English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Paper

You Get What You Chat: Using Conversations to Personalize Search-based Recommendations

MPS-Authors
/persons/resource/persons231571

Haratinezhad Torbati,  Ghazaleh
Databases and Information Systems, MPI for Informatics, Max Planck Society;

/persons/resource/persons206666

Yates,  Andrew
Databases and Information Systems, MPI for Informatics, Max Planck Society;

/persons/resource/persons45720

Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

arXiv:2109.04716.pdf
(Preprint), 322KB

Supplementary Material (public)
There is no public supplementary material available
Citation

Haratinezhad Torbati, G., Yates, A., & Weikum, G. (2021). You Get What You Chat: Using Conversations to Personalize Search-based Recommendations. Retrieved from https://arxiv.org/abs/2109.04716.


Cite as: https://hdl.handle.net/21.11116/0000-0009-64B9-6
Abstract
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 in certain domains.