Sequential Recommendation on Temporal Proximities with Contrastive Learning and Self-Attention
CoRR(2024)
Abstract
Sequential recommender systems identify user preferences from their past
interactions to predict subsequent items optimally. Although traditional
deep-learning-based models and modern transformer-based models in previous
studies capture unidirectional and bidirectional patterns within user-item
interactions, the importance of temporal contexts, such as individual
behavioral and societal trend patterns, remains underexplored. Notably, recent
models often neglect similarities in users' actions that occur implicitly among
users during analogous timeframes-a concept we term vertical temporal
proximity. These models primarily adapt the self-attention mechanisms of the
transformer to consider the temporal context in individual user actions.
Meanwhile, this adaptation still remains limited in considering the horizontal
temporal proximity within item interactions, like distinguishing between
subsequent item purchases within a week versus a month. To address these gaps,
we propose a sequential recommendation model called TemProxRec, which includes
contrastive learning and self-attention methods to consider temporal
proximities both across and within user-item interactions. The proposed
contrastive learning method learns representations of items selected in close
temporal periods across different users to be close. Simultaneously, the
proposed self-attention mechanism encodes temporal and positional contexts in a
user sequence using both absolute and relative embeddings. This way, our
TemProxRec accurately predicts the relevant items based on the user-item
interactions within a specific timeframe. We validate this work through
comprehensive experiments on TemProxRec, consistently outperforming existing
models on benchmark datasets as well as showing the significance of considering
the vertical and horizontal temporal proximities into sequential
recommendation.
MoreTranslated text
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined