Socialised Exploration and Sense-Making over Online Information Spaces

Description

This project aims to establish the methodological foundation of a novel Socialised Exploration and Sense-Making (SESM) paradigm over online information spaces. SESM refers to an “information journey” in which the user engages with a community (e.g., through instant dialogues with other users or chatbots) to interactively search and navigate through a shared online information space, for making sense of information and finding relevant information with respect to an information need. This is common in our everyday online information seeking practices, e.g., googling about a topic related to a task while chatting with a colleague about it. An instant chat with another user or a chatbot while searching may lead to a change of the user’s information need, such as topics of interest, understanding and opinion about a topic, criterion for relevance judgment (e.g., from “topicality” to “novelty”). This shifts away from the traditional Google-type query-response mode search.

However, currently the search process and the online social interaction process are largely un-integrated, and it is still unclear how they influence each other and work jointly to infer the user’s evolving information need and guide the user through the information space to meet the information need. We propose to use artificial intelligence and user interaction modelling techniques to capture the user’s evolving information need with respect to user instant dialogues and interactions, thus allowing users to better explore the information space within a domain.

Skills Required:

Good undergraduate degree (2.1 or above) or Master degree in Computing, ideally with experience in natural language processing and information retrieval.

Background Reading:

Wang J., Zhang, P., Zhang, C., Song, D. (2019). SCSS-LIE: A Novel Synchronous Collaborative Search System with a Live Interactive Engine. The 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR2019), pp. 1309-1312.

Zhang, C., Zhang, P., Li, J., Song, D. (2016). SECC: A Novel Search Engine Interface with Live Chat Channel. ACM SIGIR2016, 1137-1140.

Contact:

Prof. Dawei Song (dawei.song@open.ac.uk)