1 / 32

Implicit feedback: Good may be better than best

niveditha
Télécharger la présentation

Implicit feedback: Good may be better than best

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

    1. Implicit feedback: Good may be better than best Steve Lawrence

    3. Web Xanadu (1960) Improved design, fixes all of these limitations Essentially unused The web Widely used Disadvantages of the improved design Extra effort imposed on users Added complexity in the system Extended development time e.g., if link consistency is enforced, no longer can anyone make information available simply by putting a file in a specific directory The web has become very popular in part due to its limitations Good may be better than best

    4. Web vs. Xanadu Ted Nelson Much credit: hypertext, inspiration for the web, Lotus notes, HyperCard More to Xanadu not covered here (transclusion, bidirectional links, version management) According to Nelson: On both the desktop and world-wide scale, culturally and commercially, we are poorer for these bad tools [the web] The World Wide Web is precisely what we were trying to prevent

    5. CiteSeer CiteSeer Metadata not required for submission Specific citation formats not required More optimal system? Require manual submission which specifies title, author, etc. (CORR) Require citations to be submitted in a specific form (Cameron) CiteSeer is likely to contain more errors Error rate on articles not processed is 100% Value of explicit feedback not obtained is 0 Much lower overhead and complexity for users

    6. Implicit vs. explicit feedback Explicit feedback Overhead for the user Implicit feedback No overhead for the user Implicit feedback may be better than explicit feedback because you may not be able to get sufficient explicit feedback Other issues - accuracy of feedback

    7. Good may be better than best Not a binary choice Often many possible systems Also Worse is better Best is the worst enemy of good MIT approach vs. New Jersey approach for design (Gabriel) The increased overhead, complexity and/or cost (for the system and/or the users), and extended development times of more optimal systems may make them far less successful than alternatives

    8. Convenience of access 119,924 conference articles (bibliographical data from DBLP)

    9. Explicit metadata usage Only 34% of sites use description or keywords tags on their homepage Analyzed 2,500 random servers 0.3% of sites contained Dublin Core tags Attention is the scarce resource. Herb Simon (1967) Difficult to obtain explicit feedback

    10. Implicit vs. explicit feedback Limitations of implicit feedback Hard to determine the meaning of a click. If the best link is not displayed, users will still click on something Click duration may be misleading People leave machines unattended Opening multiple windows quickly, then reading them all slowly Multitasking Limitations of explicit feedback Spam Inconsistent ratings

    11. CiteSeer

    12. CiteSeer Scientific literature digital library Over 600,000 documents indexed Earths largest free full-text index of scientific literature (Los Alamos arXiv about 200,000 papers) Over 20,000 hosts accessing the site daily Accesses from over 150 countries per month Over 10 requests per second at peak times

    13. Improving implicit feedback Have to go to details page before getting link to article Have seen abstract before downloading Shown context of citations before downloading

    14. No download link

    15. Document information page

    16. Citation context

    17. CiteSeer: explicit feedback Document ratings and comments

    18. CiteSeer: explicit feedback Allow users to correct errors Authors may be motivated to correct errors relating to their own work How many explicit corrections? (About 600,000 papers) How many explicit ratings? (percentage of document accesses)

    19. Explicit feedback Over 300,000 explicit corrections/updates How many bogus updates? (We require a validated email address) Explicit ratings: 0.17% of document accesses

    20. Explicit corrections Over 100 bogus correction attempts

    21. Comparison of feedback types How well do document access, document downloads, and explicit ratings predict high-citation papers? Low citation papers (<= 5 citations) High citation papers (> 5 citations) Ratio of downloads/accesses/ratings for high to low-citation papers Accesses ? Downloads ? Ratings ?

    22. Comparison of feedback types Low citation papers (<= 5 citations) High citation papers (> 5 citations) Ratio of downloads/accesses/ratings for high to low-citation papers Accesses 2.5 Downloads 3.1 Ratings 0.96 (low 2.3 high 2.2)

    23. CiteSeer: user profiling Profiling system not currently active (scale) Profile contains documents, citations, keywords, etc. of interest User notified of new related documents or citations by email or via the web interface Both implicit and explicit feedback Record the actions of a user for recommendations View Download Ignore

    26. CiteSeer: user profiling Implicit feedback should be more successful in CiteSeer due to citation context, query-sensitive summaries, document details pages, and the expense of document downloads Users can better determine the relevance of documents before they request details or download articles Analyze co-viewed/downloaded documents to recommend documents related to a given document Similar to one of Amazons book recommenders

    27. Profile creation (Pseudo)-documents added to users profile whenever a user performs an action in the profile editor or on a real document when browsing Action interestingness a(.) Explicitly added to profile Very high positive Downloaded High positive Details viewed Moderate positive Recommendation ignored Low negative Removed from profile Set to zero

    28. Paper recommendations New papers recommended periodically via email or the web interface New paper d* recommended if it has a sufficiently high interestingness Threshold initially set at a small positive value

    29. Profile adaption Adaption occurs via manual adjustment and machine learning User can explicitly modify a profile by adjusting the weight of pseudo-documents Browsing actions implicitly modify the weight of corresponding pseudo-documents User response to recommendation of a paper d* is used to update weights that contributed to the recommendation where is the learning rate

    30. Weight update rule properties Weights modified according to their contribution to recommendations Overall precision/recall threshold automatically adapted. Ignoring recommendations raises the threshold for recommending a paper. Explicitly adding papers lowers the threshold The influence of different relatedness measures is adapted separately

    31. REFEREE Recommender framework where outside groups can test recommendation systems live on CiteSeer Implemented a version of Pennocks Personality Diagnosis recommender for initial testing

    32. REFEREE Statistics on recommender performance available quickly For evaluation we focus on measuring impact on user behavior Implicit feedback more effective because users see a lot of information about documents before they can download them Which recommenders best? Users who viewed x also viewed? Exact sentence overlap? Papers that cite this paper? Citation similarity?

    33. Recommendations followed

    34. NewsSeer

    35. NewsSeer Primarily a single page with implicit feedback only Also supports explicit feedback but this is optional

    39. NewsSeer statistics About 1 million pageviews About 10,000 users (>= 5 requests) 5,000 users (>= 10 requests) How many users rated an article? What percentage of requests were ratings on the homepage? What percentage of requests were for the source ratings page?

    40. NewsSeer statistics 1,000 users rated an article from the 10,000 with >= 5 requests About 10% About 20% of the top 2,500 users About 30% of the top 1,000 users 20 of 56 users that did >1,000 requests 10 of 21 users that did >2,000 requests Homepage 51% (auto-reloaded) View article 40% Keyword query 4% (was not available initially) Ratings on homepage 5% Source rating page views 0.2%

    41. MusicSeer

    42. Music similarity

    43. Music similarity Music similarity survey Erds game

    44. Music similarity

    46. MusicSeer Survey 713 users, 10,997 judgments Game 680 users, 11,313 judgments

    47. Summary Implicit feedback may be better because there is much lower overhead Much greater participation may more than compensate for the less accurate information received Can structure system to maximize implicit feedback gained Can obtain explicit feedback if enough incentive, or easy enough

More Related