Copy the page URI to the clipboard
Zhang, Cheng; Zhang, Peng; Li, Jingfei and Song, Dawei
(2016).
DOI: https://doi.org/10.1145/2911451.2911454
Abstract
Traditional information retrieval systems rank documents according to their relevance to users' input queries. State of the art commercial search engines (SEs) train ranking models and suggest query refinements by exploiting collective intelligence implicitly using global users' query logs. However, they do not provide an explicit channel for users to communicate with each other in the search process. By asking or discussing with other users on the fly, a user could find relevant information more conveniently and gain a better search experience. In this paper, we present a demo of novel Search Engine with a live Chat Channel (SECC). SECC can group users automatically based on their input queries and allow them to communicate with each other in real time through a chat interface.
Viewing alternatives
Download history
Metrics
Public Attention
Altmetrics from AltmetricNumber of Citations
Citations from DimensionsItem Actions
Export
About
- Item ORO ID
- 47229
- Item Type
- Conference or Workshop Item
- ISBN
- 1-4503-4069-5, 978-1-4503-4069-4
- Keywords
- information retrieval; search engine; collective intelligence; chat channel
- Academic Unit or School
-
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM) - Copyright Holders
- © 2016 ACM
- Depositing User
- Dawei Song