Logical reasoning-based recommendation methods formulate logical expressions to characterize user-item interaction patterns, incorporating regularization constraints to ensure consistency with logical rules. However, these methods face two critical challenges: (1) As sequence length increases, they cannot effectively capture the dynamic transfer of user interests across subsequences (i.e., subsequence interest drift), thereby degenerating logical expressions to single-subsequence inference. (2) The time complexity of logical reasoning and rule learning scales quadratically with the sequence length, severely constraining computational efficiency in long-sequence recommendation. To address these challenges, we propose ELECTOR, an intErest-shift-aware long-sequence Logical reasoning for EffiCienT lOng-sequence Recommendation method. Specifically, we design a Subsequence Interest Learning Module (SIL) to model cross-subsequence interest drifts in long sequences. SIL employs a local attention mechanism to extract subsequence interests effectively and a global attention mechanism to capture the correlations among subsequence interests. Subsequently, we propose an Interest-aware Logical Reasoning (ILR) mechanism that performs logical reasoning using a limited set of subsequence and short-term interests, rather than reasoning over the entire sequence, significantly reducing time complexity. Additionally, ILR employs interest logical reasoning contrastive loss to ensure the model simultaneously considers multiple interests. Experiments on four real-world datasets demonstrate that our method significantly outperforms all baselines regarding computational efficiency and recommendation accuracy, confirming its effectiveness.

Lifelong sequential recommendation aims to model users' long-term interests by leveraging their entire interaction history, but the high computational overhead caused by ultra-long sequences poses a core challenge. To address this, existing methods generally adopt the subsequence learning strategies to shorten the input sequence, which can be divided into two categories: (1) Sequence compression methods compress long sequences into multiple subsequence representations through strategies such as uniform segmentation and clustering; (2) Top-k retrieval methods filter the subsequence related to the target item from long sequences via target attention mechanisms or retrieval mechanisms and learn its representation. However, these two types of methods still face challenges in subsequence representation learning: (1) Sequence compression methods struggle to simultaneously balance the high similarity of items within subsequences and smooth temporal continuity (i.e., small temporal intervals between adjacent items), resulting in incorrect learning of subsequence representations; (2) Top-k retrieval methods lose a large amount of effective context information when the length of the retrieved subsequence is much smaller than the original sequence, resulting in incomplete sequence representations. To overcome these challenges, we propose a novel lifelong sequential recommendation method with adaptive subsequence compression and contextual fusion. Specifically, an adaptive subsequence compression module is first designed: it utilizes gradient policy sampling to achieve adaptive segmentation of subsequences, thereby retaining their temporal continuity, and introduces a reward function to enhance the similarity of items within subsequences. Second, a subsequence context fusion module is constructed: it leverages causal cross-attention to fuse recent interactions with their subsequence context and to capture correlations among recent items, thereby learning more complete and accurate sequence representations. We conduct extensive experiments on three public long-sequence recommendation datasets. Experimental results demonstrate that our proposed method consistently outperforms a variety of strong baselines in both predictive accuracy and computational efficiency.

Data augmentation is an effective technique for tackling data sparsity in sequential recommendation (SR). Existing methods generate new data during the model training to improve the performance. However, deploying them on a backbone model requires retraining, architecture modification, or introducing additional modules and learnable parameters. These processes are time-consuming and costly for well-trained models, especially when the model and data scales become large. In this work, we explore the test-time augmentation (TTA) for SR, which augments the input sequences during the inference phase and then fuses the model's predictions to improve final accuracy. It avoids the significant overhead associated with training-time augmentation. We first experimentally examine the potential of existing augmentation operators for TTA and find that the Substitute and Mask consistently achieve better performance. Further analysis reveals that these two operators retain the original sequential pattern while adding appropriate perturbations. Moreover, the random selection of augmentation positions creates suitable augmented samples from both semantic and temporal perspectives. Meanwhile, we find that the fixed operation ratio limits the diversity of augmented data, and the TTA may impair the model's performance on long sequences. In addition, the two operators still face time-consuming similarity-based item selection or interference from mask tokens. Based on the analysis and limitations, we present TNoise and TMask. The former injects uniform noise into the representation, avoiding the computational overhead of item selection. The latter blocks mask tokens from participating in model calculations (TMask-B) or directly removes interactions that should have been replaced with mask tokens (TMask-R). Further, we sample the augmentation ratio from a uniform distribution to improve the data diversity. For short sequences, we introduce a sequence smoothing and lengthening method based on inter-item interpolation. For long sequences, we set a threshold to avoid the negative effects of TTA. Comprehensive experiments demonstrate the effectiveness, efficiency, and generalizability of our method.

Sequential recommendation (SR) has received much attention and made promising progress in the past few years due to its high alignment with real recommendation scenarios. It models users' preferences and behavior patterns from their historical behavior sequences and provides personalized recommendations. However, the widespread problem of data sparsity limits the performance of sequential recommendation models. To tackle this, data augmentation (DA) provides a feasible solution by improving the quantity, quality, or diversity of the training samples without the need for additional data collection. In this survey, we present a systematic and timely review of research efforts on data augmentation for sequential recommendation. We start by providing a clear formulation of the problem and task. Then, we develop a unified taxonomy that categorizes existing augmentation methodologies regarding their augmentation objects and principles. Next, we conduct a comparative discussion on the advantages and disadvantages of different categories, supplemented with quantitative performance evaluations, time-complexity analyses, and visual case studies of representative methods, aiming to provide actionable guidance for the selection and development of augmentation methods in real-world scenarios. Finally, we present the future research directions and summarize this survey.

By generating new yet effective data, data augmentation has become a promising method to mitigate the data sparsity problem in sequential recommendation. Existing works focus on augmenting the original data but rarely explore the issue of imbalanced relevance and diversity for augmented data, leading to semantic drift problems or limited performance improvements. In this paper, we propose a novel Balanced data Augmentation Plugin for Sequential Recommendation (BASRec) to generate data that balance relevance and diversity. BASRec consists of two modules: Single-sequence Augmentation and Cross-sequence Augmentation. The former leverages the randomness of the heuristic operators to generate diverse sequences for a single user, after which the diverse and the original sequences are fused at the representation level to obtain relevance. Further, we devise a reweighting strategy to enable the model to learn the preferences based on the two properties adaptively. The Cross-sequence Augmentation performs nonlinear mixing between different sequence representations from two directions. It produces virtual sequence representations that are diverse enough but retain the vital semantics of the original sequences. These two modules enhance the model to discover fine-grained preferences knowledge from single-user and cross-user perspectives. Extensive experiments verify the effectiveness of BASRec. The average improvement is up to 72.0% on GRU4Rec, 33.8% on SASRec, and 68.5% on FMLP-Rec. We demonstrate that BASRec generates data with a better balance between relevance and diversity than existing methods.

Multi-behavior recommendation exploits auxiliary behaviors (e.g., view, cart) to help predict users' potential target behavior (e.g., purchase) on a given item. However, existing works suffer from two issues: (1) They generally consider only a single chain from auxiliary behaviors to the target behavior, referred to as a purchase chain (e.g., view -> cart -> purchase), ignoring other valuable purchase chains (e.g., view ->purchase) that are beneficial for recommendation performance. (2) Most studies presume that interacted items in auxiliary behaviors are good for recommendations, and pay little attention to the negative transfer problem. That is, some auxiliary behaviors may negatively transfer the influence to the modeling of target ones (e.g., items viewed but not purchased). To alleviate these issues, we propose a novel Multiple Purchase Chains (MPC) model with negative transfer elimination for multi-behavior recommendation. Specifically, we construct multiple purchase chains from auxiliary to target behaviors according to users' historical interactions, while the representations of a previous behavior will be fed to initialize the next behavior on the chain. Then, we construct a negative graph for the latter behavior and learn the negative representations of users and items which will be filtered out to eliminate negative transfer. Experimental results on two real datasets outperform the best baseline by 40.97% and 47.26% on average in terms of Recall@10 and NDCG@10 respectively, demonstrating the effectiveness of our method.

Prompt tuning is a promising method to fine-tune a pre-trained language model without retraining its large-scale parameters. Instead, it attaches a soft prompt to the input text, whereby downstream tasks can be well adapted by merely learning the embeddings of prompt tokens. Nevertheless, existing methods still suffer from two challenges: (i) they are hard to balance accuracy and efficiency. A longer (shorter) soft prompt generally leads to a better (worse) accuracy but at the cost of more (less) training time. (ii) The performance may not be consistent when adapting to different downstream tasks. We attribute it to the same embedding space but responsible for different requirements of downstream tasks. To address these issues, we propose an Efficient Prompt Tuning method (EPT) by multi-space projection and prompt fusion. Specifically, it decomposes a given soft prompt into a shorter prompt and two low-rank matrices, significantly reducing the training time. Accuracy is also enhanced by leveraging low-rank matrices and the short prompt as additional knowledge sources to enrich the semantics of the original short prompt. In addition, we project the soft prompt into multiple subspaces to improve the performance consistency, and then adaptively learn the combination weights of different spaces through a gating network. Experiments on 13 natural language processing downstream tasks show that our method significantly and consistently outperforms 11 comparison methods with the relative percentage of improvements up to 12.9%, and training time decreased by 14%.

Involving collaborative information in Large Language Models (LLMs) is a promising technique for adapting LLMs for recommendation. Existing methods achieve this by concatenating collaborative features with text tokens into a unified sequence input and then fine-tuning to align these features with LLM's input space. Although effective, in this work, we identify two limitations when adapting LLMs to recommendation tasks, which hinder the integration of general knowledge and collaborative information, resulting in sub-optimal recommendation performance. (1) Fine-tuning LLM with recommendation data can undermine its inherent world knowledge and fundamental competencies, which are crucial for interpreting and inferring recommendation text. (2) Incorporating collaborative features into textual prompts disrupts the semantics of the original prompts, preventing LLM from generating appropriate outputs. In this paper, we propose a new paradigm, Collaborative LoRA (CoRA), with a collaborative query generator. Rather than input space alignment, this method aligns collaborative information with LLM's parameter space, representing them as incremental weights to update LLM's output. This way, LLM perceives collaborative information without altering its general knowledge and text inference capabilities. Specifically, we employ a collaborative filtering model to extract user and item embeddings and inject them into a set number of learnable queries. We then convert collaborative queries into collaborative weights with low-rank properties and merge the collaborative weights into LLM's weights, enabling LLM to perceive the collaborative signals and generate personalized recommendations without fine-tuning or extra collaborative tokens in prompts. Extensive experiments confirm that CoRA effectively integrates collaborative information into LLM, enhancing recommendation performance.

Haoyu Xu, Pengxiang Lan, Enneng Yang, Guibing Guo, Jianzhe Zhao, Linying Jiang, Xingwei Wang. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.

Pengxiang Lan, Haoyu Xu, Enneng Yang, Yuliang Liang, Guibing Guo, Jianzhe Zhao, Xingwei Wang. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Data augmentation has become a promising method of mitigating data sparsity in sequential recommendation.Existing methods generate new yet effective data during model training to improve performance.However, deploying them requires retraining, architecture modification, or introducing additional learnable parameters.These steps are time-consuming and costly for well-trained models, especially when the model scale becomes large.In this work, we explore the test-time augmentation (TTA) for sequential recommendation, which augments the inputs during the model inference and then aggregates the model's predictions for augmented data to improve final accuracy.It avoids significant time and cost overhead from the previously mentioned steps.We first experimentally disclose the potential of existing augmentation operators for TTA and find that the Mask and Substitute consistently achieve better performance.Further analysis reveals that these two operators are effective because they retain the original sequential pattern while adding appropriate perturbations.Meanwhile, we argue that these two operators still face time-consuming item selection or interference information from mask tokens.Based on the analysis and limitations, we present TNoise and TMask.The former injects uniform noise into the original representation, avoiding the computational overhead of item selection.The latter blocks

Logical reasoning-based recommendation methods employ logical rules to mitigate the adverse effects of noise items in short interaction sequences on recommendation accuracy.However, there are two problems with existing methods: 1) As the length of the interaction sequence increases, introducing more noise items exacerbates the negative impact on logical reasoning, thereby reducing the accuracy of these methods.2) They are often dominated by the user's single primary interest, which prevents simultaneous consideration of users' multiple-aspect interests in long sequences.To address these issues, we propose a novel dEnoising Multi-Interestaware Logical rEasoning (EMILE) method for long-sequence recommendation.Specifically, we design a logical rule-based interest extractor that enhances the importance of preferred items in constructing user interests while minimizing the negative impact of disliked items.This extractor effectively mitigates the adverse effects of noise items in long interaction sequences.Furthermore, we propose a novel multi-interest learning strategy that optimizes two new objective functions-interest probability distribution contrastive loss and interest logical reasoning contrastive loss-to ensure the model simultaneously considers multiple-aspect interests.These two objective functions require that the target item is more * Corresponding authors.

Graph Neural Networks (GNNs)-based recommendation algorithms typically assume that training and testing data are drawn from independent and identically distributed (IID) spaces. However, this assumption often fails in the presence of out-of-distribution (OOD) data, resulting in significant performance degradation. In this study, we construct a Structural Causal Model (SCM) to analyze interaction data, revealing that environmental confounders (e.g., the COVID-19 pandemic) lead to unstable correlations in GNN-based models, thus impairing their generalization to OOD data. To address this issue, we propose a novel approach, graph representation learning via causal diffusion (CausalDiffRec) for OOD recommendation. This method enhances the model's generalization on OOD data by eliminating environmental confounding factors and learning invariant graph representations. Specifically, we use backdoor adjustment and variational inference to infer the real environmental distribution, thereby eliminating the impact of environmental confounders. This inferred distribution is then used as prior knowledge to guide the representation learning in the reverse phase of the diffusion process to learn the invariant representation. In addition,we provide a theoretical derivation that proves optimizing the objective function of CausalDiffRec can encourage the model to learn environment-invariant graph representations, thereby achieving excellent generalization performance in recommendations under distribution shifts. Our extensive experiments validate the effectiveness of CausalDiffRec in improving the generalization of OOD data, and the average improvement is up to 10.69% on Food, 18.83% on KuaiRec, 22.41% on Yelp2018, and 11.65% on Douban datasets.

The distributionally robust optimization (DRO)-based graph neural network methods improve recommendation systems' out-of-distribution (OOD) generalization by optimizing the model's worst-case performance. However, these studies fail to consider the impact of noisy samples in the training data, which results in diminished generalization capabilities and lower accuracy. Through experimental and theoretical analysis, this paper reveals that current DRO-based graph recommendation methods assign greater weight to noise distribution, leading to model parameter learning being dominated by it. When the model overly focuses on fitting noise samples in the training data, it may learn irrelevant or meaningless features that cannot be generalized to OOD data. To address this challenge, we design a Distributionally Robust Graph model for OOD recommendation (DRGO). Specifically, our method first employs a simple and effective diffusion paradigm to alleviate the noisy effect in the latent space. Additionally, an entropy regularization term is introduced in the DRO objective function to avoid extreme sample weights in the worst-case distribution. Finally, we provide a theoretical proof of the generalization error bound of DRGO as well as a theoretical analysis of how our approach mitigates noisy sample effects, which helps to better understand the proposed framework from a theoretical perspective. We conduct extensive experiments on four datasets to evaluate the effectiveness of our framework against three typical distribution shifts, and the results demonstrate its superiority in both independently and identically distributed distributions (IID) and OOD.

Recommendation unlearning is an emerging task to erase the influences of user-specified data from a trained recommendation model. Most existing research follows the paradigm of partitioning the original dataset into multi-fold and then retraining corresponding sub-models while those influences are totally removed. Despite the effectiveness, two key problems remain unexplored: (i) Existing work becomes inefficient and computationally expensive to retrain all sub-models, especially when facing large amounts of unlearning data. (ii) User preferences are dynamically changing. If users express negative opinions on some interacted items they used to prefer, how can we adaptively erase the outdated preferences behind such transformation from the trained model? Although these unlearning data contain outdated information, there is still a lot of helpful knowledge worth preserving. Existing methods ignore this preservation during unlearning and may remove all the knowledge in the interactions, compromising the final performance. In light of these limitations, we propose a novel unlearning framework called GFEraser, which transforms the unlearning into an efficient guided filtering process to avoid time-consuming retraining and retain beneficial knowledge. Specifically, we develop an intra-user negative sampling strategy to learn the outdated preferences that need to be erased. Under the guidance of differential maximization agreement and attention-based fusion module, the original representations are adaptively filtered and aggregated based on the learned preferences. Besides, we leverage contrastive learning to preserve the invariant user preferences, maintaining the final performance. Finally, we devise a new metric called Ranking Decrease Rate to evaluate the unlearning effect. Experimental results demonstrate that GFEraser can maintain reliable recommendation performance while achieving efficient outdated preferences unlearning, up to 37 \(\times\) acceleration.

Preference logical reasoning utilizes user-item interactions (e.g., ratings and reviews) to infer user preferences and discover user decision paths from the knowledge graph to enhance the explainability of item recommendations. However, existing algorithms assume that the ratings and reviews of any item are always consistent, ignoring situations where items with high ratings have negative reviews or items with low ratings but positive reviews. This leads to inaccurate learning of user preferences. In fact, through experimental analysis of two real datasets, we found that on average, about 10% of the interactive data exhibited this inconsistency, that is, items with high ratings but negative reviews appear in the recommendation list. To address this issue, we propose a general preference logical reasoning method based on preference operators. Specifically, we capture the semantic information of users toward the item (its corresponding attributes) in reviews and define two preference operators ( like and dislike ) for the item to correct ambiguous neutral ratings or false ratings that do not reflect true preferences. In the process of preference path reasoning, the like preference operator increases the occurrence probability of liked items, while the dislike preference operator reduces the occurrence probability of disliked items. By fusing the preference operators in the preference path, we obtain consistent user preferences and enhance the explainability of item recommendations. The experimental results on four real datasets demonstrate that our method can effectively improve the performance of all comparison baselines in terms of recommendation accuracy and user decision explainability.

Graph Contrastive Learning (GCL) leverages data augmentation techniques to produce contrasting views, enhancing the accuracy of recommendation systems through learning the consistency between contrastive views. However, existing augmentation methods, such as directly perturbing interaction graph (e.g., node/edge dropout), may interfere with the original connections and generate poor contrasting views, resulting in sub-optimal performance. In this article, we define the views that share only a small amount of information with the original graph due to poor data augmentation as noisy views (i.e., the last 20% of the views with a cosine similarity value less than 0.1 to the original view). We demonstrate through detailed experiments that noisy views will significantly degrade recommendation performance. Further, we propose a model-agnostic Symmetric Graph Contrastive Learning (SGCL) method with theoretical guarantees to address this issue. Specifically, we introduce symmetry theory into graph contrastive learning, based on which we propose a symmetric form and contrast loss resistant to noisy interference. We provide theoretical proof that our proposed SGCL method has a high tolerance to noisy views. Further demonstration is given by conducting extensive experiments on three real-world datasets. The experimental results demonstrate that our approach substantially increases recommendation accuracy, with relative improvements reaching as high as 12.25% over nine other competing models. These results highlight the efficacy of our method. The code is available at https://github.com/user683/SGCL .

Graph Neural Networks (GNNs) have attracted much attention due to their outstanding performance in fraud detection tasks, which reveal fraudsters by aggregating neighborhood features. However, most GNNs assume that neighborhood features are homogeneous, which does not match the fundamentally heterogeneous fraud graph, while heterogeneous information is equally important for fraud detection. In addition, the label-imbalanced neighborhood also deteriorates fraud detection accuracy. To address the above issues, we propose an innovative spectral and spatial dual graph neural network for fraud detection, namely SSD-GNN. In SSD-GNN, we design a dual-pathway framework, including spectral and spatial processing paths, and aggregate neighborhood features according to the homogeneity and heterogeneity assumptions, respectively. In the spectral processing path, we design high-pass filtering and low-pass filtering to form a mixed-pass filter to extract low-frequency information of homogeneity and high-frequency information of heterogeneity. In the spatial processing path, we first enhance the connection between the target node and the global node by clustering information to alleviate the label imbalance problem. Then we design similarity aggregation and disparity aggregation to capture homogeneity and heterogeneity information, respectively. We run experiments on two real-world fraud detection datasets, using AUC and F1-macro as test metrics. The comprehensive experimental results show that the AUC and F1-macro on the average source exceed at least 5 percent. demonstrate the superiority of the proposed SSD-GNN.

Sequential recommendation aims to provide users with personalized suggestions based on their historical interactions. When training sequential models, padding is a widely adopted technique for two main reasons: 1) The vast majority of models can only handle fixed-length sequences; 2) Batch-based training needs to ensure that the sequences in each batch have the same length. The special value 0 is usually used as the padding content, which does not contain the actual information and is ignored in the model calculations. This common-sense padding strategy leads us to a problem that has never been explored in the recommendation field: Can we utilize this idle input space by padding other content to improve model performance and training efficiency further?