A new visual search interface for web browsing

Li, Zongfang; Jin, Tao; Lau, Francis C. M.

doi:10.1145/1498759.1498821

Cited by 16 publications

(17 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this context, Yippy 8 (formerly known as Clusty) and the work presented in [29] are both efforts to aid web searchers by organizing search results in topical clusters. Both approaches add a sidebar containing clusters next to the search results list.…”

Section: Search Results Manipulationmentioning

confidence: 99%

Semantifying queries over large-scale Web search engines

Papadakis

Stefanidakis

Stamou

et al. 2012

J Internet Serv Appl

View full text Add to dashboard Cite

In many situations, searching the web is synonymous to information seeking. Currently, web search engines are the most popular vehicle via which people get access to the web. Their popularity is partially due to the intrinsic way that people interact with them, i.e., by typing some keywords to the corresponding input box. Despite their popularity, search engines often fail to satisfy certain information needs, especially when the latter are haze and poorly articulated. In this paper, we focus on the occasions when largescale web search engines find it difficult to cope with specific information-seeking behaviors and we accordingly introduce a query construction service that is targeted towards the solution of this problem. The proposed service leverages information coming from various DBpedia datasets and provides an intuitive GUI via which searchers determine the semantic orientation of their queries before these are addressed to the underlying search engine. The evaluation of the query construction service justifies the motive of this paper and indicates that it can considerably improve the searchers' querying ability when search engines fail to provide adequate help.

show abstract

Section: Search Results Manipulationmentioning

confidence: 99%

Semantifying queries over large-scale Web search engines

Papadakis

Stefanidakis

Stamou

et al. 2012

J Internet Serv Appl

View full text Add to dashboard Cite

show abstract

“…After the user has posed a query, s/he can decide to "scatter" the results into a fixed number of clusters; then, s/he can "gather" the most promising clusters, possibly to scatter them again in order to further refine the search. Many other works on text (in particular, Web page) search results clustering are along this line, such as [19,22,24,36,38,39]. For more details, there is an excellent survey [4] regarding this topic published recently.…”

Section: Search Results Clusteringmentioning

confidence: 99%

Clustering Web video search results based on integration of multiple features

et al. 2010

View full text Add to dashboard Cite

The usage of Web video search engines has been growing at an explosive rate. Due to the ambiguity of query terms and duplicate results, a good clustering of video search results is essential to enhance user experience as well as improve retrieval performance. Existing systems that cluster videos only consider the video content itself. This paper presents the first system that clusters Web video search results by fusing the evidences from a variety of information sources besides the video content such as title, tags and description. We propose a novel framework that can integrate multiple features and enable us to adopt existing clustering algorithms. We discuss our careful design of different components of the system and a number World Wide Web (2011) 14:53-73 of implementation decisions to achieve high effectiveness and efficiency. A thorough user study shows that with an innovative interface showing the clustering output, our system delivers a much better presentation of search results and hence increases the usability of video search engines significantly.

show abstract

“…There is a wide body of work for visual search [63], visual query [16] and visual information seeking [4] interfaces, both in the desktop [64,10] and mobile contexts [25]. These systems focus on the use of rapid filtering to iteratively refine search parameters, and thus reduce the result set.…”

Section: Visual Queryingmentioning

confidence: 99%

Don't Just Swipe Left, Tell Me Why

Beltrán¹,

Huang

Abouzied

et al. 2017

Proceedings of the 22nd International Conference on Intelligent User Interfaces

View full text Add to dashboard Cite

Despite several advances in information retrieval systems and user interfaces, the specification of queries over text-based document collections remains a challenging problem. Query specification with keywords is a popular solution. However, given the widespread adoption of gesture-driven interfaces such as multitouch technologies in smartphones and tablets, the lack of a physical keyboard makes query specification with keywords inconvenient. We present BINGO, a novel gestural approach to querying text databases that allows users to refine their queries using a swipe gesture to either "like" or "dislike" candidate documents as well as express the reasons they like or dislike a document by swiping through automatically generated "reason bins". Such reasons refine a user's query with additional keywords. We present an online and efficient bin generation algorithm that presents reason bins at gesture articulation. We motivate and describe BINGO's unique interface design choices. Based on our analysis and user studies, we demonstrate that query specification by swiping through reason bins is easy and expressive.

show abstract

A new visual search interface for web browsing

Cited by 16 publications

References 21 publications

Semantifying queries over large-scale Web search engines

Semantifying queries over large-scale Web search engines

Clustering Web video search results based on integration of multiple features

Don't Just Swipe Left, Tell Me Why

Contact Info

Product

Resources

About