Session-based recommendation (SBR), which focuses on next-item prediction for anonymous users based on short-term interaction sequences, has garnered increasing attention from researchers. While graph neural networks (GNNs) have become predominant in modeling complex item transition patterns, our empirical study reveals two critical limitations in existing GNN-based SBR methods. On the one hand, they struggle to differentiate between sessions sharing the same last item, resulting in indistinguishable session representations. On the other hand, the inherent popularity bias in session data leads to the over-recommendation of popular items. Inspired by contrastive learning techniques, this paper presents a unified mitigation framework for Same lAst-item confusion in Graph lEarning (SAGE) for SBR. In SAGE, we first obtain normalized session embeddings on constructed session graphs. We then build positive and negative samples of sessions through dual forward propagations and a novel negative sample selection strategy, followed by calculating contrastive loss. Finally, the enhanced session embeddings are utilized for prediction. Extensive experiments on two real-world datasets demonstrate that integrating SAGE with various state-of-the-art GNN-based SBR methods significantly improves their original performances.
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Can we design effective recommender systems free from users, IDs, and GNNs? Recommender systems are central to personalized content delivery across domains, with top-K item recommendation being a fundamental task to retrieve the most relevant items from historical interactions. Existing methods rely on entrenched design conventions, often adopted without reconsideration, such as storing per-user embeddings (user-dependent), initializing features from raw IDs (ID-dependent), and employing graph neural networks (GNN-dependent). These dependencies incur several limitations, including high memory costs, cold-start and over-smoothing issues, and poor generalization to unseen interactions. In this work, we propose AlphaFree, a novel recommendation method free from users, IDs, and GNNs. Our main ideas are to infer preferences on-the-fly without user embeddings (user-free), replace raw IDs with language representations (LRs) from pre-trained language models (ID-free), and capture collaborative signals through augmentation with similar items and contrastive learning, without GNNs (GNN-free). Extensive experiments on various real-world datasets show that AlphaFree consistently outperforms its competitors, achieving up to around 40% improvements over non-LR-based methods and up to 5.7% improvements over LR-based methods, while significantly reducing GPU memory usage by up to 69% under high-dimensional LRs.
The prediction objectives of online advertisement ranking models are evolving from probabilistic metrics like conversion rate (CVR) to numerical business metrics like post-click gross merchandise volume (GMV). Unlike the well-studied delayed feedback problem in CVR prediction, delayed feedback modeling for GMV prediction remains unexplored and poses greater challenges, as GMV is a continuous target, and a single click can lead to multiple purchases that cumulatively form the label. To bridge the research gap, we establish TRACE, a GMV prediction benchmark containing complete transaction sequences rising from each user click, which supports delayed feedback modeling in an online streaming manner. Our analysis and exploratory experiments on TRACE reveal two key insights: (1) the rapid evolution of the GMV label distribution necessitates modeling delayed feedback under online streaming training; (2) the label distribution of repurchase samples substantially differs from that of single-purchase samples, highlighting the need for separate modeling. Motivated by these findings, we propose RepurchasE-Aware Dual-branch prEdictoR (READER), a novel GMV modeling paradigm that selectively activates expert parameters according to repurchase predictions produced by a router. Moreover, READER dynamically calibrates the regression target to mitigate under-estimation caused by incomplete labels. Experimental results show that READER yields superior performance on TRACE over baselines, achieving a 2.19% improvement in terms of accuracy. We believe that our study will open up a new avenue for studying online delayed feedback modeling for GMV prediction, and our TRACE benchmark with the gathered insights will facilitate future research and application in this promising direction. Our code and dataset are available at https://github.com/alimama-tech/OnlineGMV.
Recommenders built upon implicit collaborative filtering are typically trained to distinguish between users' positive and negative preferences. When direct observations of the latter are unavailable, negative training data are constructed with sampling techniques. But since items often exhibit clustering in the latent space, existing methods tend to oversample negatives from dense regions, resulting in homogeneous training data and limited model expressiveness. To address these shortcomings, we propose a novel negative sampler with diversity guarantees. To achieve them, our approach first pairs each positive item of a user with one that they have not yet interacted with; this instance, called hard negative, is chosen as the top-scoring item according to the model. Instead of discarding the remaining highly informative items, we store them in a user-specific cache. Next, our diversity-augmented sampler selects a representative subset of negatives from the cache, ensuring its dissimilarity from the corresponding user's hard negatives. Our generator then combines these items with the hard negatives, replacing them to produce more effective (synthetic) negative training data that are informative and diverse. Experiments show that our method consistently leads to superior recommendation quality without sacrificing computational efficiency.
Recommendation systems play a central role in modern services, yet often treat item cover images as static attributes, overlooking their influence on user decisions. We introduce the task of cover recommendation and study few-shot, interaction-free selection using multimodal user interest profiles. To address cold-start and sparsity challenges in traditional methods, we propose Multimodal Cover Recommendation (MCRec), a framework that leverages Vision-Language Models (VLMs) for multimodal feature extraction. Our approach includes: (1) a Text-Guided Visual Interest Aggregation network (TGVIA) integrating visual and textual representations; (2) multimodal interest embeddings fused via templated prompts; and (3) a multimodal-driven textual inversion technique enabling training-free generalization to new scenarios. We further propose MCRec+, a fine-tuning variant using hybrid sampling. To support evaluation, we construct three benchmarks and propose two new metrics. Extensive experiments show our methods significantly outperform baselines across datasets, especially with average gains of 3.72% in Recall@1, 1.70% in APMS and 1.25% in MPMS on MCRec. Code and data are publicly available from https://github.com/WeixinZhengRec/MCRec.
With the rapid proliferation of short-video platforms and content-driven social networks, sequential recommendation models capable of accurately capturing user interests have become increasingly crucial. Among these, Transformer-based sequential recommendation models have gained widespread adoption due to their superior ability. The positional encoding (PE) in Transformer architectures serves to incorporate positional information into sequences. However, relying solely on original absolute positional information may be insufficient for sequential recommendation models. In contrast, the dwell time after interactions (i.e., the time intervals between consecutive user interactions) provides a more accurate reflection of users' emotional responses and evolving interests. Despite its significance, this aspect has often been overlooked in existing works. To fully utilize this information, our work introduces an adaptive PE method, termed TSAPE (Temporal-Series-Aware Positional Encoding). This approach introduces an innovative modeling of the sequence of time intervals between user interactions, rather than the numerical values of the intervals themselves, thereby capturing real-time feedback on user interests and integrating it with conventional PE mechanisms. Furthermore, we employ multiple layers of one-dimensional convolutional networks and attention mechanisms to endow the features with adaptive capabilities across various time interval scenarios. This enables TSAPE to more accurately capture sequential positional information at any given moment. By enhancing the sequential order information of interactions, TSAPE significantly improves the accuracy of next-item recommendations. We seamlessly integrated our method into several Transformer-based sequential recommendation models and conducted comparisons with state-of-the-art sequential recommendation approaches and widely-used PE methods. The results demonstrate that the integration of TSAPE consistently outperforms the original backbone models and other SOTA methods. The SASRec model integrated with TSAPE achieves an average improvement of 15.61% across three evaluation metrics on four benchmark datasets. Our code has been made publicly available at https://github.com/rongbo-qi/TSAPE_Rec.
In industrial recommender systems, conversion rate (CVR) is often used for traffic allocation, but fails to fully reflect recommendation effectiveness as it does not account for refund rate (RFR). Thus, net conversion rate (NetCVR), the probability that a clicked item is purchased and not refunded, is proposed to better show true user satisfaction and business value. Unlike CVR, NetCVR prediction involves a more complex multi-stage cascaded delay feedback phenomenon. The two cascaded delays Click->Conversion and Conversion->Refund in NetCVR have opposite effects. Therefore, traditional CVR methods cannot be directly applied. At present, the lack of relevant open-source datasets and online continuous training schemes poses a challenge. To address these, we first introduce CAscadal Sequences of Conversion And Delayed rEfund (CASCADE), the first large-scale open dataset derived from Taobao app for online continuous NetCVR prediction. We further analyze CASCADE and derive three key insights: (1) NetCVR exhibits clear temporal patterns necessitating online continuous modeling; (2) Cascaded modeling CVR and RFR for NetCVR outperforms directly modeling NetCVR; and (3) delay time, which correlated with both CVR and RFR, is an important feature for NetCVR prediction. Based on these insights, we propose neT convErsion caScaded modeLing and debiAsing method (TESLA). This continuous method features a CVR-RFR cascaded architecture, stage-wise debiasing, and a delay-time-aware ranking loss for efficient NetCVR prediction. Experiments show that TESLA outperforms state-of-the-art methods on CASCADE, achieving an absolute improvement of 12.41% in RI-AUC and 14.94% in RI-PRAUC on NetCVR over the strongest baseline. We hope this work provides a new direction for online delayed feedback modeling in NetCVR prediction. Our code and dataset are available at https://github.com/alimama-tech/NetCVR.
Large language models (LLMs) have emerged as a promising paradigm for recommender systems, due to their powerful capabilities in global knowledge integration and reasoning. However, LLMs are inherently prone to confirmation bias -- the tendency to favor information that reinforces users' existing views -- which leads to an overemphasis on previously shown viewpoints and ignores diverse user beliefs for recommendations. To address this issue, in this paper, we propose SCoTRec, a social chain-of-thought reasoning framework for recommendation. SCoTRec first constructs sentiment-aware user profiles by extracting sentiment terms from user reviews. It then incorporates users' social sentiment information into the social chain-of-thought reasoning units to improve recommendations. In particular, we categorize the social chain-of-thought into sentiment-based pathways and apply human evaluation operations -- backtracking, discarding, retaining, and aggregating -- to simulate nuanced sentiment cognition and interpersonal influence, effectively alleviating confirmation bias. Extensive experiments on four benchmark datasets demonstrate the effectiveness of SCoTRec in alleviating confirmation bias and improving recommendations.
Recent agent-based recommendation frameworks aim to simulate user behaviors by incorporating memory mechanisms and prompting strategies, but they struggle with hallucinating non-existent items and full-catalog ranking. Besides, a largely underexplored opportunity lies in leveraging LLMs' commonsense reasoning to capture user intent through substitute and complement relationships between items, which are usually implicit in datasets and difficult for traditional ID-based recommenders to capture. In this work, we propose a novel LLM-agent framework, AgentDR, which bridges LLM reasoning with scalable recommendation tools. Our approach delegates full-ranking tasks to traditional models while utilizing LLMs to (i) integrate multiple recommendation outputs based on personalized tool suitability and (ii) reason over substitute and complement relationships grounded in user history. This design mitigates hallucination, scales to large catalogs, and enhances recommendation relevance through relational reasoning. Through extensive experiments on three public grocery datasets, we show that our framework achieves superior full-ranking performance, yielding on average a twofold improvement over its underlying tools. We also introduce a new LLM-based evaluation metric that jointly measures semantic alignment and ranking correctness.
Multi-modal recommendation has become essential with the rapid expansion of online platforms such as e-commerce and video-sharing applications. In this work, we focus on the Multi-Modal New Item Recommendation (MMNIR) problem, where items with multi-modal content but newly introduced items lack interaction history. The MMNIR problem is particularly challenging in two aspects: (1) a large number of new items are created rapidly over time without any interaction data, (2) not all existing interactions are equally useful, and it is non-trivial to identify informative behaviors from users with similar preferences. However, previous methods fail to identify users with similar preferences and to capture informative behaviors from historical data. Furthermore, conventional models primarily rely on simple co-occurring signals, leading to spurious neighbors and neglecting the informative behaviors of truly similar users with consistent preferences. To fill this gap, we propose Joint Similar User Exploration and Informative Behavior Guidance (SuperG) for solving the MMNIR problem. SuperG first proposes a similar user exploration module to identify users with similar preferences to the target user. Then it incorporates an informative behavior mining module to retrieve informative behaviors from both the target user and similar users' histories to support new item recommendation. Finally, SuperG proposes a behavior-guided hybrid recommendation module to incorporate the retrieved behavioral signals to guide the recommendation of new items. Our empirical study on three real datasets demonstrates that SuperG outperforms the state-of-the-art models under the MMNIR setting.
Federated cross-domain recommendation (Federated CDR) aims to collaboratively learn personalized recommendation models across heterogeneous domains while preserving data privacy. Recently, large language model (LLM)-based recommendation models have demonstrated impressive performance by leveraging LLMs' strong reasoning capabilities and broad knowledge. However, adopting LLM-based recommendation models in Federated CDR scenarios introduces new challenges. First, there exists a risk of overfitting with domain-specific local adapters. The magnitudes of locally optimized parameter updates often vary across domains, causing biased aggregation and overfitting toward domain-specific distributions. Second, unlike traditional recommendation models (e.g., collaborative filtering, bipartite graph-based methods) that learn explicit and comparable user/item representations, LLMs encode knowledge implicitly through autoregressive text generation training. This poses additional challenges for effectively measuring the cross-domain similarities under heterogeneity. To address these challenges, we propose an LLM-based framework for federated cross-domain recommendation, FeDecider. Specifically, FeDecider tackles the challenge of scale-specific noise by disentangling each client's low-rank updates and sharing only their directional components. To handle the need for flexible and effective integration, each client further learns personalized weights that achieve the data-aware integration of updates from other domains. Extensive experiments across diverse datasets validate the effectiveness of our proposed FeDecider.
The persistent challenges of data sparsity and class imbalance have long limited the development of recommender systems. Fortunately, line graph theory offers a novel perspective to overcome these issues. By transforming the user-item interaction bipartite graph into a line graph, the problems of data sparsity and class imbalance are elegantly reformulated as those of insufficient labeled nodes and imbalanced label distribution in the line graph domain. This reformulation allows us to directly apply mature techniques from node classification and imbalanced graph learning to address these core challenges. Inspired by this insight, we propose a Line Graph Data Augmentation (LGDA) strategy, which features two distinct characteristics. Firstly, it is a plug-and-play module that resolves data sparsity and imbalance without modifying the underlying recommendation framework. Secondly, it employs a targeted augmentation and confidence filtering mechanism to generate high-quality, balanced augmented data. Extensive experiments on four real-world datasets validate that LGDA effectively alleviates data sparsity and class imbalance, leading to significant improvements in both recommendation performance and system robustness.
Watch time prediction (WTP) has emerged as a pivotal task in short video recommendation systems, designed to quantify user engagement through continuous interaction modeling. Predicting users' watch times on videos often encounters fundamental challenges, including wide value ranges and imbalanced data distributions, which can lead to significant estimation bias when directly applying regression techniques. Recent studies have attempted to address these issues by converting the continuous watch time estimation into an ordinal regression task. While these methods demonstrate partial effectiveness, they exhibit notable limitations: (1) The discretization process frequently relies on bucket partitioning, inherently reducing prediction flexibility and accuracy. (2) The interdependencies among different partition intervals remain underutilized, missing opportunities for effective error correction. Inspired by language modeling paradigms, we propose a novel Generative Regression (GR) framework that reformulates WTP as a sequence generation task. Our approach employs structural discretization to enable nearly lossless value reconstruction while maintaining prediction flexibility. Through carefully designed vocabulary construction and label encoding schemes, each watch time is bijectively mapped to a token sequence. To mitigate the training-inference discrepancy caused by teacher-forcing, we introduce a curriculum learning with embedding mixup strategy that gradually transitions from guided to free-generation modes. We test our models extensively on two public datasets, a large-scale offline industrial dataset, and an online A/B test on Kuaishou App with over 400 million daily active users (DAU) and GR consistently outperforms existing state-of-the-art approaches significantly. Our code is available at https://github.com/snailma0229/GR.git.
From Prediction to Understanding: Leveraging Reasoning in Large Language Model-based Recommendations
Recently, large language models (LLMs) have shown great promise in sequential recommendation. Existing methods typically transform users' historical interactions into textual sequences and then feed these sequences into LLMs to generate recommended items. However, since the output of LLMs is limited to recommendations, the models must analyze user interactions and infer user preferences implicitly. This implicit approach constrains the expressive reasoning abilities of LLMs. Moreover, as the output of LLMs consists solely of recommended items, the resulting recommendations lack explainability. To address these issues, we propose RE2, which enables LLMs to explicitly generate reasoning content before providing recommendations. Making the reasoning process explicit helps elicit the reasoning abilities of LLMs and simultaneously enhances the explainability of recommendation results. RE2 consists of three steps: (1) Reasoning Collection, which collects reasoning data by guiding LLMs to generate both reasoning content and recommended items for each interaction sequence. (2) Pattern Imitation, which leverages the collected data to train LLMs via supervised fine-tuning to imitate the pattern of first generating reasoning content and then providing recommendations. (3) Pattern Internalization, which further internalizes this reasoning-and-recommendation pattern and enhances both recommendation performance and the rationality of the reasoning content through reinforcement learning. RE2 can be implemented under both the self-distillation and teacher-distillation frameworks, without requiring external user metadata such as user reviews. Experimental results demonstrate the effectiveness of RE2 in improving recommendation performance and in generating high-quality reasoning content. Furthermore, we show that RE2 can mitigate popularity bias while maintaining recommendation accuracy to some extent. Our data and code are available at https://github.com/zhiyuanc2001/RE2.
Users increasingly interact with content across multiple domains, resulting in sequential behaviors marked by frequent and complex transitions. While Cross-Domain Sequential Recommendation (CDSR) models two-domain interactions, Multi-Domain Sequential Recommendation (MDSR) introduces significantly more domain transitions, compounded by challenges such as domain heterogeneity and imbalance. Existing approaches often overlook the intricacies of domain transitions, tend to overfit to dense domains while underfitting sparse ones, and struggle to scale effectively as the number of domains increases. We propose GMFlowRec, an efficient generative framework for MDSR that models domain-aware transition trajectories via Gaussian Mixture Flow Matching. GMFlowRec integrates: (1) a unified dual-masked Transformer to disentangle domain-invariant and domain-specific intents, (2) a Gaussian Mixture flow field to capture diverse behavioral patterns, and (3) a domain-aligned prior to support frequent and sparse transitions. Extensive experiments on JD and Amazon datasets show GMFlowRec achieves up to 44% NDCG@5 improvement over state-of-the-art baselines while maintaining efficiency with a single unified backbone. Our code and data are available here.
YouTube is the world's most widely used video platform, with over 70% of content viewed through algorithmic recommendations. While prior audits have examined polarization in YouTube's long-form video recommendations, the platform's fast-growing Shorts feature remains understudied. In this paper, we present the first large-scale audit comparing political content exposure and engagement dynamics across short-form and long-form videos on YouTube. We design a matched audit based on the insight that many news media organizations publish both short and long versions of the same content and collect 50,000 pairs of long-form and short-form video recommendations from both political and nonpolitcal seed videos. We analyze recommendations along several dimensions: the frequency of political recommendations, the diversity of retrieved videos, the engagement those videos receive, and finally, the partisan alignment between recommended videos and seed videos. Our results highlight fundamental differences between each algorithm, which we hope we can inform future research in analyzing the impact of YouTube recommendations.
Federated recommendation systems (FRSs) have recently gained widespread attention due to their ability to train collaborative recommendation models without exchanging raw user data. However, existing FRSs face a severe challenge of data sparsity, which manifests at both the user and item levels. First, user data sparsity: some users may only have a small number of interactions with items, struggling to adequately train the personalized user embedding locally. Second, item data sparsity: some items may only receive a small number of user ratings, causing the global model to lack knowledge about them. Considering these, we propose the Knowledge Enhanced Federated Recommendation System named as KE-FedRS, of which the core idea is to enhance the knowledge of users with few interactions and items with few ratings at both the local and global levels. Specifically, at the local level, we introduce an auxiliary user embedding and average and aggregate this auxiliary embedding across similar users, thereby enriching the knowledge of the local user embedding. At the global level, we propose a hybrid client selection strategy based on item embedding discrepancies, prioritizing clients that exhibit greater divergence in item embeddings from others, thus enhancing the knowledge of items with fewer interactions in the global model. We conduct comprehensive experiments on four real-world datasets, and the results show that the proposed method consistently outperforms baseline approaches in terms of HR@10 and NDCG@10.
Existing LLM-driven recommendation systems (RS) suffer from over-reliance on a single pre-trained model, which limits adaptability across diverse scenarios due to differences in large language models' strengths in semantics, knowledge, and reasoning. To address this issue, we propose AMLrec (Adaptive Multi-LLM Recommendation), a dynamic routing-based adaptive multi-LLM collaboration framework that unifies two dominant paradigms—LLM as Recommender and LLM + Recommender—through decision knowledge complementation. For each user or item, a lightweight encoder generates embeddings that are compared with learnable LLM prototypes using cosine similarity to select the most suitable models. In the first paradigm, selected LLMs generate recommendations via structured prompts, and their outputs are aggregated to form the final recommendation list. In the second paradigm, chosen LLMs produce semantic embeddings, which are fused with learnable embeddings after PCA-based dimensionality reduction and aligned using a lightweight adapter to bridge distribution gaps. Notably, AMLrec does not require fine-tuning of the underlying LLMs, significantly reducing computational overhead. Experiments on real-world datasets demonstrate that the proposed approach consistently outperforms single-LLM baselines across all evaluation metrics, validating its effectiveness. The main contributions of this work are threefold: introducing dynamic routing for multi-LLM recommendation system collaboration, proposing a unified architecture that harmonizes both paradigms, and enabling efficient adaptation without LLM fine-tuning. The code is available at https://github.com/Jiale-12138/AMLrec.
Customer Lifetime Value (LTV) prediction, a central problem in modern marketing, is characterized by a unique zero-inflated and long-tail data distribution. This distribution presents two fundamental challenges: (1) the vast majority of low-to-medium value users numerically overwhelm the small but critically important segment of high-value ''whale'' users, and (2) significant value heterogeneity exists even within the low-to-medium value user base. Common approaches either rely on rigid statistical assumptions or attempt to decouple ranking and regression using ordered buckets; however, they often enforce ordinality through loss-based constraints rather than inherent architectural design, failing to balance global accuracy with high-value precision. To address this gap, we propose Conditional Cascaded Ordinal-Residual Networks (CC-OR-Net), a novel unified framework that achieves a more robust decoupling through structural decomposition, where ranking is architecturally guaranteed. CC-OR-Net integrates three specialized components: a structural ordinal decomposition module for robust ranking, an intra-bucket residual module for fine-grained regression, and a targeted high-value augmentation module for precision on top-tier users. Evaluated on real-world datasets with over 300M users, CC-OR-Net achieves a superior trade-off across all key business metrics, outperforming state-of-the-art methods in creating a holistic and commercially valuable LTV prediction solution.
Search and Recommendation (S&R) are core information access channels on modern multi-scenario platforms. Existing joint S&R models face two critical challenges: (1) cross-scenario interest inconsistency, failing to unify explicit search intent (queries) and implicit recommendation intent (behavioral interactions) into coherent user interest representations; (2) severe S&R trade-off, where enhancing one task degrades the other due to static knowledge sharing and unbalanced feature utilization. To address these issues, we propose MinSAR , a novel framework focusing on cross-S&R user interest consistency. It integrates two key innovations: a Unified Interest Generation (UIG) module using Vector Quantized-Variational Autoencoder (VQ-VAE) to fuse long-term user preferences (via a user-specific memory network) and dynamic short-term contextual behaviors, generating compact cross-scenario latent representations that bridge explicit and implicit intents. Additionally, an Interest-Guided Attention Expert Network replaces static multi-task gating with intent-aware weight allocation. Guided by UIG's unified interest, it dynamically balances cross-S&R shared knowledge and task-specific expertise (semantic matching for search, collaborative filtering for recommendation), mitigating inter-task conflicts. Extensive experiments on two real-world datasets (KuaiSAR and Amazon Kindle Store) against 13 baselines show MinSAR outperforms state-of-the-art joint S&R models. Further analysis confirms its ability to eliminate the S&R performance trade-off.