A poster discussing feedback in consumer to consumer economic environments

Wu, Hao

Wu, Zhengping

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Preference-based Detailed Feedback Management  for E-Commerce Applications     Zhengping Wu1, Hao Wu2 1Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, CT 2Department of Computer Science, Illinois Institute of Technology, Chicago, IL Introduction In consumer-to-consumer (C2C) e-commerce environments, the magnitude of products and the diversity of vendors have caused confusion and difficulty for consumers to choose the right product from a trustworthy vendor. Feedback system is a widely used solution to help consumers evaluate vendors’ reputations. Some C2C environments have started to provide detailed feedback besides the overall rating system to help consumers distinguish individual vendors from multiple aspects. However, the increase in detailed feedback may add to consumer confusion and increase the time needed to consider all aspects for a reputation evaluation decision. This paper analyzes a typical feedback and reputation system for the e-commerce environment and proposes a novel, perception-based reputation model for individual vendors. Perception-based Fuzzy Logic Here, we take an example to illustrate Perception-based Fuzzy Logic. We use feedback score (F) and the probability that a vendor’s reputation is high (P* ) to build a sample linguistic rule set: “If F is high then P* is high.” “If F is normal then P* is medium.” “If F is low then P* is low.” The probability of “the vendor’s reputation is high” can be represented as an F-granular distribution (Figure 1) and written as: P* = high*high + normal*medium + low*low Then we use Z(F) to represent the fuzzy set in F domain and P(P*) to represent fuzzy set in P* domain. Then, the linguistic rule set can be represented as: “If F is Zi then P* is Pj”, where Zi∈Z(F), Pj∈P(P*). “if… than…” rule is the most widely used but not the only format to present linguistic rule. As long as uncertainties need to be contained in rules, our proposed fuzzy term description can be embedded into any rule format. Using f-granular to describe P*, P* can be written as:  jiji PZP,*F f low normal high P* 0 high low medium Z2 * Pj F P* 0 P1 P2 Pj P3 Z3 * Pj-1 Zi * P4 Z3 * P3 Z2 * P2 Z1 * P2 . . . . . . …… Z1 Z2 Z3 Zi Figure 1. F-granular  Parallel Model Feedback Adjustable Model We propose a parallel model as shown in Figure 3, treating detailed feedbacks as a sub set of all relevant factors, in which detailed feedbacks are as important as general factors so that the reputation can be calculated, depending not only upon overall performance factors but also consisting of user’s preferences on different aspects within detailed feedbacks. Shoppers can define main policy set for general factors and sub policy  set for detailed feedbacks. And these two sets are co-actively processed by reputation generator to calculate a reputation for a vendor.   On the other hand, we propose another model - the feedback adjustable model– in which detailed feedbacks are only used to augment general performance factors,  which use available detailed feedback ratings to adjust the decision-making process in order to meet shoppers’ perceptions toward a vendor. Unlike the parallel model, the feedback adjustable model uses a parameter generator to map detailed feedback ratings into one parameter that has the range from 0 to 1([0,1]). Then this parameter is used in the reputation generation process in order to adjust the calculation of the overall reputation using general feedback information. Comparison of Different Models Figure 4 illustrates the comparisons of reputations generated by different models. RP1 to PR7 represents the general model without detailed feedback, parallel model with sub policy set1, parallel model with sub policy set2, feedback adjustable model, general model without RP index and detailed feedbacks, fuzzy beta reputation model and fuzzy beta reputation model with RP index respectively. Our system provides reputations at three different levels: low, normal and high. The confidence of each level will be represented by a percentage following that level. In order to perform the comparison, we normalize our reputations into a reputation score ([0,1]). Comparison of Shopping Recommendations We set the sub policy set 1 used for the parallel model as our default sub policy set. The first main policy set is defined following human common sense, which always tries to select a low-priced product from a reputable vendor. The second main policy set is defined as an extreme case, which always prefers an expensive product from a reputable vendor. Figure 5 indicates the huge differences between two policy sets. Normally Recommendation 1 Recommendation 2 Highly Not Experiments Environment  To examine the performance and adaptability of the system, we select an unlocked Nokia N900 cell phone as the target product for shopping. Then we run the system with the eBay environment. Hundreds of vendors who sell this cell phone (with the “buy it now” option) are compared in the experiments. And according to the percentage of vendors with/without detailed feedbacks (two categories), we proportionally choose first 7 and 4 vendors from the raw result returned by eBay representing both categories for the comparison. Detailed vendors’ information is shown in table 1. Then we use different models to calculate the reputation of each vendor respectively. Then we add price information into decision-making and provide users a final recommendation following users’ preferences on different policy sets and different models.  Vendor Price Positive   feedback (%) Feedback   score 12 month   feedback Active   Months Item as des.  (rating/# of rating) Communication  (rating/# of rating) Shipping time  (rating/# of rating) Shipping handle  (rating/# of rating) eBay1 465.00 50 4 6 48 N/A N/A N/A N/A eBay2 538.00 100 98 83 39 N/A N/A N/A N/A eBay3 480.99 83.3 32 2 26 N/A N/A N/A N/A eBay4 499.95 100 144 150 23 N/A N/A N/A N/A eBay5 649.66 100 57 1 117 4.4/34 4.5/35 4.9/38 4.4/35 eBay6 449.95 99.6 2616 2894 78 4.9/1712 4.9/1703 4.8/1703 4.9/1703 eBay7 529.99 99 684 551 46 4.8/415 4.8/412 4.8/418 4.8/410 eBay8 540.00 98.4 3974 67 106 4.7/52 4.5/51 4.4/51 4.7/52 eBay9 589.99 98.1 743 775 36 4.8/635 4.7/631 4.8/629 4.8/632 eBay10 599.00 100 141 91 17 4.5/43 4.7/43 4.7/43 4.5/43 eBay11 575.00 98.9 105 95 16 4.8/62 4.8/62 4.6/62 4.8/62 eBay12 499.95 100 132 137 15 5.0/61 5.0/61 4.9/60 4.9/61 eBay13 538.00 99.4 1310 188 50 4.9/142 4.8/140 4.9/141 5.0/136 eBay14 538.99 99.7 1708 316 114 4.9/229 4.6/229 4.9/226 4.9/227 eBay15 490.00 100 1439 1119 97 4.9/905 4.9/902 4.9/899 4.9/899 Figure 2.Parallel Model Figure 3.  Feedback Adj. Model Table 1. Vendors’ information  Figure 5. Comparison of recommendations Figure 4. Comparisons of different reputation models # 43 :  __  __  __ 

Preference-based Detailed Feedback Management for E-commerce Applications

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Preference-based Detailed Feedback Management for E-commerce Applications

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