Frequency-domain sequential recommendation models enhance sequence representation capacity through spectral transformations. However, existing methods typically adopt coarse-grained spectrum reweighting strategies that strengthen high-frequency components while amplifying random noise within frequency bands. Moreover, they generally rely on a coupled modeling mechanism that handles both long-term and short-term preferences within a single backbone network, lacking dedicated modeling paths tailored to their distinct temporal characteristics. To address these challenges, we propose a Balanced Frequency Decoupling Sequential Recommendation model (BFDRec). Specifically, we design an energy-aware spectrum denoising mechanism to adaptively suppress low-energy noises according to the energy distribution within each frequency band while preserving salient behavioral fluctuation signals. Additionally, we construct a multi-scale decoupled architecture to model users' multi-scale preferences and adaptively integrate them through a dynamic gating mechanism, aligning the sequence modeling process with the distinct temporal characteristics of different frequency bands. Extensive experiments across five real-world datasets demonstrate that BFDRec effectively achieves noise suppression and accurate multi-scale preference modeling, with average improvements of 6.67% and 5.05% in HR and NDCG, respectively, over advanced baseline models. Our code is available at https://anonymous.4open.science/r/BFDRec.

The long-tail recommendation problem remains a significant challenge in modern recommender systems, primarily due to data sparsity and popularity bias, which hinder the accurate ID representation of users and items. Recent advancements in large language models (LLMs) have enabled the direct modeling of user and item semantic representations, offering potential improvements in representation learning through the alignment of these two types of representations. However, systems relying on LLM representation alignment face two critical challenges: (1) the substantial differences between LLMs and recommendation models in terms of training objectives, phases, and data; (2) the pervasive popularity bias in collaborative data. These challenges create a semantic gap between ID representations and semantic representations. Directly aligning these representations risks introducing recommendation-irrelevant noise, disrupting the collaborative information embedded in ID representations, and ultimately leading to suboptimal recommendation outcomes. To address this gap, we propose DeltaRec, aDouble-enhancement framework forlong-tailRecommendation. DeltaRec tackles the long-tail recommendation problem through two approaches. First, it incorporates semantic information for all items. Second, it provides additional supervision signals specifically for long-tail items. The framework begins by disentangling ID representations into interest representations and conformity representations. To integrate semantic information from LLMs while preserving popularity information, we design a contrastive learning-based semantic alignment module that aligns interest representations with semantic representations. Furthermore, to enhance the representation learning of unpopular items, we introduce a ranking-based behavior alignment module, which provides additional supervision signals for these items. To avoid introducing recommendation-irrelevant noise and disrupting collaborative semantics due to excessive alignment, we propose a curriculum learning-based training mechanism. Extensive experiments on real-world datasets demonstrate that DeltaRec effectively mitigates popularity bias and significantly improves long-tail recommendation performance without relying on prior knowledge of popularity distributions. Our code is available athttps://github.com/leo0481/DeltaRec/E3D7.

Multi-behavior recommendation leverages multiple user-item interaction information to alleviate data sparsity. Although different types of user-item interactions are temporally mutually exclusive, the sequence of behavioral interactions consisting of multi-level positive feedback signals contains rich information. However, most existing studies have unilaterally focused on the positive utility of auxiliary behaviors, ignoring multi-level user preference information. Effectively fusing multi-behavioral data and better modeling behavioral dependencies are urgent problems that need to be addressed for multi-behavior recommendation. We propose the p arallel learning of p ositive and n egative interests with an a uxiliary-view r epresentation e nhancement (PPN-ARE) scheme for multi-level user interest learning based on multi-behavioral interaction sequences. Specifically, multi-level positive and negative feedback view chains are constructed from multi-behavioral sequence data to learn multi-level user interests. User preference evolution is simulated during multi-behavior interactions using residual connections, and the shortcomings of the cascading structure used for higher-order graph learning are analytically highlighted. The influence of low-quality embeddings of auxiliary behaviors is filtered, and the learning of target behaviors is optimized by designing a representation enhancement layer. Finally, the model is optimized using a multi-task training framework. The experimental results indicate that PPN-ARE significantly improved over the state-of-the-art (SOTA). The open source code is available at https://github.com/lhybq/PPN-ARE .

Session-based recommendation (SBR) methods often rely on user behavior data, which can struggle with the sparsity of session data, limiting performance. Researchers have identified that beyond behavioral signals, rich semantic information in item descriptions is crucial for capturing hidden user intent. While Large Language Models (LLMs) offer new ways to leverage this semantic data, the challenges of session anonymity, short-sequence nature, and high LLM training costs have hindered the development of a lightweight, efficient LLM framework for SBR. To address the above challenges, we propose an LLM-enhanced SBR framework that integrates semantic and behavioral signals from multiple views. This two-stage framework leverages the strengths of both LLMs and traditional SBR models while minimizing training costs. In the first stage, we use multi-view prompts to infer latent user intentions at the session semantic level, supported by an intent localization module to alleviate LLM hallucinations. In the second stage, we align and unify these semantic inferences with behavioral representations, effectively merging insights from both large and small models. Extensive experiments on two real datasets demonstrate that the LLM4SBR framework can effectively improve model performance. We release our codes along with the baselines at https://github.com/tsinghua-fib-lab/LLM4SBR .

Next point-of-interest (POI) recommendation optimizes user travel experiences and enhances platform revenues by providing users with potentially appealing next location choices. In recent research, scholars have successfully mined users' general tastes and varying interests by modeling long-term and short-term check-in sequences. However, conventional methods for long and short-term modeling predominantly employ distinct encoders to process long and short-term interaction data independently, with disparities in encoders and data limiting the ultimate performance of these models. Instead, we propose a shared trajectory encoder and a novel Contrastive learning of Long and Short-term Preferences for next POI Recommendation (CLSPRec) model to better utilize the preference similarity among the same users and distinguish different users' travel preferences for more accurate next POI prediction. CLSPRec adopts a masking strategy in long-term sequences to enhance model robustness and further strengthens user representation through short-term sequences. Extensive experiments on three real-world datasets validate the superiority of our model. Our code is publicly available at https://github.com/Wonderdch/CLSPRec.

Session-based Recommendation (SBR) has recently received significant attention due to its ability to provide personalized recommendations based on the interaction sequences of anonymous session users. The challenges facing SBR consist mainly of how to utilize information other than the current session and how to reduce the negative impact of irrelevant information in the session data on the prediction. To address these challenges, we propose a novel graph attention network-based model called Multiple Sparse Graph Attention Networks (MSGAT). MSGAT leverages two parallel channels to model intra-session and inter-session information. In the intra-session channel, we utilize a gated graph neural network to perform initial encoding, followed by a self-attention mechanism to generate the target representation. The global representation is then noise-reduced based on the target representation. Additionally, the target representation is used as a medium to connect the two channels. In the inter-session channel, the noise-reduced relation representation is generated using the global attention mechanism of target perception. Moreover, MSGAT fully considers session similarity from the intent perspective by integrating valid information from both channels. Finally, the intent neighbor collaboration module effectively combines relevant information to enhance the current session representation. Extensive experiments on five datasets demonstrate that simultaneous modeling of intra-session and inter-session data can effectively enhance the performance of the SBR model.

In decision-making systems, how to address uncertainty plays an important role for the improvement of system performance in uncertainty reasoning. Dempster—Shafer evidence (DSE) theory is an effective method to address uncertainty in decision-making problems by means of basic belief assignments (BBAs) and Dempster's combination rule. In the DSE theory, divergence measure between BBAs, which is beneficial for conflict information management in decision making, remains an open issue. In this paper, several generalized evidential divergences (EDs) are proposed and studied to measure the difference and discrepancy between BBAs in DSE theory, which have more universal applicability in decision theory. On this basis, a uniform BJS divergence-based decision-making algorithm is devised to improve the decision level. Furthermore, the extensions of weighted BJS to decision-making algorithms are discussed by considering not only subjective weights but also objective weights. Notably, this is the first work to propose the weighted BJS divergence in DSE theory providing a promising way to analyze decision-making problems from different perspectives. Finally, the proposed BJS-based decision-making algorithm is applied to pattern classification. The results validate that the proposed decision-making algorithm is beneficial for diverse real-world datasets and outperforms several well-known related works and demonstrates higher classification accuracy as well as robustness.

Centrality measures are widely used to map each node to its importance in a network. For many practical applications, vital nodes bearing high centrality scores have superior positions over other nodes. To benefit from the positive impact of becoming a vital node, the problem of improving the centrality of the target node has attracted increasing attention. Many existing studies attack this problem by directly increasing the centrality score of the target node on the premise of knowing the network structure. However, these methods suffer from privacy issues due to their dependence on the network structure and may lose their effectiveness because other nodes can simultaneously increase the scores. Therefore, in this paper, we explore the following question: given a black-box network whose structure is unknown, is it possible to improve the centrality ranking (rather than the score) of a target node by implementing certain strategies? We provide an affirmative answer to this question. First, to avoid relying on the network structure for promotion, we propose strategies that freeze the original graph while appending nodes and edges just around the target node. Second, to guide strategies for effectively boosting centrality, we devise two principles that provide the target node with either the maximum gain or the minimum loss of centrality scores over other nodes. We prove that a strategy meeting the proposed principles is guaranteed to upgrade the target node's ranking. Extensive experiments were conducted to verify the effectiveness of the proposed strategies on black-box networks.

Personalized recommendation system (PRS) is an effective tool to automatically extract meaningful information from the big data of the users. Collaborative filtering is one of the most widely used personalized recommendation techniques to recommend the personalized products for users. In this paper, a PRS model based on the support vector machine (SVM) is proposed. The proposed model not only considers the items’ content information, but also the users’ demographic and behavior information to fully capture the users’ interests and preferences. Meanwhile, an improved particle swarm optimization (PSO) algorithm is applied to optimize the SVM’s learning parameters. The efficiency of the proposed method is verified by multiple benchmark datasets.

The open nature of recommender systems makes them vulnerable to shilling attacks. Biased ratings are introduced in order to affect recommendations, have been shown to cause great harm to collaborative filtering algorithms. Most of previous research focuses on the differences between genuine profiles and attack profiles, ignoring the group characteristics in an attack. There exists class unbalance problems in SVM based detecting methods, that is, the detecting performance is not good when the amount of samples of attack profiles in training set is small. In this paper, we study the use of SVM based method and group characteristics in attack profiles to detect attack profiles. Based on this, a two phase detecting method SVM-TIA is proposed. In the first phase, Borderline-SMOTE method is used to alleviate the class unbalance problem in classification; a rough detecting result is obtained in this phase; the second phase is a fine-tuning phase whereby the target items in the potential attack profiles set are analysed. We conduct experiments on the MovieLens 100K Dataset and compare the performance of SVM-TIA with other shilling detecting methods to demonstrate the effectiveness of the proposed approach.

Recommender systems using Collaborative Filtering techniques are capable of make personalized predictions. However, these systems are highly vulnerable to profile injection attacks. Group attacks are attacks that target a group of items instead of one, and there are common attributes among these items. Such profiles will have a good probability of being similar to a large number of user profiles, making them hard to detect. We propose a novel technique for identifying group attack profiles which uses an improved metric based on Degree of Similarity with Top Neighbors (DegSim) and Rating Deviation from Mean Agreement (RDMA). We also extend our work with a detailed analysis of target item rating patterns. Experiments show that the combined methods can improve detection rates in user-based recommender systems.