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.
Zhang, Cheng; Zhang, Peng ; Li, Jingfei and Song, Dawei (2016). SECC: A Novel Search Engine Interface with Live Chat Channel. In: SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, ACM, New York, pp. 1137–1140.