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

TL;DR

This paper explores using online chat conversations as implicit signals to personalize search-based entity recommendations, developing models that incorporate domain-specific vocabularies and entity expansion.

Contribution

It introduces a novel approach to personalize recommendations by leveraging chat data, enhancing user models beyond traditional explicit signals.

Findings

Chat-based user models perform comparably to questionnaire profiles in NCDG@20.

Domain-specific vocabularies improve the personalization accuracy.

Entity expansion techniques enhance user modeling in recommendation systems.

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.