Call For Papers
WSDM ConvERSe is a full day workshop taking place on Friday, February 7, 2020 in conjunction with WSDM 2020 in Houston, Texas.
Important Dates
- Submissions Due -
Nov 15, 2019 Dec 15, 2019 (11: 59 P.M. AoE)
- Notification - Jan 7, 2020
- Camera Ready Version of Papers Due - Jan 15, 2020
- WSDM ConvERSe Full day Workshop - Feb 7, 2020
We invite quality research contributions, position and opinion papers addressing relevant challenges in the domain of eCommerce. We invite submission of papers and posters of two to ten pages (including references), representing original research, preliminary research results, proposals for new work, position and opinion papers. All submitted papers and posters will be single-blind and will be peer reviewed by an international program committee of researchers of high repute. Accepted submissions will be presented at the workshop.
Topics
Topics of interest include, but are not limited to:
- Active learning for conversational systems
- Deep learning and online learning approaches for conversational systems
- User modeling
- Intent understanding and intent modeling
- Design of conversational systems and agents
- User-interaction and User-experience design
- Preference elicitation approaches
- Cold-start recommendations
- Proactive recommendations
- Critiquing in conversational recommenders
- Question-asking in conversational recommenders
- Personalization in conversational recommenders
- Recommendations-explanations in conversational systems
- Natural language interaction with conversational systems
- Dialogue management for conversational recommenders
- Conversational group recommenders
- Interaction methods in conversational recommenders
- Evaluation metrics and approaches for multi-turn conversational systems
- Integrating user feed-back and responses
- Narrative-driven recommendations
- Resources and data sets
Note: We welcome submissions in conversational systems from other related domains such as news, music etc.
Data/ Resource Track
In order to promote academic research in the eCommerce domain, we plan to accept a small number of high quality dataset contributions. These submissions should be accompanied by a clear and detailed description of the dataset, some potential questions and applications that arise from it. Preliminary empirical investigations conveying any insight about the data will increase the quality of the submission.
