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

نویسندگان

چکیده

Prior work on personalized recommendations has focused exploiting explicit signals from user-specific queries, clicks, likes and ratings. This paper investigates tapping into a different source of implicit interests tastes: online chats between users. The develops an expressive model effective methods for personalizing search-based entity recommendations. User models derived augment re-ranking answers medium-grained queries. presents specific techniques to enhance the user by capturing domain-specific vocabularies entity-based expansion. Experiments are based collection controlled study covering three domains: books, travel, food. We evaluate configurations compare chat-based against concise profiles questionnaires. Overall, these two variants perform par in terms NCDG@20, but each advantages certain domains.

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ژورنال

عنوان ژورنال: Lecture Notes in Computer Science

سال: 2021

ISSN: ['1611-3349', '0302-9743']

DOI: https://doi.org/10.1007/978-3-030-72113-8_14