IDNP: Interest Dynamics Modeling using Generative Neural Processes for Sequential Recommendation

• URL: http://arxiv.org/abs/2208.04600v1
• Date: Tue, 9 Aug 2022 08:33:32 GMT
• Title: IDNP: Interest Dynamics Modeling using Generative Neural Processes for Sequential Recommendation
• Authors: Jing Du, Zesheng Ye, Lina Yao, Bin Guo, Zhiwen Yu
• Abstract summary: We present an textbfInterest textbfDynamics modeling framework using generative textbfNeural textbfProcesses, coined IDNP, to model user interests from a functional perspective. Our model outperforms state-of-the-arts on various evaluation metrics.
• Score: 40.4445022666304
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent sequential recommendation models rely increasingly on consecutive short-term user-item interaction sequences to model user interests. These approaches have, however, raised concerns about both short- and long-term interests. (1) {\it short-term}: interaction sequences may not result from a monolithic interest, but rather from several intertwined interests, even within a short period of time, resulting in their failures to model skip behaviors; (2) {\it long-term}: interaction sequences are primarily observed sparsely at discrete intervals, other than consecutively over the long run. This renders difficulty in inferring long-term interests, since only discrete interest representations can be derived, without taking into account interest dynamics across sequences. In this study, we address these concerns by learning (1) multi-scale representations of short-term interests; and (2) dynamics-aware representations of long-term interests. To this end, we present an \textbf{I}nterest \textbf{D}ynamics modeling framework using generative \textbf{N}eural \textbf{P}rocesses, coined IDNP, to model user interests from a functional perspective. IDNP learns a global interest function family to define each user's long-term interest as a function instantiation, manifesting interest dynamics through function continuity. Specifically, IDNP first encodes each user's short-term interactions into multi-scale representations, which are then summarized as user context. By combining latent global interest with user context, IDNP then reconstructs long-term user interest functions and predicts interactions at upcoming query timestep. Moreover, IDNP can model such interest functions even when interaction sequences are limited and non-consecutive. Extensive experiments on four real-world datasets demonstrate that our model outperforms state-of-the-arts on various evaluation metrics.

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