Interest Alignment for Denoising Sequential Recommendation

• The paper demonstrates that the IADSR framework extracts sparse, denoised interest signals to improve next-item prediction accuracy.
• It employs a principled sparse interest extraction mechanism using self-attention for robust filtering of noisy user behavior.
• Empirical evaluations indicate substantial improvements in Hit Rate and NDCG metrics compared to traditional unified embedding models.

Interest Alignment for Denoising Sequential Recommendation (IADSR) encompasses a methodological shift in recommender systems, wherein the central goal is to robustly filter noisy, irrelevant, or spurious interaction signals from user histories by explicitly aligning the recommendation mechanism with the true, multi-faceted interests of each user. Rather than assuming all user behaviors are equally informative—or relying solely on recency or frequency—IADSR approaches integrate principled denoising strategies and interest alignment modules to isolate, activate, and aggregate the behavioral signals most predictive and causally connected to the user’s future actions.

1. Motivations and Problem Framing

Traditional sequential recommendation models typically operate on the premise that a user’s entire behavior sequence, or its recent segment, is an accurate reflection of current interests. These models represent user history as a single embedding vector derived from all past interactions. However, empirical analyses reveal that real user sequences contain a mix of heterogeneous interests, transient explorations, and substantial noise—including accidental clicks, fleeting interests, or popularity-driven biases. The composition and dominance of these factors can mask the underlying intention, leading to “interest misalignment” in prediction and reduced recommendation precision.

Interest Alignment for Denoising Sequential Recommendation (IADSR) was proposed to directly address this issue by introducing framework elements that:

• Explicitly model the multiple latent interests present in user histories
• Denoise behavioral sequences by selecting, weighting, or extracting only the interests relevant to future preference
• Prevent over-fitting to recent, frequent, or popular actions that distort genuine user intent
• Adaptively aggregate multi-interest signals to derive a dynamic prediction based on aligned interests

This framing is exemplified by the Sparse-Interest Network (SINE) (Tan et al., 2021), which, rather than relying on a “unified embedding vector,” constructs adaptive, sparse multi-interest representations and actively aligns them for next-item prediction.

2. Sparse-Interest Extraction and Denoising Mechanisms

A hallmark of IADSR is the use of mechanisms that infer a sparse set of interest prototypes or conceptual clusters from a large candidate pool, denoising the user’s interaction history by discarding irrelevant or outlier interests. The process in SINE (Tan et al., 2021) consists of the following steps:

• Represent the behavior sequence as an embedding matrix $X_u$.
• Compute a virtual concept (summary vector) $\mathbf{z}_u$ via self-attention:

$$\mathbf{a} = \operatorname{softmax}(\tanh(X_u W_1) W_2), \quad \mathbf{z}_u = (\mathbf{a}^T X_u)^T$$

• Match $\mathbf{z}_u$ against a global prototype matrix $C$ to obtain relevance scores $s_u = \langle C, \mathbf{z}_u \rangle$.
• Select only the top-$K$ prototypes to activate per user, yielding a sparse, highly focused set of interest vectors.
• Each of the activated prototypes yields a distinct “interest embedding” that is instantiated by attending over the behavior sequence, creating denoised and semantically-aligned user representations.

Sparse interest extraction both denoises the high-dimensional behavior signal and permits the modeling of a large, fine-grained pool of potential interests, overcoming the limitations of cluster-based methods which are constrained by category coarseness and clustering errors.

3. Interest Aggregation and Active Alignment

After extracting multiple candidate interests, IADSR frameworks aggregate these representations in an adaptive, intention-driven way to model the next action. The SINE aggregation module demonstrates a principled approach:

• Assign an “intention” probability to each item in the sequence with respect to each activated interest.
• Compute per-interest attention weights and form interest embeddings via weighted sums.
• Predict a “next intention” vector by reformulating the sequence using intention assignments and further processing with non-linear transformations (self-attention or MLP layers).
• Finally, aggregate the multiple interest embeddings into a single user vector via attention computed from the predicted intention:

$$e_{uk} = \frac{\exp((C_{u,\text{apt}})^T \varphi^k(x_u)/\tau)}{\sum_{k'} \exp((C_{u,\text{apt}})^T \varphi^{k'}(x_u)/\tau)}$$

$v_u = \sum_k e_{uk} \cdot \varphi^k(x_u)$

Here, $\varphi^k(x_u)$ denotes the $k$th interest embedding and $C_{u,\text{apt}}$ the adaptively-predicted intention.

This mechanism performs “interest alignment” by weighting and combining only those interests that best match the predicted current intention, resulting in a denoised, purpose-specific representation for next-item scoring.

4. Empirical Findings and Impacts

Empirical studies demonstrate that IADSR models, particularly those deploying sparse interest extraction and active aggregation, yield substantial improvements on public and industrial benchmarks when compared to conventional sequential and multi-interest baselines (Tan et al., 2021). Key impacts include:

• Substantial lift in Hit Rate (HR) and NDCG metrics (e.g., up to 34% improvement in HR@50 over the best competitor on ULarge)
• Enhanced performance in domains with highly diverse item pools or user behaviors exhibiting multi-faceted interests
• Superior robustness to noisy, imbalanced, or temporally-skewed data, as the denoising effect is achieved by filtering out non-predictive interests rather than naively assigning equal weight to all behaviors
• Visualizations and ablation studies confirming tighter, more semantically coherent user segmentation and improved capability to match the true next-item preference

5. Theoretical Underpinnings and Model Properties

The success of IADSR-oriented models such as SINE can be partially attributed to several critical theoretical elements:

• The use of large latent concept pools allows fine granularity, capturing subtle or underrepresented interests without requiring explicit manual item categorization.
• Adaptive, top-$K$ activation introduces both computational scalability and regularization, ensuring the model does not overfit to the noise of rarely-activated or spurious prototypes.
• Fully differentiable pipeline and joint end-to-end training enable alignment of all model parameters for the final recommendation objective, learning both to extract denoised interests and to align them according to downstream task loss.

These properties confer flexibility, scalability, and general applicability across a variety of sequential recommendation settings.

6. Broader Connections and Future Directions

IADSR as a methodological paradigm generalizes beyond a single implementation, bridging research trends in multi-interest modeling (Wu et al., 2021), denoising transformers (Chen et al., 2022), OOD stability (Liu et al., 2023), and causal disentanglement (Ren et al., 2023). The explicit alignment between extracted interest signals and predicted intentions shares conceptual lineage with attention mechanisms, but the progressive denoising and adaptive aggregation set IADSR approaches apart in their focus on robust, noise-tolerant user modeling.

Potential avenues for future research include:

• Application of IADSR methods to scenarios with rapidly changing item spaces or user populations, leveraging their ability to “track” emerging dominant interests
• Integration with counterfactual modeling and debiasing schemes to further correct for systemic biases or exogenous shocks (e.g., popularity bias)
• Exploration of techniques that dynamically determine the optimal number of interest prototypes or adaptively re-weight denoising strength according to user context and noise characteristics
• Fusion with multi-modal or multi-behavioral data for even finer-grained interest alignment and denoising capabilities

These directions build upon the foundational advances established by the SINE framework and the IADSR concept, supporting the evolution toward robust, adaptive, and interpretable sequential recommender systems.