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• Implicit and explicit feedback from news users: collected in a user study from a variety of users with explicit feedback, implicit feedback, a wider range of information about documents and topics, and a heterogeneous set of documents. This data set was originally collected by the PI in Carnegie Mellon University.

• Conversation recommender systems data: can be used for training Natural Language Understanding models and models for making recommendations in dialogues. It includes data in movie domain and restaurant domain. The data set was used for the experiments in our SIGIR 2019 paper

• Meal plan data: contains the data used in the meal plan user study, as described in our SIGIR 2018 paper "Item Retrieval As Utility Estimation".

• Airline Ticket Retrieval data: contains the data used in the air ticket search user study, as described in our SIGIR 2018 paper "Item Retrieval As Utility Estimation".

• Movie faceted search data: This dataset contains faceted metadata describing contemporary American films, along with relevance judgments by actual human users. This dataset can be used for developing personalized faceted search interfaces, among other projects requiring rich, structured metadata. This dataset was used for the experiments “Personalized Faceted Search” in WWW 08.

• Annotated twitter social bully data used in our paper CIKM 2012 paper 4Is of social bully filtering: identity, inference, influence, and intervention (available upon request)

• Code for the algorithm described in "Yi Zhang, Wei Xu, Fast Exact Maximum Likelihood Estimation for Mixture of Language Models, Poster in Proceedings of the 30st Annual International ACM SIGIR"

TBA:

Conversational Recommendation System

Multi modality based dialog systems

Social bully detection algorithm