CaRR Workshop Keynote 際際滷s
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David Elsweiler discusses his research on user behavior within search and recommender systems across various domains, particularly leisure and health. The document outlines insights gained from user interaction data on event discovery and planning, highlighting different user preferences and how system features affect their experiences. Findings indicate that user behavior is influenced by various factors, including the usage of specific system features and the context of their goals.
CaRR Workshop Keynote 際際滷s
- 1. David Elsweiler| david.elsweiler@ur.de Lehrstuhl f端r Informationswissenschaft| www.iw.ur.de Behaviour with Search and Recommender Systems: what can it tell us?
- 2. Coming up... Discuss some of the work I have been doing in Rec-Sys and Search Leisure and Food / Health domains Behavioural focus Outline the benefits I believe such a focus has for both the rec-sys and the IR community
- 3. Caveat: Not just my work!
- 6. Photo by Martin LaBar (going on hiatus) - Creative Commons Attribution-NonCommercial License http://www.flickr.com/photos/32454422@N00 Created with Haiku Deck
- 7. Photo by Martin LaBar (going on hiatus) - Creative Commons Attribution-NonCommercial License http://www.flickr.com/photos/32454422@N00 Created with Haiku Deck
- 8. Photo by ell brown - Creative Commons Attribution License http://www.flickr.com/photos/39415781@N06 Created with Haiku Deck
- 9. Photo by will_cyclist - Creative Commons Attribution-NonCommercial License http://www.flickr.com/photos/88379351@N00 Created with Haiku Deck
- 10. Photo by Pete Prodoehl - Creative Commons Attribution-NonCommercial-ShareAlike License http://www.flickr.com/photos/35237092540@N01 Created with Haiku Deck
- 11. Photo by Pete Prodoehl - Creative Commons Attribution-NonCommercial-ShareAlike License http://www.flickr.com/photos/35237092540@N01 Created with Haiku Deck
- 12. Photo by davidjwbailey - Creative Commons Attribution-NonCommercial-ShareAlike License http://www.flickr.com/photos/27711971@N06 Created with Haiku Deck
- 13. Photo by davidjwbailey - Creative Commons Attribution-NonCommercial-ShareAlike License http://www.flickr.com/photos/27711971@N06 Created with Haiku Deck
- 16. Photo by bigwhitehobbit - Creative Commons Attribution-NonCommercial-ShareAlike License http://www.flickr.com/photos/28618339@N03 Created with Haiku Deck
- 17. Photo by bigwhitehobbit - Creative Commons Attribution-NonCommercial-ShareAlike License http://www.flickr.com/photos/28618339@N03 Created with Haiku Deck
- 18. Photo by My name's axel - Creative Commons Attribution-NonCommercial-ShareAlike License http://www.flickr.com/photos/37611179@N00 Created with Haiku Deck
- 21. Photo by John Howard - Getty Royalty-Free License http://www.gettyimages.com/Corporate/LicenseAgreements.aspx Created with Haiku Deck
- 22. Photo by John Howard - Getty Royalty-Free License http://www.gettyimages.com/Corporate/LicenseAgreements.aspx Created with Haiku Deck This is a rec-sys problem. Think about Netflix, Spotify, Amazon etc. but the process of searching can also be part of the fun We have been investigating these questions in different contexts: Wikipedia, social-media, distributed leisure events
- 25. App Helps vistors find events Generates Plans Guides the visitor 1000-2000 users Interaction log-data
- 26. App Helps vistors find events Generates Plans Guides the visitor 1000-2000 users Interaction log-data
- 27. App Helps vistors find events Generates Plans Guides the visitor 1000-2000 users Interaction log-data
- 28. App Helps vistors find events Generates Plans Guides the visitor 1000-2000 users Interaction log-data Every 6 months 1-2,000 users Interaction data logged
- 30. App Helps vistors find events Generates Plans Guides the visitor 1000-2000 users Interaction log-data Combine with other data sources e.g. survey from >50 users Rich understanding of how system features were used How system usage influences experience on evening
- 31. Photo by Kaysse - Creative Commons Attribution License http://www.flickr.com/photos/29862505@N08 Created with Haiku Deck
- 32. Photo by Thomas Rousing - Creative Commons Attribution License http://www.flickr.com/photos/43812360@N05 Created with Haiku Deck Offline evaluation of various Rec-Sys algs LNMusic: 860 users; 4,973 ratings LNMuseums: 1,047 users; 10,992 ratings Of the single recommenders the popularity baseline performs best Combining Content-based and Collaborative Filtering improves performance (dynamic weighting even more) Additionally considering temporal contiguity does not affect the performance
- 33. Photo by Thomas Rousing - Creative Commons Attribution License http://www.flickr.com/photos/43812360@N05 Created with Haiku Deck Online evaluation (live A/B testing) Different weights with our best system and TempCont Slight cost to user acceptance (ERec ESel ) Routes were tighter and more compact, which would allow users to spend less time travelling and more time visiting events First hint that changing the system has an influence on the behaviour (and perhaps on the experience)
- 34. Investigating behavioural patterns Long Night of Music (1159 users, 111 GPS) Dominant tab for users: Most users (81.2%) stick to one or two tabs for selecting events of interest Most events (82.8%) came from dominant tab Rec Sys By Tour Genre Search Map 37.2% 15.6% 17.4% 24.5% 5.3%
- 35. Tab-usage during the night
- 36. Tab-usage during the night Planning phase Event discovery with the aim of planning in mind e.g. Searching, Browsing and in particular RecSys Tab-usage during the night
- 37. Tab-usage during the night After 8pm behaviour changed Less interaction with search, genre & RecSys More geographical, in part. Map tab Tab-usage during the night
- 38. Photo by Leo Reynolds - Creative Commons Attribution-NonCommercial-ShareAlike License http://www.flickr.com/photos/49968232@N00 Created with Haiku Deck Metrics to model user experience on evening # event visits Evening duration Ratio of visiting time Avg. event visiting time Recall and Precision of visited events, Diversity of events Temporal contiguity of events Ratio of top N events
- 39. Visit significantly more events than the others on average nearly 1.5 events more Spent significantly more time visiting events Likely because of the Temporal Contiguity component in the RecSys More efficient use of time on evening Significantly shorter interaction times More popular events
- 40. Also visited more events Spent less time visiting events Longer evenings Tend to only visit events near stops on one or two lines Value for money users
- 41. Visited less diverse and less popular events Favour more esoteric choices that fit more closely with their specific genres of interest. Specificity comes at a cost of a smaller number of visited events and also a lower ratio of visiting time Greater precision, meaning they tend to adhere more rigidly to their original plans during the night.
- 42. Spent less time during the evening overall (~30 mins) and 5 mins less at each event Surprisingly no influence on popularity Seems users cherry pick known about events of interest e.g. recommendations from friends Spend a lot of time planning these events (increased interaction time before event)
- 43. Map Tab Interacted less before the evening (5.6min vs. 15.7min) Temporal contiguity for visited events is lower Visited events less likely to have been previously marked likely explained by such users marking fewer events as interesting (4.71 events vs. 9.79; p=0.01). Visited events were less popular (10.1% vs. 15.7% of visited events were among the top 5)
- 44. Visited events precision over time: Map users stuck with their smaller plans until around 9.30pm Other users until around 12.30 am Both groups were more likely to deviate as time went on
- 45. 55 users provided feedback about the app and their priorities for the evening Rec-sys and Tour tab users appreciate routes with: an efficient use of time, shorter paths, and many events. Tour tab users value interestingness of events less than other users
- 46. Genre tab users: were less interested in using time efficiently, didnt care much about having short travel times not bothered about visiting many events. Instead, they put value on visiting interesting but not diverse events
- 47. Map tab users: 88.9% claimed they used the app as an electronic program guide (vs 62.2%) Reflects map tab users having no ambitions of making plans but instead to spontaneously decide where to go next.
- 48. Search tab users: Outliers dont really state any real prefences with respect to the other groups There was one finding of note that linked to their outcomes: Strong disagreement with the statement that the app helped to reduce travelling time, while other groups strongly agreed Cherry-picking events not a good strategy if you want an efficient route
- 49. Photo by fractalznet - Creative Commons Attribution-NonCommercial License http://www.flickr.com/photos/95575701@N00 Created with Haiku Deck What users want differs and changes over time Distinct patterns of usage: Correlation between using specific features and outcomes of the evening Correlation between reported user priorities and usage of specific features Different support best in different situations Users adapt their behaviour
- 51. Photo by marco bono - Creative Commons Attribution-NonCommercial-ShareAlike License http://www.flickr.com/photos/47001509@N00 Created with Haiku Deck
- 53. M端ller, M.; Harvey, M.; Elsweiler, D. & Mika, S. (2012), Ingredient Matching to Determine the Nutritional Properties of Internet-Sourced Recipes, in 'Proc. 6th International Conference on Pervasive Computing Technologies for Healthcare'
- 54. Harvey, M., Elsweiler, D., Ludwig, B. (2013)You are what you eat: learning user tastes for rating prediction20th String Processing and Information Retrieval Symposium (SPIRE). Jerusalem, Israel.
- 55. Plans User created Automatically based on user tastes and WHO guidelines
- 56. Plans User created Automatically based on user tastes and WHO guidelines
- 57. Plans User created Automatically based on user tastes and WHO guidelines
- 58. Behaviour with the system How is this system used? What factors affect this? Behavioural Change User has a goal (e.g. eat less fatty foods, lose weight, eat more protein) Can the system help change behaviour to move the user towards his or her goal? Does system usage influence behavioural change?
- 59. Photo by Fake Plastic Alice - Creative Commons Attribution License http://www.flickr.com/photos/57764541@N00 Created with Haiku Deck A behavioural approach is system agnostic Behaviour is highly context-dependent As are user goals Behaviour > interaction: non-system behaviours e.g. LN outcomes Complementary evaluation approach
- 60. Photo by Derek Bridges - Creative Commons Attribution License http://www.flickr.com/photos/84949728@N00 Created with Haiku Deck