1,207 research outputs found
Modeling Multi-interest News Sequence for News Recommendation
A session-based news recommender system recommends the next news to a user by
modeling the potential interests embedded in a sequence of news read/clicked by
her/him in a session. Generally, a user's interests are diverse, namely there
are multiple interests corresponding to different types of news, e.g., news of
distinct topics, within a session. %Modeling such multiple interests is
critical for precise news recommendation. However, most of existing methods
typically overlook such important characteristic and thus fail to distinguish
and model the potential multiple interests of a user, impeding accurate
recommendation of the next piece of news. Therefore, this paper proposes
multi-interest news sequence (MINS) model for news recommendation. In MINS, a
news encoder based on self-attention is devised on learn an informative
embedding for each piece of news, and then a novel parallel interest network is
devised to extract the potential multiple interests embedded in the news
sequence in preparation for the subsequent next-news recommendations. The
experimental results on a real-world dataset demonstrate that our model can
achieve better performance than the state-of-the-art compared models
Improving End-to-End Sequential Recommendations with Intent-aware Diversification
Sequential Recommendation (SRs) that capture users' dynamic intents by
modeling user sequential behaviors can recommend closely accurate products to
users. Previous work on SRs is mostly focused on optimizing the recommendation
accuracy, often ignoring the recommendation diversity, even though it is an
important criterion for evaluating the recommendation performance. Most
existing methods for improving the diversity of recommendations are not ideally
applicable for SRs because they assume that user intents are static and rely on
post-processing the list of recommendations to promote diversity. We consider
both recommendation accuracy and diversity for SRs by proposing an end-to-end
neural model, called Intent-aware Diversified Sequential Recommendation (IDSR).
Specifically, we introduce an Implicit Intent Mining module (IIM) into SRs to
capture different user intents reflected in user behavior sequences. Then, we
design an Intent-aware Diversity Promoting (IDP) loss to supervise the learning
of the IIM module and force the model to take recommendation diversity into
consideration during training. Extensive experiments on two benchmark datasets
show that IDSR significantly outperforms state-of-the-art methods in terms of
recommendation diversity while yielding comparable or superior recommendation
accuracy
Sequential Recommendation Based on Multivariate Hawkes Process Embedding With Attention.
Recommender systems are important approaches for dealing with the information overload problem in the big data era, and various kinds of auxiliary information, including time and sequential information, can help improve the performance of retrieval and recommendation tasks. However, it is still a challenging problem how to fully exploit such information to achieve high-quality recommendation results and improve users' experience. In this work, we present a novel sequential recommendation model, called multivariate Hawkes process embedding with attention (MHPE-a), which combines a temporal point process with the attention mechanism to predict the items that the target user may interact with according to her/his historical records. Specifically, the proposed approach MHPE-a can model users' sequential patterns in their temporal interaction sequences accurately with a multivariate Hawkes process. Then, we perform an accurate sequential recommendation to satisfy target users' real-time requirements based on their preferences obtained with MHPE-a from their historical records. Especially, an attention mechanism is used to leverage users' long/short-term preferences adaptively to achieve an accurate sequential recommendation. Extensive experiments are conducted on two real-world datasets (lastfm and gowalla), and the results show that MHPE-a achieves better performance than state-of-the-art baselines
AMER: Automatic Behavior Modeling and Interaction Exploration in Recommender System
User behavior and feature interactions are crucial in deep learning-based
recommender systems. There has been a diverse set of behavior modeling and
interaction exploration methods in the literature. Nevertheless, the design of
task-aware recommender systems still requires feature engineering and
architecture engineering from domain experts. In this work, we introduce AMER,
namely Automatic behavior Modeling and interaction Exploration in Recommender
systems with Neural Architecture Search (NAS). The core contributions of AMER
include the three-stage search space and the tailored three-step searching
pipeline. In the first step, AMER searches for residual blocks that incorporate
commonly used operations in the block-wise search space of stage 1 to model
sequential patterns in user behavior. In the second step, it progressively
investigates useful low-order and high-order feature interactions in the
non-sequential interaction space of stage 2. Finally, an aggregation
multi-layer perceptron (MLP) with shortcut connection is selected from flexible
dimension settings of stage~3 to combine features extracted from the previous
steps. For efficient and effective NAS, AMER employs the one-shot random search
in all three steps. Further analysis reveals that AMER's search space could
cover most of the representative behavior extraction and interaction
investigation methods, which demonstrates the universality of our design. The
extensive experimental results over various scenarios reveal that AMER could
outperform competitive baselines with elaborate feature engineering and
architecture engineering, indicating both effectiveness and robustness of the
proposed method
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