USE: Dynamic User Modeling with Stateful Sequence Models

• URL: http://arxiv.org/abs/2403.13344v1
• Date: Wed, 20 Mar 2024 07:05:19 GMT
• Title: USE: Dynamic User Modeling with Stateful Sequence Models
• Authors: Zhihan Zhou, Qixiang Fang, Leonardo Neves, Francesco Barbieri, Yozen Liu, Han Liu, Maarten W. Bos, Ron Dotsch,
• Abstract summary: User Stateful Embedding (USE) generates user embeddings without the need for exhaustive reprocessing. We introduce a novel training objective named future W-behavior prediction to transcend the limitations of next-token prediction. We conduct experiments on 8 downstream tasks using Snapchat users' behavioral logs in both static (i.e., fixed user behavior sequences) and dynamic (i.e. periodically updated user behavior sequences) settings.
• Score: 26.74966828348815
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: User embeddings play a crucial role in user engagement forecasting and personalized services. Recent advances in sequence modeling have sparked interest in learning user embeddings from behavioral data. Yet behavior-based user embedding learning faces the unique challenge of dynamic user modeling. As users continuously interact with the apps, user embeddings should be periodically updated to account for users' recent and long-term behavior patterns. Existing methods highly rely on stateless sequence models that lack memory of historical behavior. They have to either discard historical data and use only the most recent data or reprocess the old and new data jointly. Both cases incur substantial computational overhead. To address this limitation, we introduce User Stateful Embedding (USE). USE generates user embeddings and reflects users' evolving behaviors without the need for exhaustive reprocessing by storing previous model states and revisiting them in the future. Furthermore, we introduce a novel training objective named future W-behavior prediction to transcend the limitations of next-token prediction by forecasting a broader horizon of upcoming user behaviors. By combining it with the Same User Prediction, a contrastive learning-based objective that predicts whether different segments of behavior sequences belong to the same user, we further improve the embeddings' distinctiveness and representativeness. We conducted experiments on 8 downstream tasks using Snapchat users' behavioral logs in both static (i.e., fixed user behavior sequences) and dynamic (i.e., periodically updated user behavior sequences) settings. We demonstrate USE's superior performance over established baselines. The results underscore USE's effectiveness and efficiency in integrating historical and recent user behavior sequences into user embeddings in dynamic user modeling.

Related papers

• Look into the Future: Deep Contextualized Sequential Recommendation [28.726897673576865]
  We propose a novel framework of sequential recommendation called Look into the Future (LIFT) LIFT builds and leverages the contexts of sequential recommendation. In our experiments, LIFT achieves significant performance improvement on click-through rate prediction and rating prediction tasks.
  arXiv  Detail & Related papers  (2024-05-23T09:34:28Z)
• PUNR: Pre-training with User Behavior Modeling for News Recommendation [26.349183393252115]
  News recommendation aims to predict click behaviors based on user behaviors. How to effectively model the user representations is the key to recommending preferred news. We propose an unsupervised pre-training paradigm with two tasks, i.e. user behavior masking and user behavior generation.
  arXiv  Detail & Related papers  (2023-04-25T08:03:52Z)
• Latent User Intent Modeling for Sequential Recommenders [92.66888409973495]
  Sequential recommender 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.
  arXiv  Detail & Related papers  (2022-11-17T19:00:24Z)
• Recommender Transformers with Behavior Pathways [50.842316273120744]
  We build the Recommender Transformer (RETR) with a novel Pathway Attention mechanism. We empirically verify the effectiveness of RETR on seven real-world datasets.
  arXiv  Detail & Related papers  (2022-06-13T08:58:37Z)
• PinnerFormer: Sequence Modeling for User Representation at Pinterest [60.335384724891746]
  We introduce PinnerFormer, a user representation trained to predict a user's future long-term engagement. Unlike prior approaches, we adapt our modeling to a batch infrastructure via our new dense all-action loss. We show that by doing so, we significantly close the gap between batch user embeddings that are generated once a day and realtime user embeddings generated whenever a user takes an action.
  arXiv  Detail & Related papers  (2022-05-09T18:26:51Z)
• Modeling Dynamic User Preference via Dictionary Learning for Sequential Recommendation [133.8758914874593]
  Capturing the dynamics in user preference is crucial to better predict user future behaviors because user preferences often drift over time. Many existing recommendation algorithms -- including both shallow and deep ones -- often model such dynamics independently. This paper considers the problem of embedding a user's sequential behavior into the latent space of user preferences.
  arXiv  Detail & Related papers  (2022-04-02T03:23:46Z)
• Learning Self-Modulating Attention in Continuous Time Space with Applications to Sequential Recommendation [102.24108167002252]
  We propose a novel attention network, named self-modulating attention, that models the complex and non-linearly evolving dynamic user preferences. We empirically demonstrate the effectiveness of our method on top-N sequential recommendation tasks, and the results on three large-scale real-world datasets show that our model can achieve state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-03-30T03:54:11Z)
• TEA: A Sequential Recommendation Framework via Temporally Evolving Aggregations [12.626079984394766]
  We propose a novel sequential recommendation framework based on dynamic user-item heterogeneous graphs. We exploit the conditional random field to aggregate the heterogeneous graphs and user behaviors for probability estimation. We provide scalable and flexible implementations of the proposed framework.
  arXiv  Detail & Related papers  (2021-11-14T15:54:23Z)
• Dynamic Memory based Attention Network for Sequential Recommendation [79.5901228623551]
  We propose a novel long sequential recommendation model called Dynamic Memory-based Attention Network (DMAN) It segments the overall long behavior sequence into a series of sub-sequences, then trains the model and maintains a set of memory blocks to preserve long-term interests of users. Based on the dynamic memory, the user's short-term and long-term interests can be explicitly extracted and combined for efficient joint recommendation.
  arXiv  Detail & Related papers  (2021-02-18T11:08:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.