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improving explicit preference entry byvisualising data similaritieskris jack and florence duclaye13 january 2008
summary1 the problem in context2 background3 proposed solution4 user evaluation5 results6 discussion7 conclusionkris jack ...
1the problem in contextkris jack – p3
context   general context        recommender systems attempt to offer items to users that will be appreciated        th...
the problem entering         your preferences explicitly        can be boring        can be difficult ifa system is to...
2backgroundkris jack – p6
definitions   preferences, many types and definitions exist        concentrating on the elicitation of monadic preferenc...
some existing epe interfaces   recommenders with epe interfaces        minekey (www.minekey.com)        stumbleupon (ww...
data visualisation  perhaps we can improve epe interfaces by visualising data  data visualisation techniques have had so...
3proposed solutionkris jack – p10
visualising data similarity in an epeinterface   the epe interface        should encourage users to enter their preferen...
instantiating the systems semanticknowledge the systems semantic knowledge describes the  similarity between descriptors ...
strategies for defining semanticsimilarities   considered        instantiation by hand        differencing mechanism   ...
normalised google distance   actors         jackie chan bruce lee          jane fonda   jackie         2,420,000      965,...
data visualisation   use of the radial tree layout to visualise data similarities        manageable linear complexity in...
radialtree kris jack – p16
radial tree (partial)kris jack – p17
epe interface   the visualisation is mounted in an epe interface that allows    users to       zoom in by right clicking...
epe interfacekris jack – p19
kris jack – p20
4user evaluationkris jack – p21
evaluating the epe interface   evaulated:        choice of similarity metric in the context of actors        type of pr...
participants and procedure 28 participants (14 male, 14 female) procedure       practice the functions of the epe inter...
hypothesis and measurements   hypothesis        participants will find it easier to declare their preferences for actors...
5resultskris jack – p25
preference elicitation   more preferences were entered using the organised graph:        significant increase in like pr...
perceived ease of enteringpreferences   participants reported that it was:        easy to enter preferences using the or...
perceived differences between graphs 22 participants (79%) reported differences between the two  graphs commented that t...
appreciation of the epe interface participants enjoyed using the interface and would be happy  using it again (mean = 4.1...
6discussionkris jack – p30
discussion   the participants mark more like preferences with the    organised graph (34%)        find more actors who t...
discussion   how well did the notion of similarity come across?        with only 5 minutes of exposure to each graph, th...
discussion   participants report that the organised graph is easier to use        an organised graph is easier to naviga...
discussion   when should the epe interface appear in the recommendation    process?        from the start, users should ...
7conclusionkris jack – p35
conclusion a new epe interface is introduced that can takes data and  organises it based on a robust similarity metric d...
the end   many thanks for your attentionkris jack – p37
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improving explicit preference entry by visualising data similarities

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I gave this presentation in the Recommendation and Collaboration Intelligence User Interfaces workshop at the 2008 International Conference on Intelligent User Interfaces.

Presents a data visualisation technique that is used to help users to enter their explicit preferences for their profile. In this case, users were able to enter more preferences for actors when the actors were visualised according to a similarity metric rather than randomly organised, in the same time period.

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Transcript of "improving explicit preference entry by visualising data similarities"

  1. 1. improving explicit preference entry byvisualising data similaritieskris jack and florence duclaye13 january 2008
  2. 2. summary1 the problem in context2 background3 proposed solution4 user evaluation5 results6 discussion7 conclusionkris jack – p2
  3. 3. 1the problem in contextkris jack – p3
  4. 4. context general context  recommender systems attempt to offer items to users that will be appreciated  the quality of these recommendations is largely constrained by the data • a user’s preferences • domain-specific ‘general knowledge • the items that can be recommended • previous users’ opinions of items  the acquisition of such data is of central importance specific context  a hybrid content-based and collaborative filtering recommendation system  the system will be implemented in the domain of cinema  the user can explicit enter their preferenceskris jack – p4
  5. 5. the problem entering your preferences explicitly  can be boring  can be difficult ifa system is too much trouble to use, then it simply wont be used how can we improve the explicit preference entry process?  maximise the number of explicit preferences that could be elicited within a given period of timekris jack – p5
  6. 6. 2backgroundkris jack – p6
  7. 7. definitions preferences, many types and definitions exist  concentrating on the elicitation of monadic preferences  represent a user’s like, dislike, or indifference towards an item or item attribute (e.g. “i like tim burton”, “i dislike horror movies” and “i love kill bill”) preference acquisition strategies  explicit – user explicitly enters their preferences  implicit – learning strategies that are non-invasive (e.g. user-profiling and collaborative filtering) explicit preference entry (epe) interface  an interface that asks users to explicitly give their preferences towards items and item attributes (e.g. “i love comedies”)kris jack – p7
  8. 8. some existing epe interfaces recommenders with epe interfaces  minekey (www.minekey.com)  stumbleupon (www.stumbleupon.com)  movielens drawbacks  boring to use minekey  difficult to be inspired (when guidance is lacking)  difficult to describe yourself in their terms at timeskris jack – p8 stumbleupon
  9. 9. data visualisation  perhaps we can improve epe interfaces by visualising data  data visualisation techniques have had some success in recommenders  music plasma (www.musicplasma.com)  amaznode (amaznode.fladdict.net/)music plasma amaznode kris jack – p9
  10. 10. 3proposed solutionkris jack – p10
  11. 11. visualising data similarity in an epeinterface the epe interface  should encourage users to enter their preferences  may guide users based upon the preferences required  should be enjoyable to use and not boring the epe interface creation process  must be robust and reliable  desirable if it is automated from start to finish creation process summary  input a list of items or item descriptors (e.g. actors)  using a similarity metric, find the similarities between data elements  visualise the similarities between the data elementskris jack – p11
  12. 12. instantiating the systems semanticknowledge the systems semantic knowledge describes the similarity between descriptors in the database the notion of similarity is necessarily subjective  how similar do you find these two actors? (robert de niro and al pacino)kris jack – p12
  13. 13. strategies for defining semanticsimilarities considered  instantiation by hand  differencing mechanism  co-occurrence measures  clustering algorithms  collaborative filtering techniques opted for one based on co-occurrences of actor names found using the google search estimates max{log f (i1),log f (i 2)} − log f ( i1, i 2) d (i1, i 2 ) = log M − min{log f (i1),log f (i 2)} where m is the total number of pages considered, f(i1) and f(i2) are the number of hits for i1 and i2 respectively, and f(i1,i2) is the number of hits for the co-occurrence of i1 and i2.  in essence, the more often two items appear together on the same web pages, the more similar they are  the measure of semantic relatedness server provides free accesskris jack – p13
  14. 14. normalised google distance actors jackie chan bruce lee jane fonda jackie 2,420,000 965,000 145,000 chan (0.0) (0.09) (0.26) bruce 965,000 2,630,000 46,700 lee (0.09) (0.0) (0.37) jane 145,000 46,700 1,930,000 fonda (0.26) (0.37) (0.0) the number of google hits for actor pairs and their normalized google distances given in brackets (the smaller the distance, the more similar the actors)kris jack – p14
  15. 15. data visualisation use of the radial tree layout to visualise data similarities  manageable linear complexity in laying out the tree • efficient even with several hundreds of nodes  the more similar two items are, the closer their proximity in the graph  a radial layout encourages users to explore the tree in a less hierarchical fashion as it is unclear where the root node is  tree can be focussed upon any node in the tree (implementing a smooth transition animation) • users have previously found this form of visualisation attractive  implementation available in prefuse (www.prefuse.com) library strategy  each item (actor) is represented by a node in the tree  connect every node with their two closest nodes (using item similarity)kris jack – p15
  16. 16. radialtree kris jack – p16
  17. 17. radial tree (partial)kris jack – p17
  18. 18. epe interface the visualisation is mounted in an epe interface that allows users to  zoom in by right clicking on a graph area (in zoomed out mode) and zoom out by right clicking on a graph area that does not contain an actor (in zoomed in mode);  pan within the graph by left clicking on a graph area and dragging in the direction to pan;  search for an actor by typing the actor’s name in the search box. when the user starts to type a name, the mode changes to zoomed out mode and all actor nodes who’s names match the string are enlarged;  change the preference towards an actor (like, dislike, neutral, no preference) by righting clicking on the actor’s node (in zoomed in mode);  re-organise the graph to centre upon one actor by double left clicking on another actor node.kris jack – p18
  19. 19. epe interfacekris jack – p19
  20. 20. kris jack – p20
  21. 21. 4user evaluationkris jack – p21
  22. 22. evaluating the epe interface evaulated:  choice of similarity metric in the context of actors  type of preferences elicited  epe interfaces ease of use  appreciation of the epe interface materials  epe interface mounted with 3 different graphs: • organised graph (nodes positions according to actor similarity) • unorganised graph (organised graph with nodes randomised) • demonstration graph (organised graph with nodes randomised)  each graph contained the same 500 nodes (most frequent actors from an in-house french database)  instruction sheetkris jack – p22
  23. 23. participants and procedure 28 participants (14 male, 14 female) procedure  practice the functions of the epe interface using the demonstration graph (took 10 minutes on average)  task • enter as many actor-based preferences as possible in 5 minutes • once with the organised graph and once with the unorganised graph (following a within-subjects design with 2 groups of 14 participants) • note that the graphs were not named here, the tasks were referred to as task 1 and task 2  participants completed a questionnaire on terminating the taskskris jack – p23
  24. 24. hypothesis and measurements hypothesis  participants will find it easier to declare their preferences for actors in the organised graph task than in the unorganised graph task, within the same time period measuring the ease of declaring preferences  quantity of preferences entered  subjective questioning in the questionnairekris jack – p24
  25. 25. 5resultskris jack – p25
  26. 26. preference elicitation more preferences were entered using the organised graph:  significant increase in like preferences (34%)  decrease in dislike and neutral preferences P ref erenc e Elic itation 60 50 40 Organised 30 Unorganised 20 10 0 All Like Dislike Neutralkris jack – p26
  27. 27. perceived ease of enteringpreferences participants reported that it was:  easy to enter preferences using the organised graph  neither easy nor difficult to enter preferences using the unorganised graph ease of preference entry mean statements organised unorganised "i found it easy to enter my preferences." 3.96 3.36 "entering my preferences demanded too much effort" (mean reversed) 3.64 3.11 cronbach’s alpha = 0.80kris jack – p27
  28. 28. perceived differences between graphs 22 participants (79%) reported differences between the two graphs commented that the organised graph  had been hand designed so that it was easier to navigate;  rearranged itself based on the actors that the participant said that they liked;  arranged the participant’s favourite actors together;  had more connections between nodes;  arranged actors together who: • co-starred in the same films; • shared the same nationality; • shared the same degree of celebrity; • were similar to one another. commented that the unorganised graph  was less organisedkris jack – p28
  29. 29. appreciation of the epe interface participants enjoyed using the interface and would be happy using it again (mean = 4.11/5.00, sd = 0.99) suggested improvements  preference changing. some participants did not like having to click twice to register a dislike and three times to enter a neutral preference.  zooming in. some participants would have preferred a precise indication of the region into which they could zoom into before zooming.  zooming out. some participants felt lost when zoomed in on the actors as they were not sure of where they were with respect to the entire mapkris jack – p29
  30. 30. 6discussionkris jack – p30
  31. 31. discussion the participants mark more like preferences with the organised graph (34%)  find more actors who they like and less who they dislike or are neutral towards  why? • in searching, participants tended to begin by using the search feature, then zoom in on their desired actor • when at the zoomed in level, they would pan around to find other actors. • actors who were in close proximity tended to be similar • similar actors tend to be liked too  applications that can exploit likes better than dislikes may want to introduce semantic similarity in an epe interfacekris jack – p31
  32. 32. discussion how well did the notion of similarity come across?  with only 5 minutes of exposure to each graph, the majority of participants found that one was organised and that the other was lesser so or not at all  the word similar was repeatedly used by participants  results serve to validate the use of the google distance metric in this area  the similarity metric thus goes some way to replacing what is traditionally in the domain of human-design decisions the epe interface was very much appreciated  participants liked it and wanted to use it again  they commented that it was more like playing a game and not like entering their preferenceskris jack – p32
  33. 33. discussion participants report that the organised graph is easier to use  an organised graph is easier to navigate than an unorganised graph  they find more actors who they like addressing interface issues  replace the right click to change a preference with three icons next to the node. a single click on the item will designate the corresponding preference  offer a mini-map, with the absolute position of the main map indicated, that is always zoomed outkris jack – p33
  34. 34. discussion when should the epe interface appear in the recommendation process?  from the start, users should be able to use it • initial entry of preferences  all throughout the recommendation process also • could be used to visualise learned or predicted preferences too, allowing the user to correct any mistakes at a visual level what other benefits does a similarity-based epe interface bring?  users become aware of the notion of similarity as used by the system  the logic of the system, in this case the positioning of actor nodes, becomes learned  imagine a system that uses this form of similarity to produce non-exact results for searches (e.g. cannot fine any jackie chan films, would you like bruce lee films instead?)  understanding the logic of the system is very important in developing trust in the systemkris jack – p34
  35. 35. 7conclusionkris jack – p35
  36. 36. conclusion a new epe interface is introduced that can takes data and organises it based on a robust similarity metric data similarities are visualised into a pleasing tree-based graph users can navigate through the graph and explicitly enter their preferences for different items interface favours elicitation of like preferences  users enter 34% more like preferences when the graph is organised with the similarity metric compared to when it is left unorganised users report a reduction in cognitive effort when using the organised graph the epe creation process is a robust and flexible solution to eliciting explicit user preferences in a recommendation systemkris jack – p36
  37. 37. the end many thanks for your attentionkris jack – p37
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