Abstract:
A system and method for reputation rating is disclosed. The method discloses: collecting a set of reputation ratings on a target entity from a set of reputation rating entities; attributing a weight to each of the reputation ratings based on a set of filtering criteria; and combining the weighted reputation ratings to generate a filtered reputation rating with respect to the target entity. The system discloses various means, mediums and systems for effecting the method.

Description:
BACKGROUND OF THE INVENTION  
       [0001]     1. Field of the Invention  
         [0002]     The present invention relates generally to reputation rating systems and methods, and more particularly to filtering reputation ratings with online networks.  
         [0003]     2. Discussion of Background Art  
         [0004]     In the context of e-commerce, reputations often involve a rating system in which parties to a transaction rate each other based on whether they fulfilled the terms of the exchange as promised (e.g., as provided by eBay). Reputation mechanisms help establish trust in economic transactions where some aspects of a transaction are not readily observable by some of the participants, at least prior to completing the transaction. For example, whether the quality of a good or service offered for sale is as good as the vendor claims. People considering new transactions then use the ratings as part of their decision of whom to do business with.  
         [0005]     One difficulty with applying a ratings-based reputation system is the possibility of manipulating ratings either through collusion within groups of friends or the creation of false identities. Such groups can give mutually high ratings in spite of poor actual performance, distorting the reported reputation values. To help address this problem, several groups have proposed using information available in social networks.  
         [0006]     One approach has been to construct a social network from past ratings given by one user to another based on just the most recent interaction. Users can rate anyone they know, whether they are a social contact or someone they have conducted a business transaction with. Ratings are then filtered through the social network to produce personalized results for each user.  
         [0007]     There are two disadvantages to this approach. The first is that it does not distinguish between actual social contacts and business transactions. Hence one cannot filter ratings based only on actual social contacts. It also makes it susceptible to collusion, since friends can rate each other highly and these ratings are treated the same as ratings based on business transactions.  
         [0008]     The second disadvantage is that it only considers a single rating from any one person no matter how much experience, i.e., number of transactions, they may have with the individual one wishes to obtain a rating for. While this approach may limit how much friends can inflate each other&#39;s ratings by repeatedly giving high praise to one another, it discards a great deal of potentially useful information, namely the amount of experience a person has with a particular vendor.  
         [0009]     A second approach to using social networks is as an implicit rating system. In this case, an entity&#39;s position in a social network gives some indication of that entity&#39;s reputation, without requiring an explicit effort on the part of other network members to provide reputation ratings on that entity. This approach is useful to the extent that social connectivity correlates with the entity&#39;s likely behavior with respect to business transactions. Automated management of reputation ratings, both for service quality and ratings reliability, can also aid in producing a reliable reputation rating mechanism. Unfortunately, the available social network may have only marginal relation to how well the entity its customers, in which case explicit ratings are potentially much more relevant for reputations.  
         [0010]     In response to the concerns discussed above, what is needed is a system and method for reputation rating that overcomes the problems of the prior art.  
       SUMMARY OF THE INVENTION  
       [0011]     The present invention is a system and method for reputation rating. The method of the present invention includes the elements of: collecting a set of reputation ratings on a target entity from a set of reputation rating entities; attributing a weight to each of the reputation ratings based on a set of filtering criteria; and combining the weighted reputation ratings to generate a filtered reputation rating with respect to the target entity. The system of the present invention includes all means, mediums and systems for effecting the method.  
         [0012]     These and other aspects of the invention will be recognized by those skilled in the art upon review of the detailed description, drawings, and claims set forth below.  
     
    
     BRIEF DESCRIPTION OF THE DRAWINGS  
       [0013]      FIG. 1  is a dataflow diagram of one embodiment of a system for reputation rating;  
         [0014]      FIG. 2  is a flowchart of one embodiment of a root method for reputation rating; and  
         [0015]      FIG. 3  is a flowchart of one expanded embodiment of the root method for reputation rating.  
     
    
     DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT  
       [0016]     The present invention uses available online networks to make it more difficult to subvert reputation mechanisms (e.g. spoofing or collusion) used to rate entity&#39;s with respect to their e-commerce transactions while maintaining flexibility to include differing user views on the significance of various raters, using various filtering methods.  
         [0017]     With reduced opportunities for spoofing or collusion, participants are likely to regard reputation ratings as more accurately reflecting an entity&#39;s actual e-commerce behaviors. The availability of more accurate reputation information has also been shown to promote better behavior and higher economic efficiency in other settings.  
         [0018]     While online networks are fairly new, they are growing rapidly, and the fact that these networks are available online allows automated use of their structure for a variety of tasks, beyond just the filtering of reputation ratings discussed herein.  
         [0019]      FIG. 1  is a dataflow diagram of one embodiment of a system  100  for reputation rating. To begin, a target entity  102  (i.e., the person or business to have their reputation rated) establishes an online presence within an online network  104 .  
         [0020]     The online network  104  is herein defined as one containing information on relationships among entities (e.g. people, businesses, etc.) either directly or via their behavior. Online networks typically consist of links among entities indicating various forms of relationship, social or otherwise. Online networks containing such relationship information are preferred as compared to more general online networks, such as those including just “people connected to the internet” and responding to email, instant messages, and so on.  
         [0021]     A range of services, including Friendster, LinkedIn, and Spoke (see www.friendster.com, www.linkedin.com, and www.spoke.com), build online networks. These networks have rapidly acquired millions of entities and assist them in forming new social or business contacts or relationships through the contacts they already have. Entities either manually enumerate their contacts or these are gathered automatically from an entity&#39;s e-mail correspondence. Additional sources from which social connections can be automatically harvested include links on web home pages, common authorship of papers, and webs of trust for decentralized cryptographic keys.  
         [0022]     While the online network  104  is preferably an online social network, those skilled in the art will recognize that other types of network information may be used as well, such as credit card transaction information, and phone call records.  
         [0023]     A system manager  106  collects a set of reputation ratings on the target entity  102  from a set of reputation rating entities  108  through  110  who have provided such rating data over the network  104 . The system manager  106  stores the reputation ratings in a reputation ratings database  112 .  
         [0024]     An inquirer  114  contacts the system manager  106  and requests the target entity&#39;s  102  reputation rating. The inquirer  114  is an entity who is attempting to gain information about the target entity&#39;s reputation. The inquirer  114  is typically a person or business interested in establishing a business relationship with or purchasing a good or service from the target entity  102 .  
         [0025]     The system manager  106  requests a set of filtering criteria from the inquirer  114 . The set of filtering criteria is used to classify (i.e. assign) the reputation rating entities  108  through  110  and weight their respective reputation ratings. The system manager  106  stores the set of filtering criteria in a filtering criteria database  116 .  
         [0026]     An entity classification module  118  assigns the reputation rating entities  108  through  110  into either a default set of classes or a set of classes which have been defined by the filtering criteria provided by the inquirer  114 . Note, that some reputation rating entities  108  through  110  may be assigned to more than one class.  
         [0027]     In one example, the reputation rating entities  108  through  110  are classified based on how “close” the reputation rating entities are to the target entity  102 . Closeness is defined either by a default set of criteria, or based on the inquirer&#39;s  114  filtering criteria. For example, if “closeness” is predefined as the target entity&#39;s  102  immediate social circle (e.g. perhaps including family members, friends, classmates, etc.), then the entity classification module  118  examines the relationships between the reputation rating entities  108  through  110  and the target entity  102  within the online network  104  and identifies which of the reputation rating entities fall within the target entity&#39;s  102  immediate social circle.  
         [0028]     In another example, the reputation rating entities  108  through  110  are classified based on how “close” the reputation rating entities  108  through  110  are to the inquirer  114  according to either the same or a different “closeness” definition. In this way the inquirer&#39;s  114  friends can be singled out and, later in this method, have their reputation ratings given greater weight (e.g. emphasize your friends).  
         [0029]     In another example, the reputation rating entities  108  through  110  are classified based on how “close” the reputation rating entities  108  through  110  are to one or more of the reputation rating entities  108  through  110  according to some predetermined “closeness” definition. In this way the inquirer  114  can separate out particular reputation rating entities to whom, later in this method, the inquirer  114  can either emphasize or deemphasize such reputation rating entities&#39; reputation ratings (e.g. deemphasize their friends).  
         [0030]     In yet another example, the reputation rating entities  108  through  110  are classified based on whether the reputation rating entities  108  through  110  are members of a predefined sub-set of the online network  104 . One sub-set, could be whether a reputation rating entity is a member of a particular social network so that reputation rating entities having a false identity can be selected out (e.g. a reputation rating entity without connections, or a reputation rating entity having exactly a same set of connections within the online network as another a reputation rating entity). Thus, target entities, hoping for a fair reputation rating, would be encouraged to fully disclose all of their social network connections over the online network  104  so as not to have certain reputation rating entities improperly tagged as having a false identity.  
         [0031]     Another sub-set could be defined to include only the target entity&#39;s  102  near neighbors in the online network (e.g. professional contacts), based on the inquirer&#39;s  114  belief that the reputation ratings provided by such professional contacts would be based on better information which would tend to outweigh the potential for collusion by such professional contacts with respect to the target entity. An example of this is asking for physicians&#39; opinions about other physicians they have worked with.  
         [0032]     Yet another sub-set can be defined based on the experience a reputation rating entity may have with the target contact  102  (i.e. entities who have posted ratings on the target entity  102 ). An example of this would be reputation rating entities who have actually purchased goods from the target entity  102  and have made their prior business relationships available as part of the online network  104 .  
         [0033]     Once the reputation rating entities  108  through  110  have been assigned into one or more classes, a reputation rating weighting module  120  attributes a weight to each of the reputation ratings based on a default weighting schema, or on the filtering criteria provided by the inquirer.  
         [0034]     For example, a reputation rating from a particular reputation rating entity is weighted based on how “close” the particular reputation rating entity is to the target entity  102 . Thus, the inquirer  114  can either exclude (i.e. zero weight) or less heavily weight reputation ratings from the target entity&#39;s  102  immediate social circle under an assumption that said circle would provide reputation ratings biased in the target entity&#39;s favor.  
         [0035]     In another example, a reputation rating from a particular reputation rating entity is weighted based on how “close” the particular reputation rating entity is to the inquirer  114 . Thus, the inquirer  114  can more heavily weight reputation ratings from the inquirer&#39;s  114  own immediate social circle under an assumption that said circle would provide reputation ratings more in line with the inquirer&#39;s  114  own biases (e.g. emphasizing “word of mouth” ratings).  
         [0036]     In another example, a reputation rating from a particular reputation rating entity is weighted based on how “close” the particular reputation rating entity is to one or more of the reputation rating entities  108  through  110 . Thus, the inquirer  114  can more heavily weight reputation ratings from groups including one or more known experts in a particular field, or exclude reputation ratings from groups known to host derogatory web sites with respect to the target entity&#39;s  102  business dealings.  
         [0037]     In yet another example, a reputation rating from a particular reputation rating entity is weighted based which sub-sets of the online network  104  the particular reputation rating entity is a member of. Thus, the inquirer  114  can more heavily weight reputation ratings from entities who are members of a professional organization and who have previously had business dealings with the target entity  102 .  
         [0038]     Next, the system manager  106  combines the weighted reputation ratings to generate a filtered reputation rating for the target entity  102 . Those skilled in the art recognize that the weighted reputation ratings may be combined according to a variety of different mathematical formulas. Such formulas include an average reputation rating, a median reputation rating, as well as others. Thus, one of the present invention&#39;s benefits is for users to select various combining criteria. For example, if a target entity&#39;s reputation is decreasing over time, even though still with a high average value due to many well-rated transactions in the past, some users may pick a combining function that emphasizes recent history rather than just an average over all the ratings.  
         [0039]     The present invention&#39;s use of a variety of reputation rating filtering criteria, based on the inquirer&#39;s  114  preferences, a set of defaults, and additional available information (e.g., content of web home pages), gives flexibility in interpreting the reputation ratings available over the online network  104 . Those skilled in the art will know of other ways in which the reputation rating entities can be assigned and their respective reputation ratings weighted.  
         [0040]     Using the relationships within the online network  104  to filter the reputation ratings makes spoofing the reputation system more difficult. For instance, altering reputation scores requires collusion not only among friends, but also those further removed in the network, e.g., of friends of friends, etc. which is more difficult. Moreover, if users use a variety of filtering strategies, a vendor attempting to spoof one kind of filter could in fact be detrimental with respect to another.  
         [0041]     The present invention invention&#39;s use of assigning and filtering should be highly effective since reputation rating entities who may deliberately alter revealed links within the online network  104 , in an attempt to hide collusion with respect to their reputation ratings, risk losing the other benefits for which such networks are constructed, such as to obtain business referrals. Moreover, large-scale analysis of social networks can uncover at least some forms of collusion. For example, web pages colluding to alter their search engine ranking can be identified and removed if they all have a similar number of links. Alternately, collusion could alter the relative abundance of motifs (small subgraphs), arousing suspicion if it differs significantly from that of social networks in general. Also, the high clustering in social networks (i.e., two friends of a person are much more likely to be friends themselves than would be the case in a random graph) means that collusion among friends to hide their mutual link would usually not greatly increase the distance between them in the social network. Hence a filter based on social network distance (i.e. “closeness”) would be relatively insensitive to such deliberately altered links.  
         [0042]     As a specific example implementation of the present invention, an inquirer wants to enter into a business transaction with one of a set of target entities. The target entities are members of an online network and are respectively associated with a set of reputation ratings {r 1 , . . . ,r n } generated by “n” reputation rating entities within the online network. An average, unfiltered, reputation rating for each target entity is equal to (r 1 + . . . r n )/n.  
         [0043]     However, using the filtering criteria supplied by the inquirer, a weighted average reputation rating r=(w 1 r 1 + . . . +w n r n )/(w 1 + . . . +w n ) can be generated for each of the target entities. If the inquirer specifies only a “closeness” filtering in the filtering criteria, each of the weights are determined by a distance d i  between each of the target entities and an i th  reputation rating entity. Exactly how the weights are assigned based on the distance depends on additional parameters within the filtering criteria provided by the inquirer. For example, to filter out (i.e. assign zero weight to) reputation ratings from all reputation rating entities within distance “two” of the target (i.e., the target&#39;s friends and friends of friends), set w i =1 if d i &gt;2 and set w i =0 otherwise. The inquirer receives these weighted ratings for all of the target entities and then decides with whom to do business.  
         [0044]      FIG. 2  is a flowchart of one embodiment of a root method  200  for reputation rating. The method  200  begins in step  202 , where a set of reputation ratings on a target entity are collected from a set of reputation rating entities. Next, in step  204 , a weight is attributed to each of the reputation ratings based on a set of filtering criteria. Then in step  206 , the weighted reputation ratings are combined to generate a filtered reputation rating with respect to the target entity. The root method  200  is discussed in further detail with respect to  FIG. 3 .  
         [0045]      FIG. 3  is a flowchart of one expanded embodiment  300  of the root method for reputation rating. To begin, in step  302 , a target entity  102  establishes an online presence within an online network  104 . In step  304 , a system manager  106  collects a set of reputation ratings on the target entity  102  from a set of reputation rating entities  108  through  110  who have provided such rating data over the network  104 . In step  306 , the system manager  106  stores the reputation ratings in a reputation ratings database  112 . In step  308 , an inquirer  114  contacts the system manager  106  and requests the target entity&#39;s  102  reputation rating. In step  310 , the system manager  106  requests a set of filtering criteria from the inquirer  114 . In step  312 , the system manager  106  stores the set of filtering criteria in a filtering criteria database  116 .  
         [0046]     In step  314 , an entity classification module  118  assigns the reputation rating entities  108  through  110  into either a default set of classes or a set of classes which have been defined by the filtering criteria provided by the inquirer  114 . For example, in step  316 , the reputation rating entities  108  through  110  are classified based on how “close” the reputation rating entities are to the target entity  102 . In step  318 , the reputation rating entities  108  through  110  are classified based on how “close” the reputation rating entities  108  through  110  are to the inquirer  114  according to either the same or a different “closeness” definition. In step  320 , the reputation rating entities  108  through  110  are classified based on how “close” the reputation rating entities  108  through  110  are to one or more of the reputation rating entities  108  through  110  according to some predetermined “closeness” definition. In step  322 , the reputation rating entities  108  through  110  are classified based on whether the reputation rating entities  108  through  110  are members of a predefined sub-set of the online network  104 .  
         [0047]     In step  324 , a reputation rating weighting module  120  attributes a weight to each of the reputation ratings based on a default weighting schema, or on the filtering criteria provided by the inquirer. For example, in step  326 , a reputation rating from a particular reputation rating entity is weighted based on how “close” the particular reputation rating entity is to the target entity  102 . In step  328 , a reputation rating from a particular reputation rating entity is weighted based on how “close” the particular reputation rating entity is to the inquirer  114 . In step  330 , a reputation rating from a particular reputation rating entity is weighted based on how “close” the particular reputation rating entity is to one or more of the reputation rating entities  108  through  110 . In step  332 , a reputation rating from a particular reputation rating entity is weighted based which sub-sets of the online network  104  the particular reputation rating entity is a member of. Next, in step  334 , the system manager  106  combines the weighted reputation ratings to generate a filtered reputation rating for the target entity  102 .  
         [0048]     While one or more embodiments of the present invention have been described, those skilled in the art will recognize that various modifications may be made. Variations upon and modifications to these embodiments are provided by the present invention, which is limited only by the following claims.