Prior work on personalized recommendations has focused on exploiting explicit signals from user-specific queries, clicks, likes and ratings. This paper investigates tapping into a different source of implicit signals of interests and tastes: online chats between users. The paper develops an expressive model and effective methods for personalizing search-based entity recommendations. User models derived from chats augment different methods for re-ranking entity answers for medium-grained queries. The paper presents specific techniques to enhance the user models by capturing domain-specific vocabularies and by entity based expansion. Experiments are based on a collection of online chats from a controlled user study covering three domains: books, travel, food. We evaluate different configurations and compare chat-based user models against concise user profiles from questionnaires. Overall, these two variants perform on par in terms of NCDG@20, but each has advantages on certain domains.
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 (to appear).
The dataset contains user data (filled questionnaires, chats, assessments) is downloadable here:
YGWYC_dataset_012021.zip (released on January 2021)
Please refer to the README for more details.
This data is licensed under Creative Commons BY-NC 4.0.
Prior work on personalizing web search results has focused on considering query-and-click logs to capture users' individual interests. For product search, extensive user histories about purchases and ratings have been exploited. However, for general entity search, such as for books on specific topics or travel destinations with certain features, personalization is largely underexplored. In this paper, we address personalization of book search, as an exemplary case of entity search, by exploiting sparse user profiles obtained through online questionnaires. We devise and compare a variety of re-ranking methods based on language models or neural learning. Our experiments show that even very sparse information about individuals can enhance the effectiveness of the search results.
Ghazaleh Haratinezhad Torbati, Andrew Yates, Gerhard Weikum. Personalized Entity Search by Sparse and Scrutable User Profiles. Proceedings of the Fifth ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR 2020), pages 427-431, March 2020.