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Our experimental results show that (1) users\u2019 mobile-to-desktop search behaviors do significantly differ from desktop-to-desktop search behaviors in terms of information exploration, sense-making and repeated behaviors. (2) MTIs can be employed to predict the relevance of click-through documents, but applying document-level relevant content based on the predicted relevance does not improve search performance. (3) MTIs can also be used to identify the relevant text chunks at a fine-grained subdocument level. Such relevant information can achieve better search performance than the document-level relevant content. In addition, such subdocument relevant information can be combined with document-level relevance to further improve the search performance. However, the effectiveness of these methods relies on the sufficiency of click-through documents. (4) MTIs can also be obtained from the Search Engine Results Pages (SERPs). The subdocument feedbacks inferred from this set of MTIs even outperform the MTI-based subdocument feedback from the click-through documents.<\/jats:p>","DOI":"10.1145\/2738036","type":"journal-article","created":{"date-parts":[[2015,4,28]],"date-time":"2015-04-28T12:43:57Z","timestamp":1430225037000},"page":"1-34","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":30,"title":["Understanding and Supporting Cross-Device Web Search for Exploratory Tasks with Mobile Touch Interactions"],"prefix":"10.1145","volume":"33","author":[{"given":"Shuguang","family":"Han","sequence":"first","affiliation":[{"name":"University of Pittsburgh, Pittsburgh, PA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Yue","sequence":"additional","affiliation":[{"name":"Yahoo! 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