Abstract:
A method for producing a results list for a search query includes producing a first results list of entries from a search algorithm, the first results list corresponding to a term in the search query, the first results list being ordered according to relevance rankings, modifying a feature of the first results lists by an editor, the modification being made according to an editorial rule, determining a reliability score for the editor, the reliability score being based upon the modification made the editor, combining the modification made by the editor in a combined search index, the combined modification being included in the combined search index according to the determined reliability of the editor, and using the combined search index to produce a second results list which corresponds to the term in the search query, the second results list being ordered according to relevance rankings.

Description:
TECHNICAL FIELD  
         [0001]    This invention relates to using editorial feedback of various Internet objects.  
         BACKGROUND  
         [0002]    The Internet can be viewed as a large collection of documents, for example, text files, web-pages, newsgroup postings or pictures. Internet search engines provide a means of searching through the vast amount of documents to produce a results list of the documents found which match the terms in a search query. Typically the results list is presented as a list of document summaries that includes hyperlinks (“links”) that connect each entry to the appropriate Internet document. The results list is generally ranked by relevance (in relation to the query), with each entry included in the list presented either higher or lower on the list according to the relevance ranking as determined by the search engine being used. The way in which these relevance rankings are determined is constantly evolving as the Internet continues to evolve.  
           [0003]    Search engines apply different algorithms to “filter” the available documents and assign relevance rankings to the documents reviewed. The relevance rankings are generally stored in a search index which corresponds to documents for a specific search term (or related search terms).  
           [0004]    Initially, Internet search engines applied “content-based” filtering which is simply examining the number of times that a query search term appeared within a document, such that the greater the number of times that a search term appeared, the more relevant the document was considered and the higher it was ranked. However, content-based filtering produces rankings which are easily manipulated by the author of the documents reviewed, that is, an author can fill their web-page with multiple copies of the words that they believe will be searched upon and thereby increase the alleged relevance of their web-page.  
           [0005]    More advanced ranking methods apply “link analysis” algorithms, i.e., examining the links contained within a document to other documents with relatively high relevance rankings. However, as with the manipulation of terms in a web-page, as discussed above, an author can increase the number of links to other documents with high relevance rankings in order to increase the alleged relevance of their web-page.  
           [0006]    Editor-controlled search engines use a “staff” of editors (paid or volunteer) to manually select and rank individual web-page documents contained in a results list for a specific search term from a ranked search index. Documents may have their rankings changed, or document may be added and removed from the index over time as the editors perform their reviews. Since there billions of web-page documents available on the Internet, and the number of documents continues to grow at a tremendous pace, the amount of labor needed to maintain a current and complete editor-controlled search index is very high. The Open Directory Project (ODP) is an example of a co-operative editing process that uses a large number of volunteer editors to assess and modify the relevance rankings of documents related to a search term or within a specific category. ODP applies rules to the editor selection process. The end result of ODP is a editor-controlled ranking index that can be searched directly, however, the editorial feedback is not used to improve the efficiency of an automated search engine algorithm.  
         SUMMARY  
         [0007]    According to an aspect of this invention a method for producing a results list for a search query includes producing a first results list of entries from a search algorithm, the first results list corresponding to a term in the search query, the first results list being ordered according to relevance rankings, modifying a feature of the first results lists by an editor, the modification being made according to an editorial rule, determining a reliability score for the editor, the reliability score being based upon the modification made the editor, combining the modification made by the editor in a combined search index, the combined modification being included in the combined search index according to the determined reliability of the editor, and using the combined search index to produce a second results list which corresponds to the term in the search query, the second results list being ordered according to relevance rankings.  
           [0008]    One or more of the following features may also be included: producing a first results list that includes producing a first results list that corresponds to at least one search term, modifying a feature according to an editorial rule that includes modifying any feature of the first results list, modifying a feature according to an editorial rule that includes modifying an allowed changeable feature, the allowed changeable feature being one or more of relevance ranking, entry addition, entry deletion and document summary, modifying a feature includes modifying a feature within a pre-defined range or percentage, modifying an allowed changeable feature that includes modifying a feature within a pre-defined range or percentage, determining a reliability score that further includes comparing a ranking change for an entry by a first editor to a median ranking change for the entry by a second editor, determining a reliability score that further includes comparing a summary change for an entry to a previous summary contained in the first results list, determining a reliability score that further includes determining a difference in a number or a percentage of click-throughs which result from the editor modification, combining the modification made by the editor that further includes multiplying the reliability score by the feature modification, using the combined results to produce a second results list includes using the combined search index by a search engine and wherein the method is performed at least twice.  
           [0009]    According to a further aspect of this invention a method for producing a results list for a search query, the method includes producing a first results list of entries from a search algorithm, the first results list corresponding to a term in a first search query, the first results list being ordered according to relevance rankings, modifying a feature of the first results lists by an editor, the modification being made according to an editorial rule, determining a reliability score for the editor, the reliability score being based upon the modification made the editor, combining the modification made by the editor in a combined search index, the combined modification being included in the combined search index according to the determined reliability of the editor, detecting a difference between the feature contained in the first results list and the corresponding modified feature contained in the combined search index, and determining a rule that corresponds to the detected difference or similarity.  
           [0010]    One or more of the following features may also be included: producing a second results list from the search algorithm, the second results list corresponding to the term in the first search query and modifying the second results lists by applying the rule to a feature contained in the second results list, detecting a difference that further includes detecting a change to a relevance ranking feature, detecting a difference that further includes detecting an added feature, detecting a difference that further includes detecting a deleted feature and detecting a difference that further includes detecting a change to a document summary feature.  
           [0011]    According to a further aspect of this invention a computer program product residing on a computer readable medium having a plurality of instructions stored thereon which, when executed by the processor, cause that processor to produce a first results list of entries from a search algorithm, the first resultst list corresponding to a term in a search query, the first results list being ordered according to relevance rankings, modify a feature of the first lists according to an editorial rule, determine a reliability score for the editorial modification, the reliability score being based upon the editorial modification, combine the editorial modification in a combined search index, the combined modification being includes in the combined search index according to the reliability score of the editor, and use the combined search index to produce a second results list which corresponds to the term in the search query, the second results lists being ordered according to relevance rankings.  
           [0012]    One or more of the following features may also be included: wherein the instructions causing a computer to modify a feature of the first lists according to an editorial rule and further include instructions causing a computer to modify a feature according to an editorial rule, wherein the instruction causing a computer to modify a feature according to an editorial rule and further include instructions causing a computer to modify an allowed changeable feature, the allowed changeable feature being one or more of relevance ranking, entry addition, entry deletion and document summary, wherein the instructions causing a computer to determine a reliability score for the editorial modification and further include instructions causing a computer to compare a ranking change for an entry by a first editor to a median ranking change for the entry by a second editor.  
           [0013]    Embodiments of the invention may have one or more of the following advantages. For example, a combined editorial search index is created that automatically assesses the reliability of each editorial input, which reduces the potential of nefarious editorial input. In some embodiments, a combination of paid and un-paid editors are used in evaluating specific results lists, which further reduces the possibility of nefarious inputs from an editor being included in a search index. Some embodiments apply machine learning techniques to the combined editorial feedback search index, i.e., detecting a change in relevance rankings to a previously generated index for a specific search term, and then determining and storing a rule that reflects the detected change. The stored rule may then be re-played by a search engine, i.e., applying the stored rule to produce a refined results list for the specific search term. The stored rules may also be re-played by a search engine to re-rank results lists for a search term which has not been evaluated by the editorial process.  
           [0014]    The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will be apparent from the description and drawings, and from the claims. 
       
    
    
     DESCRIPTION OF DRAWINGS  
       [0015]    [0015]FIG. 1 is a diagrammatic view of the Internet;  
         [0016]    [0016]FIG. 2 is a diagrammatic view of an editors ranking a results list;  
         [0017]    [0017]FIG. 3 is flowchart of a method for creating a combined editorial search index;  
         [0018]    [0018]FIG. 4A is a flowchart of a machine learning process;  
         [0019]    [0019]FIG. 4B is a flowchart of the method for creating a combined editorial search index;  
         [0020]    [0020]FIG. 5 is a diagrammatic view of another embodiment of a method for creating a combined editorial search index;  
         [0021]    [0021]FIG. 6 is a diagrammatic view of another embodiment of a method for creating a combined editorial search index. 
     
    
       [0022]    Like reference symbols in the various drawings indicate like elements.  
       DETAILED DESCRIPTION  
       [0023]    The Internet and the World Wide Web can be viewed as a collection of hyperlinked documents with search engines as a primary interface for document retrieval. Search engines (e.g., lycos.com) allow the user to enter a query and perform a search based on that query. A list of potential matches is then generated that provides links to potentially relevant documents.  
         [0024]    Referring to FIG. 1, there is shown a number of users  10  accessing the Internet via a network  12  that is connected to Internet server  14 . The Internet server  14  serves web pages and Internet-based documents  16  to user  10 . Internet server  14  typically incorporates some form of database  18  to store and serve documents  16 .  
         [0025]    When user  10  wishes to search for information on a specific topic, user  10  utilizes search engine  20  running on search engine server  22 . User  10  enters a search query  24  into search engine  20 , which provides a results list  26  of potential sources for information related to the search terms of query  24 . Each entry  28  on results list  26  is a document summary for a specific relevant document (i.e., a web-page)  16  on the Internet. These documents  16  may be located on search engine server  22 , Internet server  14 , or any other server (not shown) on the network  12 ; and each document is associated with a uniform resource locator (URL) address. Typically, each entry  28  includes a hyperlink, which, when selected causes a display of the document associated with the URL.  
         [0026]    When search engine  20  provides list  26  to user  10  in response to query  24 , the individual entries in list  26  are arranged in accordance with their perceived level of relevance. Search engine  20  may determine the ranking of the entries  28  on list  26  by examining the documents themselves to determine certain factors, for example, the number of documents linked to the entry, or the presence of the search term within the document itself; etc. The ranking of the entries  28  on list  26  may also be determined from a previously stored search index of rankings in a database  40 . As discussed above, a search index may be created by a search engine algorithm or by storing editor-created rankings.  
         [0027]    Search engines can further enhance their document ranking accuracy by comparing stored queries (query-to-query relationships) to make suggestions to the user concerning modifications or supplemental search terms that would better tailor the user&#39;s query to the specific information they are searching for. Depending on which selection the user makes, the user&#39;s search will be appended and tailored accordingly.  
         [0028]    Referring to FIG. 2, there is shown a number of editors  60  who are evaluating and ranking a results list  50  received from search engine server  22 . Results list  50  includes multiple entries  52  which include relevance ranks, Rank 1 -n, which correspond to the perceived relevance of an entry. As discussed above, the editorial changes to results list  50  are returned to the search engine server  22  for inclusion in an editorially ranked search index (not shown) that is stored in database  40 .  
         [0029]    Referring to FIG. 3, there is shown a number of editors, Editor 1 -EditorN, participating in an editorial feedback process  300 . In operation, search engine  20  produces an initial results list  50  that includes relevance rankings for each entry in the list. Each editor, Editor 1 -EditorN, may then perform modifications to the results list  50  according to set of editorial rules applied through editorial rule processes  310 A- 310 N, respectively. Editorial rule processes  310 A- 310 N provide a way to control which features of the results list may be modified by and how they may be modified by each editor. For example, the editorial rule process  310 A- 310 N may allow a change in the following features: relevance rankings (i.e., changing the position of an entry within the results list), a deletion of an entry, an addition of an entry or a modification to a document summary. The editorial rule processes  310 A- 310 N may allow complete editorial control or may only allow for partial editorial control of the features contained in results list  50 . For instance, if partial editorial control is implemented:  
         [0030]    An editor may be able to change rankings of an entry, but, only within a predefined limit, that is only allowing an editor to move an entry up or down in the rankings by a pre-defined number; or,  
         [0031]    An editor may not be able to move an entry which is ranked at a pre-defined percentage above or below another entry; or,  
         [0032]    An editor may not be allowed to add an entry to the results list unless the ranking associated with the new entry is at a certain pre-defined level; or,  
         [0033]    An editor may not be allowed to change document summaries; or,  
         [0034]    An editor may only be allowed to make partial changes to a document summary.  
         [0035]    Please realize that the editorial rule process  310  applies a set of rules to control which features of a results list may be modified and how they may be modified.  
         [0036]    The editorial changes made to the features of the results list  50  by editors  310 A- 310 N are stored in modified results lists  350 A- 350 N, respectively. Reliability process  320  then applies a set of rules to determine a reliability score for each editor. Reliability process  320  then uses the determined reliability score for each editor together with the changes stored in modified results list  350 A- 350 N to weight the individual feature changes made by each editor into a combined editorial search index  360 . The reliability process  320  provides quality control to the process  300 , that is, avoiding the input of un-reliable editorial changes, or nefarious inputs (i.e., an editor who has attempted to make changes to favor an entry without good reason). In operation, if the editor maintains a high reliability rating then the changes made by that editor are given more weight in the combined editorial search index  360 . For example, reliability process  320  may include determining a reliability score by:  
         [0037]    Comparing a ranking change by an editor to the median change made by other editors to the same ranking, and if the difference is greater than a pre-defined limit, then the reliability score of the editor would be lowered; or,  
         [0038]    (If a document summary may be changed by an editor) comparing words in the new summary to words in the old summary and if a pre-defined percentage (or number) of words included in the new summary do not match the old summary (or in the document itself) then the reliability score of the editor would be lowered; or,  
         [0039]    Comparing how far up or down an entry is moved within results list  50  by an editor to the median movement made to that entry by other editors, and if the movement is greater or lower than the median by a pre-defined limit, then the reliability score of the editor would be lowered; or,  
         [0040]    Determining the ‘click-through’ increase or decrease as a result of an editorial change in the results list (i.e., if Internet users actual selections of the links included in a modified entry increase or decrease by a pre-defined limit, then the reliability score of the editor can also be changed.)  
         [0041]    Please realize that any heuristic method could be used to rate editorial reliability.  
         [0042]    Combined editorial search index  360  may be used directly by search engine  20  to produce other results lists  50  corresponding to the rankings stored in index  360 .  
         [0043]    Referring now to FIGS. 3 and 4A, there is shown machine learning process  370  which may be included in process  300 . Machine learning process  370  includes a detection process  372  that compares/contrasts the similarities/differences between the initial results list  50  and the combined editorial search index  360 . Detected changes or similarities from detection process  372  are then used by a determine rule process  374 , to determine a rule related to the detected change or similarity. The determined rule is then stored  376  for possible use by search engine  20  to produce a refined results lists  50 A which is ranked according to the stored rules  380 . Detection process  372  may include detecting that:  
         [0044]    an entry has been deleted from results list  50 ; or,  
         [0045]    an entry has been added to combined search index  360 ; or,  
         [0046]    an entry has been lowered in ranking; or,  
         [0047]    an entry has been raised in ranking; or,  
         [0048]    a summary has been changed.  
         [0049]    Machine learning process then determines  374  a rule to reflect the detected change in rankings, and stores that rule  376  for use by search engine  20 . For example, for a search term: “Michael Douglas”, the combined editorial search index reflects a change in ranking position from position 10 to position 4 for a document related to “Catherine Zeta-Jones” (with a URL of 117). Machine learning process  370  stores  376  a rule that “URL 117” was promoted from position 10 to position 4 for the search term “Michael Douglas”. If a new results list is produced which includes an entry corresponding to URL 117 at position 10 (or somewhere near position 10), the search engine can apply (“replay”) the stored rule which will cause the entry corresponding to URL 117 to be moved to position 4 within the new results list. Please realize that the stored rule is generally broadened to capture the “intent” of the learned change, i.e., the stored rule above may promote URL 117 to position 4 from a range of other positions, not just from position 10, i.e., promoting URL 117 to position 4 from a positions ranging from 5 to 15.  
         [0050]    Results lists may be produced from a single search term, or may be produced from several related terms, i.e., terms that produce a results list that includes a large number of common entries. By grouping results lists into common categories the number of results lists are reduced and the number of editors required to evaluate the results lists is also reduced. Therefore, a group of editor may be assigned to a specific search term, or a category of related terms, so the editors may be registered to that specific term or category and receive notification when new documents are available and require editorial review. Similarly, the machine learning techniques may be applied to learn and store rules based on a common category of queries, i.e., the search term “Michael Douglas” and all search terms considered common to this search term may have a rule applied to a defined category.  
         [0051]    Often a small number of search terms are common to a relatively large number of search queries, that is, many users are searching for the most popular related terms. Therefore, for those highly popular terms, the editorial group assigned may include paid editors to augment and supervise the volunteer editorial process. This would allow for a more reliable review of the editorial changes before they are combined and provide for more control of the combined search index.  
         [0052]    Now referring to FIG. 4B, there is shown a method  400  for creating a combined editorial search index, and, optionally, applying machine learning techniques to the search index to determine a machine learning rule. Method  400  includes: producing  410  a results list for a search term (or related search terms) by applying a search algorithm to a set of documents, changing  420 , by an editor, a results list feature according to a set of editorial rules, determining  430  the predicted reliability of the changed feature and/or the predicted reliability of the editor and combining  440  the weighted editorial changes into a combined editorial search index. Method  400  may also include applying  370  machine learning techniques to determine and store a rule based on a detected change between the initial results lists and the combined search index.  
         [0053]    Now referring to FIG. 5, there is shown a computer program product  550  residing on a computer readable medium  552  having a plurality of instructions  554  stored thereon. When executed by processor  556 , instructions  554  cause processor  556  to: produce  540  a results list for a search term (or related search terms), input  542  editorial changes of features in the results list by applying a set of editorial rules, determine  544  the reliability score for an editor and combine  546  weighted editorial changes into a combined search index. Computer program product  550  may also include instructions  554  causing processor to: apply  548  machine learning techniques to determine and store a rule corresponding to a detected change between the results list and the combined search index.  
         [0054]    Typical embodiments of computer readable medium  552  are: hard drive  564 ; tape drive  566 ; optical drive  568 ; RAID array  570 ; random access memory  572 ; and read only memory  574 .  
         [0055]    Now referring to FIG. 6, there is shown a processor  600  and memory  602  configured to: produce  540  a results list for a search term (or related search terms), input  542  editorial changes of features in the results list by applying a set of editorial rules, determine  544  the reliability score for an editor and combine  546  weighted editorial changes into a combined search index. Processor  600  and memory  602  may also be configured to: apply  548  machine learning techniques to determine and store a rule corresponding to a detected change between the results list and the combined editorial search index.  
         [0056]    Processor  600  and memory  602  may be incorporated into a personal computer  610 , a network server  612 , or a single board computer  614 .  
         [0057]    A number of embodiments of the invention have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spirit and scope of the invention. Accordingly, other embodiments are within the scope of the following claims.