1. Mobile Query Reformulations Author:Milad Shokouhi, Rosie Jones, Umut Ozertem, Karthik Raghunathan, Fernando Diaz Source:SIGIR '14 Spearker:LIN,CI-JIE 1 2. Outline Introduction Related Work Data analysis Conclusion 2 3. Outline Introduction Related Work Data analysis Conclusion 3 4. Introduction Users frequently interact with web search systems on their mobile devices via multiple modalities, including touch and speech These interaction modes are substantially different from the user experience on desktop System designers have new challenges and questions around understanding the intent on these platforms Study the query reformulation patterns based on their input method 4 5. Outline Introduction Related Work Data analysis Conclusion 5 6. Related Work They found that in more recent mobile searches, users were typing faster and clicking on more results, and sessions were longer and less homogeneous They also found mobile queries to be on average shorter and more repetitive compared to desktop. They reported that speech recognition errors happen in about half of voice queries 6 7. Outline Introduction Related Work Data analysis Conclusion 7 8. Data analysis Sample the mobile search query logs of the Bing search engine between 1 October 2013 and 31 October 2013 (1 month) In total there are 2,643,283 multi-query sessions containing 7,668,809 distinct queries from 706,763 unique users in our sample dataset Define each session as a sequence of search actions (e.g. queries,clicks) with no periods of inactivity longer than 30 minutes 8 9. Distribution of reformulation modalities in the mobile search logs 9 users do not tend to switch between different input methods 10. Query reformulation types 10 11. Query reformulation types 11 12. Query reformulation 12 When a query reformulation cannot be assigned to any of these groups it is called new, and it is assumed that it does not share the same intent as last query This happened respectively for 64%, 72%, 74% and 91% of T2T, V2T, V2V and T2V mobile reformulations in our dataset In total, 94% of reformulations in our desktop logs are categorized as new 13. Data analysis 13 1. the typing is generally easier on desktop devices 2. The substituted words in many cases are the incorrect predictions from the speech recognizer that had to be fixed by typing 3. People are unlikely to use abbreviations unless in their T2T reformulations. 4. People are more likely remove words in their mobile reformulations 14. Data analysis 14 1. The SkipClick ratio is highest among voice to text (V2T) searches. This can be explained by speech correction errors that needed to be fixed by typing 2. Text to voice (T2V) reformulations can be explained by the fact that most of these reformulations are about new intents 15. Outline Introduction Related Work Data analysis Conclusion 15 16. Conclusion Users rarely switch between different input types unless they are searching for a new intent or correcting speech recognizers errors Users are more likely to drop words and spell-correct their mobile queries and less likely to use abbreviations 16 17. Thanks for listening. 17