Session 3:

Sequential recommendation (SR) aims to predict the next items for users by learning the users’ representations from their historical sequences. In this process, most existing methods rely on a single globally shared encoder to model the relationship between input sequences and target items, implicitly learning input–target patterns. However, such implicit learning treats patterns uniformly and largely overlooks their intrinsic characteristics and differences among training samples. Through the empirical studies in this paper, we find that input–target patterns exhibit both conflicting and generalizable characteristics, which impose distinct modeling requirements. Neglecting these properties leads to suboptimal user representations and limited generalization. Motivated by these findings, we propose a novel MoE architecture, Automated Selection-based Mixture-of-Experts (ASMoE), with a dual-stage training scheme to address these issues. In our ASMoE, we introduce an automated expert selection mechanism to adaptively allocate selectable experts and accommodate diverse modeling requirements of input–target patterns. Furthermore, we develop a dual-stage training scheme to enhance our ASMoE for input–target pattern learning. The first stage performs initial learning over diverse patterns. In the second stage, we explicitly construct the potentially generalizable input–target patterns via a category-aware mask generator and a similarity-aware penalty, thereby facilitating the fine-tuning of our ASMoE towards generalizable knowledge. Extensive experiments on four public datasets demonstrate the effectiveness of our ASMoE. The source code and auxiliary material of our ASMoE are provided at https://anonymous.4open.science/r/ASMoE.

Sequential recommendation methods integrated with contrastive learning have been proven effective in addressing the data sparsity issue. However, most contrastive learning schemes directly perform random data augmentation on original sequences, which struggles to capture fine-grained features in users’ historical interaction sequences. Meanwhile, these augmentation methods lack semantic consistency. Additionally, most approaches employ a single aggregation strategy for user representation, making it difficult to comprehensively characterize user preferences. To tackle these issues, we propose a Fine-Grained Contrastive Learning with Dual Aggregation approach for Sequential Recommendation (FiCoRec). Specifically, we design four tailored data augmentation methods on user embedding sequences to ensure semantic consistency and adaptability, and construct rich self-supervised signals, thereby enabling fine-grained contrastive learning. Furthermore, we design a Dual Aggregation module to capture the Tail Aggregation features and Global Aggregation features of sequences, which facilitates the comprehensive learning of users’ short-term key interests and long-term global preferences. Extensive experiments conducted on four public datasets demonstrate that FiCoRec achieves superior performance compared with existing baseline models, with up to 45.93% increase on Mean Reciprocal Rank (MRR).

Modeling evolving user preferences from interaction sequences remains a core problem in sequential recommendation (SR). Recent work highlights intent learning for uncovering latent user motivations. Yet, existing methods either model intents within individual sequences or treat global intent signals as auxiliary supervision, limiting the explicit use of collective behavioral patterns and causing information isolation. Meanwhile, existing contrastive learning strategies are often costly and rely on suboptimal view construction, e.g., random sequence editing or weakly controlled model perturbations. We propose BIPCL, an end-to-end Bilateral Intent-enhanced, Embedding Perturbation-based Contrastive Learning framework. BIPCL integrates collective intent priors into both sequence- and item side representations via bilateral intent enhancement. Shared intent prototypes capture collective semantics from behaviorally similar entities and inject them into representations, alleviating information isolation and improving robustness. It further induces a non-separable cross-intent interaction, providing complementary sequence–item matching signals beyond unilateral intent modeling. To construct effective contrastive views, we introduce an embedding perturbation-based paradigm that directly perturbs structural item embeddings, yielding bounded and discriminative views while preserving temporal and structural dependencies. Compatibility studies across multiple CL-based SR backbones demonstrate the effectiveness of this paradigm beyond BIPCL. Extensive experiments show that BIPCL consistently outperforms state-of-the-art baselines. All code and datasets are publicly available at https://anonymous.4open.science/r/BIPCL-8E78/.

Sequential recommendation performance is persistently hindered by the long-tailed distribution of interaction data. Existing dual-view methods can alleviate this issue to some extent, but they typically rely on static fusion strategies and cannot adaptively regulate the contributions of semantic and collaborative signals at the sample level, nor can they transfer such decisions to training-time supervision. We propose TAGRec (Tailness-Aware Gated Recommendation), a unified tailness-aware framework that uses a single gate to coordinate both forward view fusion and backward supervision scheduling. Specifically, TAG estimates a contextual tailness prior from observable histories to produce a sample-level gate, T-MoV uses this gate to dynamically balance semantic and collaborative views, and ASCL preserves the semantic view as a stable anchor while adaptively strengthening asymmetric contrastive supervision on the collaborative branch. Experiments on four public benchmarks show that TAGRec consistently achieves the best or near-best performance, with especially clear gains in tail-heavy settings, while introducing only lightweight overhead and no extra online inference.

Side-information Integrated Sequential Recommendation (SISR) enhances preference modeling under sparse interactions by incorporating auxiliary item attributes. However, two core challenges remain. First, real interaction sequences inevitably contain noisy behaviors that distort user preference inference. Second, existing methods often fail to simultaneously preserve ID-driven transition patterns and fully exploit attribute-aware semantics. To address these challenges, we propose TSD-SR. Our approach first represents each fixed-length interaction sequence under a circular-shift topology, then applies Topology-aware Spectral Denoising, followed by a Decoupled Dual-branch Fusion module. Specifically, Topology-aware Spectral Denoising is performed on two streams: a pure ID stream and an early-fused stream that integrates item ID, attribute, and positional embeddings. This process suppresses accidental interactions and transient fluctuations prior to deep encoding, while preserving informative sequential patterns. Subsequently, a Decoupled Dual-branch Fusion module models ID-centric transition dependencies and attribute-enriched semantic context in parallel, capturing collaborative signals and side-information semantics while alleviating information invasion. Experiments on four datasets from two real-world benchmarks, namely Yelp and three domains from Amazon Reviews, demonstrate that TSD-SR consistently outperforms state-of-the-art sequential recommenders and side-information-aware baselines, while exhibiting robust performance in long-tail and noisy settings. Code: https://anonymous.4open.science/r/TSD-SR-2374

Sequential recommendation (SR) systems are widely deployed across modern online platforms and have been shown to be vulnerable to poisoning attacks. Such attacks inject fabricated user sequences into training data to promote target items. Existing methods achieve stealthiness by enforcing surface-level similarity to genuine data, including matching item frequencies, local transition patterns, and co-occurrence statistics. This assumption holds in idealized, homogeneous settings where user behaviors are narrow and repetitive. In real-world platforms, however, users exhibit rich and context-dependent behavioral intents. In such heterogeneous environments, surface-level mimicry fails to preserve the logical coherence of user intent, causing poisoned sequences to be detectable. We propose MaskPoison, an intent-guided poisoning framework that addresses this fundamental gap. Our method extracts intent anchors from real user sequences containing the target item. A masked discrete diffusion model then synthesizes poisoned sequences conditioned on these anchors as hard semantic constraints, ensuring alignment with the behavioral manifolds of genuine users. Extensive experiments demonstrate that MaskPoison outperforms existing attacks in both attack effectiveness and stealthiness, across homogeneous and heterogeneous recommendation scenarios alike. Our code is available at https://anonymous.4open.science/r/MaskPoison-Code-6678/.

Sequential recommendation (SR) models excel at capturing continuous user interests, but struggle with user reactivation – scenarios where users return after prolonged absence. We formally define the Zero-Observation Reactivation problem: a user possesses rich pre-gap behavioral history, yet the platform observes strictly zero signals during a macro-gap (delta t spanning months to years), causing their interests to evolve unobserved. Through systematic evaluation on three Amazon datasets (Video Games, CDs & Vinyl, Movies & TV) using a strictly chronologically-aligned Gap-Synthesize Protocol, we reveal a consistent empirical pattern: sequence intent lifespan exhibits strict monotonic decay, culminating in a structural collapse when delta t exceeds one year, affecting all major SR paradigms equally. To address this, we propose DeltaGate, a lightweight, backbone-agnostic post-processing plugin featuring Gap-Driven Dimensional Gating. Operating on a frozen backbone, DeltaGate performs dimension-level representation routing: as delta t increases, it selectively suppresses volatile short-term dimensions while preserving stable long-term interests, falling back to a zero-initialized global prior. Counterfactual perturbation provides causal evidence that this routing is genuinely delta t-drive. On the critical >365d zone, DeltaGate recovers up to +106.1% Hit@10 using only 66K trainable parameters (<4% overhead). Systematic comparison with end-to-end retraining reveals two complementary adaptation regimes: full retraining maximizes accuracy but causes gate saturation and embedding drift. DeltaGate is the only configuration that maintains interpretable dimensional routing with 46x higher parameter efficiency and zero backbone drift.

Modern recommender systems treat observed actions as reliable proxies for user preferences, yet interactions often reflect exploration or comparison rather than stable preference expression. As interfaces evolve from static layouts toward generative UIs and immersive extended reality (XR), the need for deeper, modality-agnostic user understanding grows: these adaptive environments must decide not only what to present but where, when, how prominently, and most importantly why a user acts. We propose an Inverse Theory of Mind (IToM) pipeline that reasons backward from observed interactions to infer the beliefs, preferences, and decision-making traits that explain behavior. The pipeline reconstructs each user’s decision context, including what was chosen and what alternatives were available, applies LLM-driven counterfactual reasoning to produce evidence-grounded natural-language belief statements, and synthesizes these beliefs through multi-hypothesis abductive inference into a structured user persona. We evaluate on the OPeRA dataset against ground-truth personality assessments, attitudinal surveys, and interview-based personas across four tasks: next action prediction, shopping category prediction, Big Five personality inference, and shopping attitude alignment. Results show that inferred personas match or exceed ground-truth personas and that multi-hypothesis reasoning is useful for personality prediction. We further demonstrate cross-modal transferability with a persona-driven spatial banking application on VisionOS.

Conversational recommender systems (CRS) have made substantial progress leveraging LLMs for preference elicitation and recommendation. Yet recent evidence suggests the field is approaching a structural ceiling: (1) recommendation accuracy degrades with deeper elicitation through a recall-specificity asymmetry, (2) the field’s most widely used metric shows near-zero correlation with self-reported user satisfaction, and (3) evaluation infrastructure suffers from reliability failures, benchmark biases, and invisibility to deceptive behaviors. This blue sky paper argues that moving past these limits requires a shift along two axes. Forward: from modeling only what users prefer toward understanding how they express preferences and why they hold them, engaging constructs such as linguistic hedging, conversational style, and motivational structure that current systems discard, and shifting from aggregate to individual-level evaluation. Outward: toward transdisciplinary engagement with psycholinguistics and pragmatics, which offer operationalizable tools for interpreting what users implicate beyond what they state, and with behavioral economics and cognitive science, which establish that preferences are constructed through interaction rather than retrieved from stable internal states. We propose the intersection of psycholinguistics and cognitive science as the most productive starting point for modeling users as whole persons rather than collections of attribute preferences.

Agentic Conversational Recommender Systems (ACRSs) are designed to recommend through multi-turn dialogue with users whose needs are not fully formed at the outset. However, their evaluation almost exclusively relies on user simulators that instantiate users with clear, pre-formed needs, reducing the interaction to a retrieval over attributes disclosed in the initial turns of the conversation. This covers only a narrow slice of the behaviors real users exhibit, and assessing the robustness and reliability of these systems requires simulated users that span a wider range. To this end, we introduce a stereotype-conditioned, open-weight user simulator that spans three behavioral stereotypes: Direct, Vague-Proactive, and Vague-Reactive. Benchmarking four state-of-the-art ACRSs across four e-commerce domains with our simulator, three findings emerge. First, under certain stereotypes the user stops contributing new information about the target as turns accumulate while the agent continues to act, a regime previously unobserved, which we name Unproductive Stagnation and formalize via Preference Coverage. Second, a systematic Robustness Gap emerges: as the simulated user shifts from decisive to passive, accuracy collapses while conversations grow longer. Third, accuracy degrades more sharply than Preference Coverage does, decomposing the gap into two separable capabilities current ACRSs lack: elicitation and retrieval, which current evaluation entangles in a single score. Our simulator makes this distinction reportable and gives the field a controllable axis along which elicitation and retrieval can be measured and compared.

Recommender systems enter their twentieth year still organized around passive preference inference: estimate latent interest from behavioral traces, then rank. Past: this abstraction was enormously productive—implicit feedback, matrix factorization, learning-to-rank, and large-scale offline evaluation defined the technical center of the field. Present: four mismatches now expose what prediction alone cannot settle—under-specified goals, evidence shifting from sparse clicks to natural-language preference statements, a wider action space than ranking, and evaluation that lags the proactive behavior it must capture. Future: the next decade should be organized around two first-class research objects, user-governed preference memory and calibrated initiative, composed inside a user-side / hybrid architecture and audited by an agency-centered evaluation that adds timing quality, memory correctness, and revocability to accuracy. To move from agenda to coordinated action, we ask the community to commit, within two RecSys cycles, to a shared reference schema, a mixed-initiative benchmark suite re-annotated from conversational-recommendation corpora, and a PPF-track reporting convention; as a first-mover deposit, we will publish a machine-readable JSON Schema for PreferenceMemoryRecord v0 as camera-ready supplementary material. These priorities do not replace ranking-centric work; they constitute the design surface on which the next decade of RecSys can be jointly read.

For two decades, recommender systems have been designed under the assumption that a human directly consumes each recommendation: receiving, interpreting, and acting upon it. The emergence of AI agents powered by large language models challenges this assumption. In the emerging Agentic Web, autonomous agents increasingly act on behalf of users, e.g., browsing, comparing, negotiating, and executing transactions, raising a central question: who is the consumer of a recommendation? In this position paper, we argue that the recommendation paradigm is undergoing a bifurcation. In delegable contexts, such as routine purchases, travel, and constrained transactional tasks, the primary operational consumer of recommendations is shifting from the human to the agent, requiring new optimization objectives, interaction protocols, and evaluation criteria. In experiential contexts, such as entertainment, art, and other subjective or high-stakes choices, humans remain the final judge of relevance, though agents may assist through pre-filtering and curation. We introduce a delegation spectrum that characterizes recommendation contexts along factors such as preference specifiability, outcome verifiability, and decision stakes, and we outline a research agenda spanning agent preference modeling, dual-audience optimization, and the emerging agent attention economy. We further discuss the implications of this shift for the design and evaluation of recommender systems.

Conversational recommender systems (CRS) have pursued progressively deeper personalization, dissolving the structural separation between system and user that characterized earlier paradigms by building rich models of individual preferences, memories, and emotional states through sustained dialogue. This paper argues that this trajectory carries a safety liability intrinsic to personalization itself: benign, organically accumulated user context degrades safety alignment across frontier LLMs through intent legitimation, memory-induced sycophancy, and cross-domain leakage, none of which require adversarial input. The vulnerability is compounded by a formal property of RLHF training that amplifies sycophantic tendencies, meaning the base LLMs on which CRS are built already carry a predisposition that personalization deepens. Emotionally vulnerable users are disproportionately affected, with population-level evidence of increasing emotional dependency and safety performance that degrades as user emotional intensity increases. This position paper traces the field’s evolution from static preference inference to conversational personalization, presents mechanistic and real-world evidence that the field’s core research objective and the safety vulnerability identified by the alignment community are structurally identical, and considers whether improved alignment, architectural decoupling, or scale can resolve the problem. We argue that the CRS community is well positioned to lead the development of personalization approaches that are safety-aware by design rather than safety-compromised by default.

As an interactive form of recommendation, critiquing demonstrates exceptional value in enhancing system steerability and transparency; nevertheless, its strategic significance has long been severely underrated. To address the pronounced chasm between current academic exploration and industrial practice, we thoroughly investigate the underlying causes of this divergence. Furthermore, we illustrate the critical role of this paradigm in optimizing algorithmic ecosystems, dismantling information cocoons, and establishing enduring user trust. In the era of generative recommendation driven by Large Language Models (LLMs), the utility of critiquing transcends mere explicit feedback, evolving into the foundational logic that facilitates the synergistic evolution of agents and humans at the architectural level. By integrating natural language interactions into the continuous self-reflection and evolution loops of agents, this mechanism necessitates a fundamental shift in recommendation logic, transitioning from unidirectional system delivery to human-machine collaborative decision-making. Looking forward, with the advent of the Agentic OS, critiquing is poised to elevate from a peripheral feedback tool to the core infrastructure that empowers users to steer complex intelligent systems. Consequently, by leveraging natural interactive interfaces and edge-based memory governance mechanisms, it ensures the long-term, precise evolution of user preferences while rigorously safeguarding privacy sovereignty. Ultimately, by reconstructing the theoretical framework surrounding the value of critiquing, we provide strategic guidelines for constructing transparent, steerable, and sustainable recommendation ecosystems.