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Software Agents Role and Predictive Approaches for Online Auctions
1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 11 | Nov -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 2111 Software Agents Role and Predictive Approaches for Online Auctions Sandeep Kumar Assistant Professor, Computer Science & Engineering Department, MSIT-New Delhi, India. ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract – Software agents have become very significant these days. In almost all the activitiestheyarebeingemployed. Here the significance of softwareagents havebeenshown. This is particularly discussed with respect to the online bidding scenario. A light has been put on the model aspectsofsoftware agent being used for the interaction with bidder.The machine learning aspects are also being used for majority of findings these days. The relevant aspects are discussed here. The social media effect have also been shown in this paper. Key Words: Software Agents, Machine Learning, Social Media. 1. INTRODUCTION In today’s innovative technological era,softwareagentshave become a vital framework towards the predictions made in online auctions. The reason for the sameistheirtremendous capability and adaptabilities. They can easily adopt to the dynamic incoming informationandthe changingsbehavioral patterns of bidders. These need not to be manually handled and least level of human intervention is required. This not only ensures their smooth functionality, but it provides quality results in online auction related predictions [1]. 1.1 Bidder Interaction Model To understand the online bidding interactionmodelinonline auction it is important to explore the role of a bidding agent to know the automation in the auction process[2]. Fromthis model, the different bidder interaction module can be identified. These are: Pre-buy Interactions, buy-process interactions and post-buy interactions. The pre-buy interactions include the product search, auction picker and the online bidding operations (Fig. 1.). Inpre-buysearch,the user searches amonglargecollectionofavailableproductson the Internet. Whatever one decides it depends upon the requirement and other aspects of product like its features, price, delivery, etc. Then user searches for the relevant auctions available on web. Normally, abundance of auction for a specific product brings brighter chances of winning it. And not only winning is important, but it may result into the lower price for a particular product during the purchase process [3]. Here bidding agent needs to make intelligent decisions like it needs to roam on different websites across the web and decide in which auction to select to participate in. Bidding agent automation here requires to co-ordinate and communicate with other agents and set the bidding preferences accordingly [4]. To implement this strategy a bidding agent need to possess and develop high level of learning skills. Fig. 1. Bidder interaction model [2]. In buy process interaction phase the subpartsofthesameare purchase order placement and product delivery. The final step, i.e. post buy interaction is very important not only for a bidder but forthe auctioneer as well.Herethesoftwareagent can build bidder profile, later this can be further utilized in order to providethe after sale services. Theseservicescanbe used to personalize the offerings later-on [2]. 1.2 Characteristics of Software Agent From the above discussed phases of bidding process, software agents possess different characteristics. These are explored briefly here: 1. Autonomous: Autonomous agent do not need an external interfere for its operations, atleastdirectly it is not required. It controls its operations directly by its own learning model [5]. 2. Proactive: In the real simulations, the intelligent agent should show proactive behavior. These goals exhibit the behavior in terms of the set objectives [6]. Here matching of pre and post conditions should be handled asdescribed.Abiddingagentwill
2.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 11 | Nov -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 2112 make the decisions according to the own objectives and preferences and analyze the available alternatives. Accordingly, it predicts the results. 3. Reactive: The software bidding agent should be not only be responsivetowardsattainingthepredefined goals but it must be able to respond to the arising situations from time to time. The changing environment and unexpected dynamic situations must responded by it in a very efficient way (Fig.2.) [7; 8; 9]. Fig. 2. Reactive software agent [8]. 4. Social: The software bidding agentshouldbeableto take instructions from the human counterpartsoas to produce a common approach toother’sproblems as well. It should track how other bidders [9]. 5. Intelligent: The software bidding agent must be smart enough so as to explore the surroundings in terms of similarities among bidding. This will assist it to find the surplus bids [10]. 2. Machine Learning Approach For Price Forecasting In MachineLearning Basedapproach [11] the stepscanbeas given below: 1. Colleting the data of online auction: Here data can be collected using the web crawlers and implementing them on a website like the e-bay. 2. Define set of features to be extracted, for example, seller features, item features,Auction Features,Temporalfeatures, etc. 3. Creating Advanced or Meta-class features that are derived from an initial set of features like auction length, shipping charge, start of week etc. Fig. 3. Price forecasting agent [13]. Extracting features from the hidden information in the data using some classifiers/extractors etc. Another example towards forecasting the price is based on forecasting agent [12, 13]. In this, the following four steps work to provide the output: i) Collection of data from the bid server. ii) Perform the clustering based upon the similarities in characteristics of online auctions. iii) On the basis with the results from transformation ofdata into clustering, bid evaluators are invoked to estimate the final price of bid. 3. Significance of Social Media in Price Forecasting The data of social networks have been playing a key role in every aspect of society.Researchersaretryingtoutilizethe data produced, but till now it is largely untapped. The social media can be used to predict on real-world scenarios [14]. Application areas spreading from price prediction [14], recording social trends on a particular event, medicalrelated or even society awareness, all these can beachieved.Insocial media, twitter can be used as one of the most important data sources [14]. This data generated comes from the common mass, so it reduces the chances of being biased. The data achieved through social media platforms are in the form of short text stringshaving ideasofcommonpeople. So techniques like classificationandclusteringcanbeutilized in finding the meaningful information from these sources [15]. This microbloggingwebsiteslikeTwitter,Facebook,etc. are emerging as a common medium as users have come and see and share the contents that are familiar to them [16]. If
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 11 | Nov -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 2113 textual and networking integration of the data can be achieved, the result can be very surprising [16]. This type of approaches like sociological approach to handle noise and short texts (SANT) can perform this classification task efficiently [16].Numbersofframeworkshavebeenpresented in this area. These frameworks are used for sentimental analysis. Though the data produced from social media are unstructured, and it is not easy to drill through thisdata[17]. It is very significant to identify the existing clusters in such unstructured data groups [18]. For this, genetic algorithm can play a key role [18]. Along with these extending applications of graph theory, the use of fitness function can play an important role in predictability of such data [18]. The static and real-time data generated by Twitter can be analyzed using a flexible framework [19]. This framework, or similar other frameworks require to have variousmodules,whichincludeminer,classification,etc.[19]. The output generated here can be in the form of CSV files based on sentimental classification of data [19]. The agent-based modeling showed promising results in the last few years. In this type of research, it finds its wide applicability in health-related issues [20]. It ranges from epidemics to cardiovascular research areas. Replacing the humans fully by the computer is not considered to present a good solution to a problem. Rather the use of visual analytics has a good scope for decision making [21]. Even the complex information represented visually can prove to be very beneficial. The volume of data generated by these social networking platforms is very large. So a model that is capable of analyzing not only the large volume, but the real time flow of data as well. This large data is very significant from a research perspective. There are approaches that combine probabilistic language model for analyzing the customer sentimentsand combines it with classical approaches togive better predictability [22]. There are domains, which requirereal-timedataanalysis. These include usingsocialmediaforemergencyservices[23]. Here visual analytics plays an important problem deriving the meaningful information. There are plugins available that can be used to check the credibility of tweets generated. These assist in evaluating the quality of information produced [24]. The sentiment analytics used in finding political scenarios or knowing more about the consumer feelings abouta particular event [25].Someresearchershave filtered tweets based messages from syntactic filtering language modeling, near duplicate detection and set cover heuristics [26]. Work related to the messages providesthestrengthofthe online sentiment analysis. This sent-strengthcanbedifferent for different languages [27]. Linguistic analysis of a corpus can be used to build a sentiment classifier for deciding about the positive, negative or neutrality of a message [28]. The social media analytics finds its applicability ranging from analyzing public behavior [29]. Health-related monitoring issues [30], to how information is propagating through the social media [31]. 4. CONCLUSIONS The review presented here highlights few significant observations in the present research work. These include, firstly, the localization impact factorisquitesignificant.Here localization means the mass segment of people, having their views on a specific product or service. There needed to have a multidimensional model to observe the impact in a real sense. Thirdly, the predictive machine learning models can be worked upon for achieving the better results. The role of software agents in price prediction of online auctions canbe elaborated. Data mining plays an important role in predicting the end price of online auction. There have been many approaches in data mining that provides good results. Multiple options can be integrated from the number of options available that tends to give the better results. REFERENCES [1] P. Anthony, W. Hall, V. Dang, and N. Jennings, “Autonomous Agents for Participating in Mulitple On- line Auctions,” 2001. [2] M. Indrawan, “Software Agents in ElectronicCommerce: An Overview,” Internet Commer. Softw. Agents Cases, 2001. [3] M. Dass, W. Jank, and G. Shmueli, “Maximizing bidder surplus in simultaneous online art auctions via dynamic forecasting,” Int. J. Forecast., 2011. [4] R. Ashri, I. Rahwan, and M. Luck, “Architectures for Negotiating Agents,” in Multi-Agent Systems and Applications III, Berlin, Heidelberg: Springer Berlin Heidelberg, 2003, pp. 136–146. [5] N. Jennings and M. Wooldridge, “Software agents,” IEE Rev., 1996. [6] M. J. Wooldridge, An introductiontomultiagent systems. John Wiley & Sons, 2009. [7] R. Brooks and R. Brooks, “Intelligence Without Representation,” Artif. Intell., vol. 47, pp. 139--159, 1991. [8] M. Fasli, Agent technology fore-commerce.JohnWiley& Sons, 2007. [9] Z. Maamar, “Association of userswithsoftwareagentsin e-commerce,” Electron. Commer. Res. Appl., vol. 1, no.1, pp. 104–112, 2002. [10] G. Adomavicius and A. Gupta, “Designing intelligent software agents for auctions with limited information feedback,” Inf. Syst., 2009.
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 04 Issue: 11 | Nov -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 2114 [11] R. Ghani and H. Simmons, “Predicting the end-price of online auctions,” Proc. Int., 2004. [12] P. Kaur, M. Goyal, and J. Lu, “A proficient and dynamic bidding agent for online auctions,” Int. Work. Agents Data, 2012. [13] P. Kaur, M. Goyal, and J. Lu, “Data mining driven agents for predicting online auction’s end price,” Data Min. (CIDM), 2011 IEEE 2011. [14] S. Asur and B. Huberman, “Predicting the future with social media,” Agent Technol. (WI-IAT), 2010 IEEE …, 2010. [15] G. Bello, H. Menéndez, and S. Okazaki, “Extracting collective trends from twitter using social-based data mining,” Int. Conf., 2013. [16] X. Hu, L. Tang, J. Tang, and H. Liu, “Exploiting social relations for sentiment analysis in microblogging,”Proc. sixth ACM Int., 2013. [17] C. Marques-Lucena, J. Sarraipa, and J. Fonseca, “Framework for customers’ sentiment analysis,” Intell. Syst., 2015. [18] G. Bello-Orgaz and H. Menéndez, “Adaptive k-means algorithm for overlapped graph clustering,” Int. J.,2012. [19] D. ÁlvaroCuesta, “A Framework formassiveTwitterdata extraction and analysis,” Malaysian J., 2014. [20] A. El-Sayed, P. Scarborough, and L. Seemann, “Social network analysis and agent-based modeling in social epidemiology.” Epidemiologic, 2016. [21] D. Thom, “Visual analytics of social media for situation awareness,” 2015. [22] O. Cheng and R. Lau, “Big data stream analytics for near real-time sentiment analysis,” J. Comput. Commun., 2015. [23] N. Calderon and R. Arias-Hernandez, “Studying animation for real-time visual analytics: a design study of social media analytics in emergency management,” Sci. (HICSS), 2014 , 2014. [24] A. Gupta, P. Kumaraguru, C. Castillo, and P. Meier, “Tweetcred: Real-time credibilityassessmentofcontent on twitter,” Int. Conf., 2014. [25] M. R-Moreno, A. Cuesta, and D. Barrero, “Twitter stream analysis in Spanish,” WIMS’13 Proc. 3rd, 2013. [26] B. O’Connor, M. Krieger, and D. Ahn, “TweetMotif: Exploratory Search and Topic Summarization for Twitter.,” ICWSM, 2010. [27] M. Thelwall, “Heart and soul: Sentiment strength detection in the social web with senti strength,” Proc. Cyber Emotions, 2013. [28] A. Pak and P. Paroubek, “Twitter as a Corpus for Sentiment Analysis and Opinion Mining.,” LREc, 2010. [29] W. Dou, X. Wang, D. Skau, and W. Ribarsky, “Leadline: Interactive visual analysis of text data through event identification and exploration,” Anal. Sci., 2012. [30] C. Chew and G. Eysenbach, “Pandemics in the age of Twitter: content analysis of Tweets during the 2009 H1N1 outbreak,” PLoS One, 2010. [31] J. Zhao, N. Cao, Z. Wen, Y. Song, and Y. Lin, “# fluxflow: Visual analysis of anomalous information spreading on social media,” IEEE Trans., 2014.
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