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# An Adaptive Speed-Call List Algorithm and Its Evaluation with ESM

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• Nice to meet you. I’m Seunghwan Lee from KAIST and I’m honored to present our work “ ” in CHI Let’s start with very short demo. ###OK. That’s a speed-call list, a kind of call recommendation.
• We were curious about this given question.To answer this question, We …Let me show you a very very short demo again, If a user press the send key, #####then the adaptive speed-call list is given with adequate recommendation reasons.
• To find out which calling method is used to call and whether there is any regular call pattern or not,We conducted online survey with 75 users.As you see in the table, Searching and selecting from recent call comprised more than sixty percent of responses. Therefore we adopted pressing the send key for the start event to see our speed call list.
• To analyze call pattern, We collected … visualized With a vertical time-of-day axis (0 to 24 hour ) and a horizontaldate axis. (1 to 30 or 31)Red circles and labels indicate call destinationYes. These call maps are from different users. Left from a graduate student.he loves to eat a late-night snack and sleep late. Right shows calls from an married man. He has regular life pattern and call pattern. Majority of calls are concentrated on his wife.
• To give a speed-call list, we selected .. The possible values for each variables are shown in the right column. For example, the phone can recommend Mom “Because it is Sunday” by the day of week,Or recommend a colleague Because it is afternoon by daypart of day .
• Let me explain the algorithm with this example call map. A user press the send key to make a call ## at this red point, one thirty AM (1:30) on Tuesday.Consider the probability for chicken delivery ### for weekend or weekday.The phone counts number of weekdays for the last 3 months. And also count number of weekdays with a phone call to chicken delivery.In the same manner, the phone calculates probabilities for different variables, too.Subtract std from these values for pessimistic estimation. Normalize these values by dividing by the individual time span. Then, take the highest P for chicken delivery, Sort all the Ps for each person. Finally select 5 of them. /// Q: because of different nc, all the Ps have Different confidence level. So And we Normalized them into probability in unit time because of Different time scales of 5 independent variablesWe can show you an example with this call map. If a user press the send button on the mobile phone at this red point, AM 1:30, the late night in TuesdayThe phone know that it’s late night of Tuesday, one of the weekdays then count number of Tuesdayduring last 3 months. And also count number of Tuesday with a phone call to each person. In the same manner, the phone calculate probabilities for each variablesBecause of Different confidence level from different 𝑛_𝑐, we adjusted p with pessimistic inference
• The result will be like this. Delivery for chicken is on the top because of time /and Call for family is recommended based on the call frequency. For the same user, The same algorithm can give a different result like this ### for different time, Monday afternoon.
• Then We compared the performance of the algorithm with other methods. …It would be the best if the number is on the top of the list. Its probability Is highest in our case. Otherwise it would be still good if it appears on the first page. Its probability Is highest in our case, too. The number of button presses for calling was smallest.The average result showed our adaptive-speed call list is best among these methods.
• Thegraphs shows individual differences and We could classify 20 users into 3 groups based on recommendation suitability. The red line group are most suitable for call prediction. We can classify the user group with some features of call logs like call concentration rate. ###Therefore if we determine the user group first, then selecting more effective variables would be possible and it is expected to bring better recommendation result.
• We implemented our method on samsung blackjack and conducted a ESM study to … ###The ESM system we used support questionnaire, self report, application logging###We replaced usualcall log page into our speed call list like this. If the recommendation is failed, then user can change the list to usual recent call log page or can access phone book easily.
• 19 % of all outgoing calls were made using … seventy percent of cases were success and others failed. In this case, the mean and median rank was about 2. The failed cases include recommendation impossible cases like callee without recent call, and the newly dialed numbers. /// Q: we used blackjack, a smartphone, so some people could use photo shortcut or other plugins installed by themselves, And another reason for low rate is that after they press the send key, they had to click ‘call log’ soft button on the screen again to see the list So some of them didn’t use the list in some cases. The page replacement was not perfect. I feel sad about it too. Actually, we could calculate all the callees with call log again, and we got 60% of success for those missed cases.
• The ESM system presented a window right after call to ask user feedback. About sixty percent answered it was helpful at that moment. And some positive or negative descriptions were also gathered.
• On the questionnaire after field study,### ###they had to press the send key and click ‘call log’ soft button on the screen again to see the list ###We requested themto give us some requirement for speed-call list, and fast switching to existing method was elected as the most important requirement, than other 2 items.Contrary to our expectation, accuracy was ranked at 3rdWe thought that people considered it as a supplemental tool, and an occasional failure did not seem to be very critical.
• I skipped some details due to short time,If you are curious, please contact me. /// Q: on average, 25 users were called by participants for 3 months
• ### An Adaptive Speed-Call List Algorithm and Its Evaluation with ESM

1. 1. An Adaptive Speed-Call List Algorithm and Its Evaluation with ESM<br />Seunghwan Lee, JungsukSeo, Geehyuk Lee<br />HCI Lab @ <br />Darling<br />Mother<br />Professor<br />Kim<br />This time<br />Frequent<br />Thursday<br />Evening<br />
2. 2. Motivation<br />Can a mobile phonepredictand recommenda number to call in a certain context?<br />
3. 3. Overview<br />We …<br />Designed a method to generate an adaptive speed-call list<br />Online survey with 75 participants<br />Using 3 month call log of 20 users<br />Verified the effectiveness <br />Using 3 month call log of 20 users<br />Implemented our method and conducted ESM study<br />With 10 users for 3 months<br />Darling<br />Mother<br />Professor<br />Kim<br />This time<br />Frequent<br />Thursday<br />Evening<br />
4. 4. How do you make a phone call?<br />Online survey with 75 participants (16-63 years old, 5+ years experience) <br />USER SURVEY<br />
5. 5. Any regular call pattern?<br />67% have a number to call regularly<br />Parents (51%) <br />Lover / spouse (44%)<br />Friends (21%), <br />Brothers & sisters (15%)<br />USER SURVEY<br />my husbandto get home every night. <br />my husbandto get home every night. <br />my colleagues to remind meeting.<br />my lazy boyfriend for morning call.<br />my lazy boyfriend for morning call.<br />my friendsfor having lunch together.<br />my parentat weekend.<br />my parentat weekend.<br />my friends after my work hours<br />Chinese restaurant for food delivery every weekend<br />my wife every lunch time.<br />my wife every lunch time.<br />my girlfriend while leaving my office.<br />my girlfriend while leaving my office.<br />
6. 6. Call pattern analysis<br />Collected 3 month call log of 20 users<br />Call map: Visualize outgoing calls on a graph <br />X-axis: Date in a month(1~30/31)<br />Y-axis: Time-of-day (0~24)<br />Red circles: outgoing calls / Label: call destination<br />CALL ANALYSIS<br />USER SURVEY<br />Can you see the difference in calling pattern? <br />
7. 7. Reasons for recommendation<br />ALGORITHM<br />CALL ANALYSIS<br />USER SURVEY<br />Phone calculates “the probabilities of Bernoulli trials” for each reasonfor each person in the current context<br />5 reasons for recommendation<br />
8. 8. Algorithm designAn example<br />ALGORITHM<br />CALL ANALYSIS<br />USER SURVEY<br />2008/3/25<br />1:30 AM(Late night)<br />Tuesday (weekday)<br />pChicken,D= 015<br /> <br />pChicken,D′= 015−stdDay<br /> <br />pChicken,D′= 015− stdDay24hour=0<br /> <br />pChicken,W= 315<br /> <br />pChicken,W′= 315−stdWeekday<br /> <br />pChicken,W′= 315 − stdWeekday5×24hour=0.002<br /> <br />pChicken,T= 590<br /> <br />pChicken,T′= 590−stdTime<br /> <br />pChicken,T′=590 − stdTime3hour=0.017<br /> <br />pChicken,DP= 690<br /> <br />pChicken,DP′= 690−stdDaypart<br /> <br />pChicken,DP′=690 − stdDaypart6hour=0.01<br /> <br />pChicken,1H= 690×24<br /> <br />pChicken,1H′= 690×24−std1−h slot<br /> <br />pChicken,1H′= 690×24 − std1−h slot1hour=0.003<br /> <br />590<br /> <br />015<br /> <br />Day of week<br />Time of day <br />1-hour slots of a day<br />690×24<br /> <br />Weekend / Weekday<br />spans<br />Dayparts of a day<br />nc=15<br /> <br />kic3<br /> <br />690<br /> <br />
9. 9. Speed-call lists for the same user / at different time<br />ALGORITHM<br />CALL ANALYSIS<br />USER SURVEY<br />
10. 10. Algorithm verification using 3 month call logs of 20 users<br />ALGORITHM<br />CALL ANALYSIS<br />USER SURVEY<br /><<br /><<br />><br />
11. 11. Callers were different<br />Our speed call list was effective for some while not for others. <br />Different recommendation method for different calling type.<br />ALGORITHM<br />CALL ANALYSIS<br />USER SURVEY<br />
12. 12. Our ESM study<br />To collect in-situ user feedback and experience in the real situations<br />With 10 users for 3 months<br />No more comparison with existing methods<br />ESM FIELD STUDY<br />CALL ANALYSIS<br />USER SURVEY<br />ALGORITHM<br />ESM system<br />Real-time addition/modification of questions<br />Easy self report with a screenshot <br />Application logging for evaluating recommendation performance<br />
13. 13. Recommendation performance<br />Using adaptive speed-call list (19%)<br />Mean Rank: 2.3 / Median rank: 2<br />ESM FIELD STUDY<br />CALL ANALYSIS<br />USER SURVEY<br />ALGORITHM<br />The rank lower than 5th<br />Include calleeswithout recent call<br />& Newly dialed numbers <br />
14. 14. In-situ feedback right after call<br />Was it helpful? <br />Can you tell me more?<br />I tried to call my boyfriend, and he was on the top of the list.<br />The person I wanted to call was the 1st.<br />It always shows a similar list, but is quite helpful.<br />I could see "home" when I was about to call home.<br />I called someone many times recently, and the list helped me. <br />The time to call was reduced due to the list.<br />I called him after a long pause, and the list was not useful. ( - )<br />I looked up the number to call from phonebook. ( - )<br />…<br />ESM FIELD STUDY<br />CALL ANALYSIS<br />USER SURVEY<br />ALGORITHM<br />
15. 15. Questionnaire summary<br />ESM FIELD STUDY<br />CALL ANALYSIS<br />USER SURVEY<br />ALGORITHM<br />80% reported the speed-call list was helpful<br />Because of adequate candidates on the list<br />No need to type for search<br />Complained about …<br />Extra button clicks to invoke a speed-call list<br />Time delay due to calculating a speed-call list<br />New requirements<br />Fast switching to a search page or recent call list<br />when recommendation fails <br />Fast calculation <br />Accuracy<br />
16. 16. Conclusion<br />We …<br />Studied mobile phone users’ calling patterns<br />Designed a call recommendation algorithm for an adaptive speed-call list<br />Evaluated it with an ESM system, as well as call logs <br /> Adaptive speed call list <br />Effective in terms of <br />Hit ratio on Top-rank <br />Hit ratio on first page<br />The number of button presses <br />Helpful in the real situation<br />Positive in-situ responses<br />Thank you <br />for your attention.<br />sh.lee@kaist.ac.kr<br />Because I’m curious<br />