Demo Data from VLDB 2026 / reviewers in the wild / expert
Akihito Tokuda
dblp:61/7184
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2009
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
1 paper |
Information retrieval · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information retrieval › user behavior
search abandonment |
0.1 | 1 | 2009 | Good abandonment in mobile and PC internet search · SIGIR 2009 |
Information retrieval › contextual search
mobile versus desktop search |
0.0 | 1 | 2009 | Good abandonment in mobile and PC internet search · SIGIR 2009 |
Information retrieval › user behavior
search behavior analysis |
0.0 | 1 | 2009 | Good abandonment in mobile and PC internet search · SIGIR 2009 |
Methods — techniques the papers use, named apart from their topics
query log analysis · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2009 | Good abandonment in mobile and PC internet searchabstractQuery abandonment by search engine users is generally considered to be a negative signal. In this paper, we explore the concept of good abandonment. We define a good abandonment as an abandoned query for which the user's information need was successfully addressed by the search results page, with no need to click on a result or refine the query. We present an analysis of abandoned internet search queries across two modalities (PC and mobile) in three locales. The goal is to approximate the prevalence of good abandonment, and to identify types of information needs that may lead to good abandonment, across different locales and modalities. Our study has three key findings: First, queries potentially indicating good abandonment make up a significant portion of all abandoned queries. Second, the good abandonment rate from mobile search is significantly higher than that from PC search, across all locales tested. Third, classified by type of information need, the major classes of good abandonment vary dramatically by both locale and modality. Our findings imply that it is a mistake to uniformly consider query abandonment as a negative signal. Further, there is a potential opportunity for search engines to drive additional good abandonment, especially for mobile search users, by improving search features and result snippets. Jane Li, Scott B. Huffman, Akihito Tokuda |
SIGIR | 3 |