This archive file contains experimental data for the research project on using conversations for personalized search-based recommendations, carried out by Ghazaleh H. Torbati at the Max Planck Institute for Informatics in Saarbruecken, Germany. The data consists of user surveys (by filling questionnaires), pair-wise user chats and search result assessments by users. All user data is anonymized, and users gave explicit consent to releasing the data for research purposes. Data guide: - surveys: There are 3 thematically domain-specific surveys filled by each user, plus a general one. Each row shows one user's answers. - chats: Each file shows one pair-wise user chat session. Each row shows one user utterance. - assessments: These files contain the search-result assessments by the participating users. -- "random20" contains the assessments for random-20 results. -- "top10" contains the assessments for top-10 ranked results. -- "all" contains all assessments (the union of the previous two). The urlID and queryID mappings can also be found in this folder. - original_rankings: These files contain the original ranks for the assessed search results obtained by a commercial search engine in April 2019 for the domains of books and food and in January 2020 for the travel domain. Chats and surveys are annotated with entity mentions as follows: [[NAMED ENTITY MENTION]] and [[[CONCEPT MENTION]]] License: This data is licensed under Creative Commons BY-NC 4.0, see https://creativecommons.org/licenses/by-nc/4.0/ Citation: When using this dataset, please cite the following publication: Ghazaleh Haratinezhad Torbati, Andrew Yates, Gerhard Weikum. You Get What You Chat: Using Conversations to Personalize Search-based Recommendations. In Proceedings of the 43nd European Conference on Information Retrieval (ECIR) 2021.