Abstract
In recent years, the amount of data generated by online platforms has grown exponentially, making it challenging for users to process large amounts of information. As a result, recommendation systems greatly improve user experience and retention by efficiently matching the content or products that users are interested in. However, most existing models only consider long-term preferences, ignoring the dynamic users’ preferences. To address this issue, this paper proposes a novel recommendation method that leverages heterogeneous information networks to capture both long-term and short-term preferences. Our model involves two components: a heterogeneous graph neural network that captures the long-term preferences of users and an attention mechanism that focuses on short-term preferences. The heterogeneous graph neural network extracts relevant information from the input sessions to learn feature representations, and the attention mechanism weights the features according to each user’s current interests. We evaluate our method on two public datasets, and our results show that our model outperforms existing approaches in terms of accuracy and robustness. In conclusion, our proposed method provides a useful framework for developing more efficient and proactive recommendation systems that can adapt to users’ ever-changing preferences. The incorporation of both long-term and short-term preferences allows our model to provide more accurate and personalized recommendations, improving the user experience.
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An, Z., Tan, Y., Zhang, J., Jiang, Z., Li, C. (2023). A Session Recommendation Model Based on Heterogeneous Graph Neural Network. In: Jin, Z., Jiang, Y., Buchmann, R.A., Bi, Y., Ghiran, AM., Ma, W. (eds) Knowledge Science, Engineering and Management. KSEM 2023. Lecture Notes in Computer Science(), vol 14119. Springer, Cham. https://doi.org/10.1007/978-3-031-40289-0_13
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