Shuguang Han
Xing Yi
ABSTRACT
When using search engines, people often issue multiple related queries to accomplish a complex search task. A simple query-task structure may not fully capture the complexity of query relations since people may divide a task into multiple subtasks. As a result, this paper applies a three-level hierarchical structure with query, goal and mission - a mission includes several goals, and a goal consists of multiple queries. Particularly, we focus on analyzing query-goal-mission structure for mobile web search because of its increasing popularity and lack of investigation in the literature. This study has three main contributions: (1) we study the query-goal-mission structure for mobile web search, which was not studied before. (2) We identify several differences between mobile and desktop search patterns in terms of goal/mission length, duration and interleaving. (3) We demonstrate that the query-goal-mission structure can be applied to design better user satisfaction metrics. Specifically, goal-based search success rate and mission-based abandonment rate are better aligned with users' long-term engagement than query and session based metrics.
- A. Drutsa, G. Gusev, and P. Serdyukov. Future user engagement prediction and its application to improve the sensitivity of online experiments. In WWW, pages 256--266, 2015. Google Scholar
Digital Library
- H. Feild and J. Allan. Task-aware query recommendation. In SIGIR, pages 83--92, 2013. Google ScholarDigital Library
- D. Guan, S. Zhang, and H. Yang. Utilizing query change for session search. In SIGIR, pages 453--462, 2013. Google ScholarDigital Library
- S. Han, D. He, Z. Yue, and P. Brusilovsky. Supporting cross-device web search with social navigation-based mobile touch interactions. In UMAP, pages 143--155, 2015.Google ScholarCross Ref
- S. Han, Z. Yue, and D. He. Understanding and supporting cross-device web search for exploratory tasks with mobile touch interactions. TOIS, 33(4):16, 2015. Google ScholarDigital Library
- A. Hassan, X. Shi, N. Craswell, and B. Ramsey. Beyond clicks: Query reformulation as a predictor of search satisfaction. In CIKM, pages 2019--2028, 2013. Google ScholarDigital Library
- D. He, A. Göker, and D. J. Harper. Combining evidence for automatic web session identi cation. IP&M, 38(5):727--742, 2002. Google ScholarDigital Library
- J. Jiang, S. Han, J. Wu, and D. He. Pitt at trec 2011 sessio track. In TREC, 2011.Google Scholar
- J. Jiang, A. Hassan Awadallah, X. Shi, and R. W. White. Understanding and predicting graded search satisfaction. In WSDM, pages 57--66, 2015. Google ScholarDigital Library
- R. Jones and K. L. Klinkner. Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs. In CIKM, pages 699--708, 2008. Google ScholarDigital Library
- E. Kanoulas, B. Carterette, P. D. Clough, and M. Sanderson. Evaluating multi-query sessions. In SIGIR, pages 1053--1062, 2011. Google ScholarDigital Library
- D. Lagun, C.-H. Hsieh, D. Webster, and V. Navalpakkam. Towards better measurement of attention and satisfaction in mobile search. In SIGIR, pages 113--122. ACM, 2014. Google ScholarDigital Library
- J. Li, S. Hu man, and A. Tokuda. Good abandonment in mobile and pc internet search. In SIGIR, pages 43--50, 2009. Google ScholarDigital Library
- L. Li, H. Deng, Y. He, A. Dong, Y. Chang, and H. Zha. Behavior driven topic transition for search task identification. In WWW, pages 555--565, 2016. Google ScholarDigital Library
- Z. Liao, Y. Song, L.-w. He, and Y. Huang. Evaluating the effectiveness of search task trails. In WWW, pages 489--498, 2012. Google ScholarDigital Library
- C. Lucchese, S. Orlando, R. Perego, F. Silvestri, and G. Tolomei. Discovering tasks from search engine query logs. TOIS, 31(3):14, 2013. Google ScholarDigital Library
- C. D. Manning and H. Schütze. Foundations of statistical natural language processing. MIT press, 1999. Google ScholarDigital Library
- M. Shokouhi and Q. Guo. From queries to cards: Re-ranking proactive card recommendations based on reactive search history. In SIGIR, pages 695--704. ACM, 2015. Google ScholarDigital Library
- Y. Song, H. Ma, H. Wang, and K. Wang. Exploring and exploiting user search behavior on mobile and tablet devices to improve search relevance. In WWW, pages 1201--1212, 2013. Google ScholarDigital Library
- Y. Wang, X. Huang, and R. W. White. Characterizing and supporting cross-device search tasks. In WSDM, pages 707--716. ACM, 2013. Google ScholarDigital Library
- R. W. White, W. Chu, A. Hassan, X. He, Y. Song, and H. Wang. Enhancing personalized search by mining and modeling task behavior. In WWW, pages 1411--1420, 2013. Google ScholarDigital Library
Index Terms
- Framing Mobile Information Needs: An Investigation of Hierarchical Query Sequence Structure
Recommendations
Mobile Voice Query Reformulation by Visually Impaired People
CHIIR '20: Proceedings of the 2020 Conference on Human Information Interaction and RetrievalVoice search has the potential to assist people who cannot look at their device, whether because of a particular situation like driving, a device that does not have a visual interface, or for people who are visually impaired. This study will compare the ...
Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs
CIKM '08: Proceedings of the 17th ACM conference on Information and knowledge managementMost analysis of web search relevance and performance takes a single query as the unit of search engine interaction. When studies attempt to group queries together by task or session, a timeout is typically used to identify the boundary. However, users ...
What Snippet Size is Needed in Mobile Web Search?
CHIIR '17: Proceedings of the 2017 Conference on Conference Human Information Interaction and RetrievalA snippet (content summary for a web page) is one of the main elements in a search result page. Search engines have been improved to reduce users' effort in web search, e.g., providing flexible snippet sizes by considering the purpose of the search and ...
Comments