Data processing: database and file management or data structures – Database design – Data structure types
Reexamination Certificate
1999-05-18
2002-04-09
Breene, John (Department: 2177)
Data processing: database and file management or data structures
Database design
Data structure types
Reexamination Certificate
active
06370526
ABSTRACT:
TECHNICAL FIELD
The present invention relates to a method and apparatus for presenting a plurality of objects in a ranking order according to a user's preferences with respect to those objects.
BACKGROUND ART
Society is experiencing explosive growth in the amount of information available through electronic media. The challenges raised by this growth are many, however, one of the most significant, if not the most significant challenge faced by the information technology industry is being able to process, organize and present this mass of information to users in an efficient manner. In most cases it is possible to treat the information as arbitrary types of objects, i.e., objects of related data treated as a unit. Examples of these types of objects include electronic mail messages within an e-mail system, documents in a document organization system, the results of a list of documents returned by a search engine or perhaps the information retrieved as a result of queries processed against a database.
Attempts have been made to address this challenge by presenting the set of objects, not in an arbitrary order, but instead in an order allowing a potential user to concentrate on “more important” objects while skimming through, or perhaps even neglecting, the “less important” objects.
Various technologies have been proposed for the classification and ranking of objects. For instance the concept of an urgency flag as utilized in present day e-mail systems is well known. An urgency flag is an indication attached to an object representing the importance of that object for the addressee. A disadvantage of this approach is that the degree of urgency is determined solely by the author, thus ignoring the importance that a reader/addressee may attach to the information. “Junk” mail would be an example where the degrees of urgency between sender and receiver could vary greatly. Moreover, this technique does not easily allow multiple recipients of the information to be treated differently with respect to the urgency of the message (perhaps only by using different mailing tasks could this flexibility be accomplished).
Another common ranking/classification technique is that used in present day search engines. Typically these engines sort a list of search “hits” based solely upon the search pattern that is entered into the engine. Because these search engines ignore user preferences that exist amongst the various objects retrieved, such ranking techniques have the disadvantage of supplying a user with “hits” that may match the search pattern, but are not within the context of the user's query.
Agent techniques are yet another commonly utilized method of preprocessing information, such as the filtering of unwanted incoming e-mail messages. Although somewhat effective, these filtering techniques have the disadvantage of requiring that the filter criteria be explicitly specified for each potential user of the information.
Sort options, such as the sorting of e-mail messages by author, subject, or other specified fields, are commonly applied in today's various systems and products. While these techniques do generally offer a rough ordering scheme, typically none of the offered sort criteria perfectly maintains a user's preferences with respect to the information being managed.
Data mining and text mining technologies exploit cluster techniques in order to segment documents into groups whose members share some common group characteristic. Again, however, the clusters typically employed in today's systems do not usually reflect a user's particular preferences with respect to the information being managed.
Another classification technique used today is that which processes information by first executing some type of training sequence on the data. An example is a system that automatically transfers received e-mail messages into predefined folders. While effective, these systems typically require a significant amount of up-front training before they can effectively process a set unknown of objects. Furthermore, this time consuming training sequence must be re-executed whenever it becomes outdated as a result of the need to process new types of information. Thus, the technique is rather inefficient and the quality of the resulting classification is limited by the robustness of the training set.
Finally, an approach for optimizing the ranking order of a set of objects based on user preferences is proposed in D. E. Rose et al., U.S. Pat. No. 5,724,567. The method disclosed compares the results of content based ranking algorithms (such as those employed in search engines) and/or collaborative filtering techniques (which are based on explicit input from other users of the information) to a user profile in order to generate the ultimate ranking order. The user profile is created using a relevance-feedback approach which requires users to enter information into the system regarding the relative importance of the information being processed. A drawback of this approach is that the resultant ranking of information is only as accurate as the feedback provided by the users of the information. Furthermore, collecting this added information requires users to explicitly enter feedback in response to system queries which detracts from the overall use of the application.
It is therefore a purpose of this invention to provide a method and apparatus for presenting a plurality of objects in a ranking order reflecting a user's preferences with respect to those objects, while easing and improving the task of describing the characteristics of the user preferences upon which the ranking order will be based.
SUMMARY OF THE INVENTION
The invention relates to a computerized method of presenting a plurality of objects in a ranking order. The objects are presented in a ranked order according to a calculated object preference. Object preferences are determined using a preference model that is based upon a user's access actions to a group of objects. This preference model is adaptively developed using the information resources associated with a user's normal interaction with the group of objects being ranked. Because the information gathered regarding object preferences is implicit to normal user activities, the adaptive development of the preference model and continual recalculation of object preferences is completely transparent to the user.
This approach offers advantages in productivity and ease-of-use over methods that require users to explicitly enter ranking information into the system during a so-called training phase. These types of applications, such as the collaborative filtering method discussed above, can often require users to invest more time and effort in training than the benefit they can expect to receive in the form of object organization. Moreover, the proposed method utilizes the most reliable information available to determine a user's preferences with respect to a group of objects, namely, the user's own access patterns to that particular set of objects.
Reliance on a user's opinion of the “importance” of an object as opposed to their “preference” for that object, as determined through actual patterns of use, often leads to misleading results. For example, an “out-of-the-office” message may certainly be important at the time of receipt, however, an analysis of the user's access patterns would likely yield that their preference for accessing this type of information is actually quite low. In addition, using implicit information resources can yield preferences for certain types of objects that a user may not even be aware exist. Such preferences would be ignored in a system relying solely on explicit importance scores to determine object preferences, thus exemplifying the advantages of the proposed method and apparatus.
BRIEF DESCRIPTION OF THE DRAWING
The foregoing and other purposes, aspects and advantages of the invention will be better understood from the following detailed description with reference to the drawing, in which a general flow chart
Agrawal Rakesh
Arning Andreas
Seiffert Roland
Srikant Ramakrishnan
Breene John
Driggs, Lucas, Brubaker & Hogg
International Business Machines - Corporation
Rayyan Susan
LandOfFree
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