3,236 research outputs found
User Intent Prediction in Information-seeking Conversations
Conversational assistants are being progressively adopted by the general
population. However, they are not capable of handling complicated
information-seeking tasks that involve multiple turns of information exchange.
Due to the limited communication bandwidth in conversational search, it is
important for conversational assistants to accurately detect and predict user
intent in information-seeking conversations. In this paper, we investigate two
aspects of user intent prediction in an information-seeking setting. First, we
extract features based on the content, structural, and sentiment
characteristics of a given utterance, and use classic machine learning methods
to perform user intent prediction. We then conduct an in-depth feature
importance analysis to identify key features in this prediction task. We find
that structural features contribute most to the prediction performance. Given
this finding, we construct neural classifiers to incorporate context
information and achieve better performance without feature engineering. Our
findings can provide insights into the important factors and effective methods
of user intent prediction in information-seeking conversations.Comment: Accepted to CHIIR 201
Alligning Vertical Collection Relevance with User Intent
Selecting and aggregating different types of content from multiple vertical search engines is becoming popular in web search. The user vertical intent, the verticals the user expects to be relevant for a particular information need, might not correspond to the vertical collection relevance, the verticals containing the most relevant content. In this work we propose different approaches to define the set of relevant verticals based on document judgments. We correlate the collection-based relevant verticals obtained from these approaches to the real user vertical intent, and show that they can be aligned relatively well. The set of relevant verticals defined by those approaches could therefore serve as an approximate but reliable ground-truth for evaluating vertical selection, avoiding the need for collecting explicit user vertical intent, and vice versa
Latent User Intent Modeling for Sequential Recommenders
Sequential recommender models are essential components of modern industrial
recommender systems. These models learn to predict the next items a user is
likely to interact with based on his/her interaction history on the platform.
Most sequential recommenders however lack a higher-level understanding of user
intents, which often drive user behaviors online. Intent modeling is thus
critical for understanding users and optimizing long-term user experience. We
propose a probabilistic modeling approach and formulate user intent as latent
variables, which are inferred based on user behavior signals using variational
autoencoders (VAE). The recommendation policy is then adjusted accordingly
given the inferred user intent. We demonstrate the effectiveness of the latent
user intent modeling via offline analyses as well as live experiments on a
large-scale industrial recommendation platform.Comment: The Web Conference 2023, Industry Trac
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