User modeling is critical for understanding user intents, while it is also challenging as user intents are so diverse and not directly observable. Most existing works exploit specific types of behavior signals for user modeling, e.g., opinionated data or network structure; but the dependency among different types of user-generated data is neglected.
We focus on self-consistence across multiple modalities of user-generated data to model user intents. A probabilistic generative model is developed to integrate two companion learning tasks of opinionated content modeling and social network structure modeling for users. Individual users are modeled as a mixture over the instances of paired learning tasks to realize their behavior heterogeneity, and the tasks are clustered by sharing a global prior distribution to capture the homogeneity among users. Extensive experimental evaluations on large collections of Amazon and Yelp reviews with social network structures confirm the effectiveness of the proposed solution. The learned user models are interpretable and predictive: they enable more accurate sentiment classification and item/friend recommendations than the corresponding baselines that only model a singular type of user behaviors.
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User-dependent neural sequence models for continuous-time event data
Continuous-time event data are common in applications such as individual behavior
data, financial transactions, and medical health records. Modeling such data can be
very challenging, in particular for applications with many different types of events,
since it requires a model to predict the event types as well as the time of occurrence.
Recurrent neural networks that parameterize time-varying intensity functions are
the current state-of-the-art for predictive modeling with such data. These models
typically assume that all event sequences come from the same data distribution.
However, in many applications event sequences are generated by different sources,
or users, and their characteristics can be very different. In this paper, we extend the
broad class of neural marked point process models to mixtures of latent embeddings,
where each mixture component models the characteristic traits of a given user. Our
approach relies on augmenting these models with a latent variable that encodes
user characteristics, represented by a mixture model over user behavior that is
trained via amortized variational inference. We evaluate our methods on four large
real-world datasets and demonstrate systematic improvements from our approach
over existing work for a variety of predictive metrics such as log-likelihood, next
event ranking, and source-of-sequence identification.
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- NSF-PAR ID:
- 10272502
- Date Published:
- Journal Name:
- Advances in neural information processing systems
- ISSN:
- 1049-5258
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
