-June 1987), 38-40.
[2]
G. Adomavicius and A. Tuzhilin, Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions, IEEE Trans. on Knowledge and Data Engineering 17 (6) (2005), 734-749.
[3]
J. Ahn, P. Brusilovsky, D. He, J. Grady, and Q. Li, Personalized Web exploration with task models, in: Proc. of the 17th International World Wide Web Conference , 2008, pp. 1-10.
[4]
E. Amitay, D. Carmel, N. Har'El, S. Ofek-Koifman, A. Soffer, S. Yogev, and N. Golbandi, Social search and discovery using a unified approach, in: Proc. of the 20th ACM Conference on Hypertext and Hypermedia , 2009, pp. 199-208.
[5]
J. Bakos, Reducing buyer search costs: Implications for electronic marketplaces, Management Science 43 (12) (1997), 1676-1692.
[6]
M. Balabanovic and Y. Shoham, Fab: Content-based, collaborative recommendation, Communications of the ACM 40 (3) (1997), 66-72.
[7]
L.A. Barroso, J. Dean, and U. Hölzle, Web search for a planet: The Google cluster architecture, IEEE Micro 23 (2) (2002), 22-28.
[8]
F. Bezerra, E. Werbet, and W. Silva, Client-side content-based refinement for image search in the Web, in: Proc. of the Brazilian Symposium on Multimedia and the Web , 2005, pp. 1-3.
[9]
S. Bohte, E.H. Gerding, and J.A. La Poutre, Market-based recommendation: Agents that compete for consumer attention, ACM Trans. on Internet Technology 4 (4) (2004), 420-448.
[10]
S. Brin and L. Page, The anatomy of a large-scale hypertextual Web search engine, in: Proc. of the International World Wide Web Conference , 1998, pp. 107-117.
[11]
A. Broder, A taxonomy of Web search, SIGIR Forum 36 (2) (2002), 3-10.
[12]
R. Cattelan, D. Kirovski, and D. Vijaywargi, Serving comparative shopping links non-invasively, in: Proc. of the IEEE/ WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology , 2009, pp. 498-507.
[13]
Click Forensics, Click Fraud Index, 2008.
[14]
A. Cooper, A survey of query log privacy-enhancing techniques from a policy perspective, ACM Trans. on the Web 2 (4) (2008), 1-27.
[15]
V. Crescenzi, P. Merialdo, and P. Missier, Clustering Web pages based on their structure, Data Knowledge Engineering 54 (3) (2005), 279-299.
[16]
D.J. Davis et al., System and method for influencing a position on a search result list generated by a computer network search engine. US Patent no. 6,269,361. Assignee: GoTo.com. Issued July 31, 2001.
[17]
G. DeMicheli, Synthesis and Optimization of Digital Circuits , McGraw Hill, 1994.
[18]
A. Díaz and P. Gervás, Personalisation in news delivery systems: Item summarization and multi-tier item selection using relevance feedback, Web Intelligence and Agent Systems 3 (3) (2005), 135-154.
[19]
U. Doo and P. Maes, Social information filtering: Algorithms for automating "word of mouth", in: Proc. of the ACM Conference on Human Factors in Computing Systems , 1995, pp. 210-217.
[20]
R. Doorenbos, O. Etzioni, and D.S. Weld, A scalable comparison-shopping agent for the World Wide Web, in: Proc. of the 1st International Conference on Autonomous Agents , 1997, pp. 39-48.
[21]
E.R. Dougherty and I. Shmulevich, Mappings between probabilistic boolean networks, Signal Processing 83 (4) (2003), 799-809.
[22]
S. Dumais and H. Chen, Hierarchical classification of Web content, in: Proc. of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval , 2000, pp. 256-263.
[23]
Ellacoya Networks, Ellacoya data shows Web traffic overtakes Peer-to-Peer (P2P) as largest percentage of bandwidth on the network, Company news release, June 18, 2007.
[24]
O. Etzioni and D. Weld, A softbot-based interface to the Internet. Communications of the ACM 37 (7) (1994), 72-76.
[25]
D. Freitag, Information extraction from HTML: Application of a general machine learning approach, in: Proc. of the 15th National Conference on Artificial Intelligence , 1998, pp. 517-523.
[26]
E.J. Glover, K. Tsioutsiouliklis, S. Lawrence, D.M. Pennock, and G.W. Flake, Using Web structure for classifying and describing Web pages, in: Proc. of the 11th International Conference on World Wide Web , 2002, pp. 562-569.
[27]
A. Gunawardana and C. Meek, Aggregators and contextual effects in search ad markets, in: Proc. of WWW Workshop on Targeting and Ranking for Online Advertising , 2008.
[28]
J. Hammer, H. Garcia-Molina, J. Cho, R. Aranha, and A. Crespo, Extracting semistructured information from the Web, in: Workshop on Management of Semistructured Data , 1997, pp. 18-25.
[29]
O. Hoeber, X. D. Yang, and Y. Yao, VisiQ: Supporting visual and interactive query refinement, Web Intelligence and Agent Systems 5 (3) (2007), 311-329.
[30]
A. Hotho, A. Nürnberger, and G. Paass, A brief survey of text mining, LDV FORUM 20 (1) (2005), 19-62.
[31]
Z. Huang, W. Chung, and H. Chen, A graph model for E-commerce recommender systems, Journal of the American Society for Information Science and Technology 55
[32]
FOSI.org, Internet Content Rating Association, 2010.
[33]
P. Jacso, Shopbots: Shopping robots for electronic commerce, Online 22 (4) (1998), 14-20.
[34]
B.J. Jansen and A. Spink, An analysis of Web searching by European alltheweb.com users, Information Processing and Management 41 (2) (2004), 361-381.
[35]
B.J. Jansen and A. Spink, How are we searching the World Wide Web?: A comparison of nine search engine transaction logs, Information Processes Management 42 (1) (2006), 248-263.
[36]
T. Joachims, D. Freitag, and T. Mitchell, WebWatcher: A tour guide for the World Wide Web, in: Proc. of International Joint Conference on Artificial Intelligence , 1997, pp. 770-775.
[37]
N. Kammenhuber, J. Luxenburger, A. Feldmann, and G. Weikum, Web search clickstreams, in: Proc. of the 6th ACM SIGCOMM Conference on Internet Measurement , 2006, pp. 245-250.
[38]
S.A. Kauffman, Metabolic stability and epigenesis in randomly constructed genetic nets, Journal of Theoretical Biology 22 (3) (1969), 437-467.
[39]
O. Khan, How much of an average website's traffic comes from Google?, ISL consulting, Company News, Aug. 22, 2008.
[40]
C. Knoblock and A. Levy, Working Notes of the AAAI Spring Symposium on Information Gathering from Heterogeneous, Distributed Environments , AAAI Press, Stanford University, 1995.
[41]
B. Krulwich, The BargainFinder agent. Comparison price shopping on the Internet, in: Bots and Other Internet Beasties , J. Williams, ed, SAMS.NET, 1996.
[42]
R. Kumar, P. Raghavan, S. Rajagopalan, and A. Tomkins, Recommendation systems: A probabilistic analysis, Journal of Computer and System Sciences 63 (1) (2001), 42-61.
[43]
I. Leenen, I. Van Mechelen, and A. Gelman, Bayesian probabilistic extensions of a deterministic classification model, Computational Statistics 15 (2000), 355-371.
[44]
L. Li, Z. Yang, K. Somboonviwat, and M. Kitsuregawa, User-assisted similarity estimation for searching related Web pages, in: Proc. of the 18th Conference on Hypertext and Hypermedia , 2007, pp. 11-20.
[45]
Q. Li, K.S. Candan, and Y. Qi, Extracting relevant snippets from Web documents through language model based text segmentation, in: Proc. of the IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology , 2007, pp. 287-290.
[46]
Z. Li, S. Shi, and L. Zhang, Improving relevance judgment of Web search results with image excerpts, in: Proc. of the International World Wide Web Conference , 2008, pp. 21-30.
[47]
H. Lieberman, Autonomous interface agents, in: Proc. of the SIGCHI Conference on Human Factors in Computing Systems , 1997, pp. 67-74.
[48]
Z. Ma, G. Pant, and O. Liu Sheng, Interest-based personalized search, ACM Trans. on Informations Systems 25 (1) (2007), article 5.
[49]
P. Maes and R. Kozierok, Learning interface agents, in: Proc. of the National Conference on Artificial Intelligence , 1993, pp. 459-464.
[50]
P. Maes, R.H. Guttman, and A.G. Moukas, Agents that buy and sell, Communications of the ACM 42 (3) (1999), 81-91.
[51]
O. Malik, Wholesale internet bandwidth prices keep falling, News release, Oct. 7, 2008.
[52]
P. Markopoulos and J. Kephart, How valuable are shopbots?, in: Proc. of the International Joint Conference on Autonomous Agents and Multiagent Systems: Part 3 , 2002, pp. 1009-1016.
[53]
N. Matsuda, T. Hirashima, T. Nomoto, H. Taki, and J. Toyoda, Context-sensitive filtering for the Web, Web Intelligence and Agent Systems 1 (3) (2003), 249-258.
[54]
Medium, Share of Ad Spending, May 2008.
[55]
Microsoft Corp., Bing API Version 2, 2009.
[56]
A. Mislove, K.P. Gummadi, and P. Druschel, Exploiting social networks for Internet search, in: Proc. of the 5th Workshop on Hot Topics in Networks , 2006.
[57]
A. Moshchuk, T. Bragin, D. Deville, S.D. Gribble, and H.M. Levy, SpyProxy: Execution-based detection of malicious Web content, in: Proc. of the USENIX Security Symposium , 2007, pp. 27-42.
[58]
G. Neumann and F. Xu, Mining natural language answers from the Web, Web Intelligence and Agent Systems 2 (2) (2004), 151-170.
[59]
A. Ntoulas, M. Najork, M. Manasse, and D. Fetterly, Detecting spam Web pages through content analysis, in: Proc. of the International World Wide Web Conference , 2006, pp. 83-92.
[60]
J. Pearl, Probabilistic Reasoning in Intelligent Systems , Morgan Kaufmann, San Francisco, 1988.
[61]
Performics, Online Advertsing Industry facts, 2007.
[62]
P. Resnick and H. Varian, Recommender systems, Communications of the ACM 40 (3) (1997), 56-58.
[63]
Reuters, PandaLabs Says Half a Million Computers are Infected with Malicious Bots Every Day, Jan. 18, 2008.
[64]
D.E. Rose and D. Levinson, Understanding user goals in Web search, in: Proc. of the International World Wide Web Conference , 2004, pp. 13-19.
[65]
K. Sadohara, Learning of Boolean functions using support vector machines, in: Proc. of the International Conference on Algorithmic Learning Theory , 2001, pp. 106-118.
[66]
J. Schafer, J. Konstan, and J. Riedi, Recommender systems in e-commerce, in: Proc. of ACM Conference on Electronic Commerce , 1999, pp. 158-166.
[67]
T. Schreiner and J. Sudds, Exploring internet explorer building browser helper objects with visual studio 2005, Microsoft Corp., Oct. 27, 2006.
[68]
A. Schwartz and A. Cooper, Search privacy practices: A work in progress, CDT Report, Aug. 2007.
[69]
D. Shen, Z. Chen, Q. Yang, H.-J. Zeng, B. Zhang, Y. Lu, and W.-Y. Ma, Web-page classification through summarization, in: Proc. of ACM SIGIR Conference on Research and Development in Information Retrieval , 2004, pp. 242-249.
[70]
X. Shen, B. Tan, and C. Zhai, Implicit user modeling for personalized search, in: Proc. of the 14th ACM International Conference on Information and Knowledge Management , 2005, pp. 824-831.
[71]
C. Silverstein, H. Marais, M. Henzinger, and M. Moricz, Analysis of a very large Web search engine query log, SIGIR Forum 33 (1) (1999), 6-12.
[72]
M.D. Smith, The impact of shopbots on electronic markets, Journal of the Academy of Marketing Science 30 (4) (2002), 442-450.
[73]
SOA World Magazine News Desk, Moore's law: We see no end in sight, Says Intel's Pat Gelsinger, May 1, 2008.
[74]
A. Sun, E.P. Lim, and W.K. Ng, Web classification using support vector machine, in: Proc. of the 4th International Workshop on Web Information and Data Management , 2002, pp. 96-99.
[75]
A. Swaminathan, C. Mathew, and D. Kirovski, Essential pages, in: Proc. of the IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology , 2009, pp. 173-182.
[76]
J. Teevan, S.T. Dumais, and E. Horvitz, Personalizing search via automated analysis of interests and activities, in: Proc. of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval , 2005, pp. 449-456.
[77]
L. Ungar and D. Foster, Clustering methods for collaborative filtering, in: Proc. of the Workshop on Recommendation Systems , 1998, pp. 112-125.
[78]
U.S. Environmental Protection Agency ENERGY STAR Program, Report to Congress on Server and Data Center Energy Efficiency, Public Law 109-431, Aug. 2, 2007.
[79]
Vendio Services Inc., What is Dealio?, 2010.
[80]
WebProNews, Microsoft's Yahoo Interest: Patently Paid Search, 2008.
[81]
WebSiteOptimization.com, US Drops to 19th in Broadband Penetration Worldwide - US Broadband Penetration Jumps to 91.5% among Active Internet Users, Bandwidth Report, Sep. 2008.
[82]
H. Weinreich, H. Obendorf, E. Herder, and M. Mayer, Not quite the average: An empirical study of Web use, ACM Trans. on the Web 2 (1) (2008), 1-31.
[83]
WorldWideWebSize.com, The size of the World Wide Web, Nov. 2008.
[84]
O. Zamir and O. Etzioni, Grouper: A dynamic clustering interface to Web search results, Computer Networks 31 (11-16) (1999), 1361-1374.
[85]
J. Yang, H. Seo, and J. Choi, MORPHEUS: A more scalable comparison-shopping agent, in: Proc. of the 5th International Conference on Autonomous Agents , 2001, pp. 63-64.
[86]
W. Yih, J. Goodman, and V.R. Carvalho, Finding advertising keywords on Web pages, in: Proc. of the 15th International Conference on World Wide Web , 2006, pp. 213-222.
[87]
H. Yu, J. Han, and K. Chang, PEBL: Web Page Classification without negative examples, IEEE Transactions on Knowledge and Data Engineering 16 (1) (2004), 70-81.
[88]
E. Zavitsanos, G. Paliouras, G. Vouros, and S. Petridis, Learning subsumption hierarchies of ontology concepts from texts, Web Intelligence and Agent Systems 8 (1) (2010), 37-51.
[89]
B. Zhang, H. Li, Y. Liu, L. Ji, W. Xi, W. Fan, Z. Chen, and W.-Y. Ma, Improving Web search results using affinity graph, in: Proc. of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval , 2005, pp. 504-511.
- Towards improving the online shopping experience: A client-based platform for post-processing Web search results
Recommendations
Improving Ranking Consistency for Web Search by Leveraging a Knowledge Base and Search Logs
CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge ManagementIn this paper, we propose a new idea called ranking consistency in web search. Relevance ranking is one of the biggest problems in creating an effective web search system. Given some queries with similar search intents, conventional approaches typically ...
A Specialized Search Assistant for Learning Objects
The Web holds a great quantity of material that can be used to enhance classroom instruction. However, it is not easy to retrieve this material with the search engines currently available. This study produced a specialized search assistant based on ...
Random walk based rank aggregation to improving web search
In Web search, with the aid of related query recommendation, Web users can revise their initial queries in several serial rounds in pursuit of finding needed Web pages. In this paper, we address the Web search problem on aggregating search results of ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Publisher
IOS Press
Netherlands
Publication History
Published: 01 April 2012
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 14 Nov 2024
Other Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in