Large language models (LLMs) have demonstrated outstanding performance in natural language processing tasks. However, in the field of recommender systems, due to the inherent structural discrepancy between user behavior data and natural language, LLMs struggle to effectively model the associations between user preferences and items. Although prompt-based methods can generate recommendation results, their inadequate understanding of recommendation tasks leads to constrained performance. To address this gap, we construct a comprehensive instruction tuning dataset, ITDR, which encompasses seven subtasks across two root tasks: user-item interaction and user-item understanding. The dataset integrates data from 13 public recommendation datasets and is built using manually crafted standardized templates, comprising approximately 200,000 instances. Experimental results demonstrate that ITDR significantly enhances the performance of mainstream open-source LLMs such as GLM-4, Qwen2.5, Qwen2.5-Instruct and LLaMA-3.2 on recommendation tasks. Furthermore, we analyze the correlations between tasks and explore the impact of task descriptions and data scale on instruction tuning effectiveness. Finally, we perform comparative experiments against closed-source LLMs with massive parameters. Our tuning dataset ITDR, the fine-tuned large recommendation models, all LoRA modules, and the complete experimental results are available at https://github.com/hellolzk/ITDR.

Recent explorations into the potential of large language models (LLMs) within recommendation systems have demonstrated promising performance. However, LLM-based recommenders still face significant challenges in complex scenarios. Most existing alignment approaches primarily focus on direct item generation based on user-interaction histories, frequently neglecting to fully exploit valuable feedback such as reviews and ratings. Furthermore, these methods rarely consider the benefits of incorporating reasoning mechanisms to enhance recommendation accuracy. To overcome these limitations and improve the reliability of LLM-based recommenders under data-scarce conditions, we propose SILLM4Rec, a framework specifically designed to strengthen both reasoning ability and recommendation performance. Our approach begins by extracting high-quality Chain-of-Thought (CoT) reasoning samples from a more capable teacher model. These samples are then used to fine-tune a smaller student model through a self-learning and iterative optimization process, enabling it to refine its outputs and adapt to user preferences. Comprehensive experiments on three publicly available benchmark datasets demonstrate that SILLM4Rec not only achieves superior performance metrics, but also enhances transparency and robustness in recommendation tasks. Our code is available at https://github.com/MKC-Lab/SILLM4Rec.

Conversational Recommender System (CRS) leverages real-time feedback from users to dynamically model their preferences, thereby enhancing the system's ability to provide personalized recommendations and improving the overall user experience. CRS has demonstrated significant promise, prompting researchers to concentrate their efforts on developing user simulators that are both more realistic and trustworthy. The advent of Large Language Models (LLMs) has demonstrated capabilities that approach human-level intelligence across a diverse range of tasks. Research efforts have been made to utilize LLMs for building user simulators to evaluate the performance of CRS. Although these efforts showcase innovation, they are accompanied by certain limitations. In this work, we introduce a Controllable, Scalable, and Human-Involved (CSHI) simulator framework that manages the behavior of user simulators across various stages via a plugin manager. CSHI tailors behavioral simulations and interaction patterns to deliver authentic user-system engagement experiences. Through experiments and case studies in two conversational recommendation scenarios, we show that our framework can adapt to a variety of conversational recommendation settings and effectively simulate users' personalized preferences. Consequently, our simulator is able to generate feedback that closely mirrors that of real users. This facilitates a reliable assessment of existing CRS studies and promotes the creation of high-quality conversational recommendation datasets.

Recently, recommender system has achieved significant success. However, due to the openness of recommender systems, they remain vulnerable to malicious attacks. Additionally, natural noise in training data and issues such as data sparsity can also degrade the performance of recommender systems. Therefore, enhancing the robustness of recommender systems has become an increasingly important research topic. In this survey, we provide a comprehensive overview of the robustness of recommender systems. Based on our investigation, we categorize the robustness of recommender systems into adversarial robustness and non-adversarial robustness. In the adversarial robustness, we introduce the fundamental principles and classical methods of recommender system adversarial attacks and defenses. In the non-adversarial robustness, we analyze nonadversarial robustness from the perspectives of data sparsity, natural noise, and data imbalance. Additionally, we summarize commonly used datasets and evaluation metrics for evaluating the robustness of recommender systems. Finally, we also discuss the current challenges in the field of recommender system robustness and potential future research directions. Additionally, to facilitate fair and efficient evaluation of attack and defense methods in adversarial robustness, we propose an adversarial robustness evaluation library–ShillingREC, and we conduct evaluations of basic attack models and recommendation models. ShillingREC project is released at https://github.com/ chengleileilei/ShillingREC.

The traditional recommendation systems mainly use offline user data to train offline models, and then recommend items for online users, thus suffering from the unreliable estimation of user preferences based on sparse and noisy historical data. Conversational Recommendation System(CRS) uses the interactive form of the dialogue systems to solve the intrinsic problems of traditional recommendation systems. However, due to the lack of contextual information modeling, the existing CRS models are unable to deal with the exploitation and exploration(E&E) problem well, resulting in the heavy burden on users. To address the aforementioned issue, this work proposes a contextual information enhancement model tailored for CRS, called Knowledge Graph-enhanced Sampling(KGenSam). KGenSam integrates the dynamic graph of user interaction data with the external knowledge into one heterogeneous Knowledge Graph(KG) as the contextual information environment. Then, two samplers are designed to enhance knowledge by sampling fuzzy samples with high uncertainty for obtaining user preferences and reliable negative samples for updating recommender to achieve efficient acquisition of user preferences and model updating, and thus provide a powerful solution for CRS to deal with E&E problem. Experimental results on two real-world datasets demonstrate the superiority of KGenSam with significant improvements over state-of-the-art methods.

The cold-start recommendation is an urgent problem in contemporary online applications. It aims to provide users whose behaviors are literally sparse with as accurate recommendations as possible. Many data-driven algorithms, such as the widely used matrix factorization, underperform because of data sparseness. This work adopts the idea of meta-learning to solve the user’s cold-start recommendation problem. We propose a meta-learning-based cold-start sequential recommendation framework called metaCSR, including three main components: Diffusion Representer for learning better user/item embedding through information diffusion on the interaction graph; Sequential Recommender for capturing temporal dependencies of behavior sequences; and Meta Learner for extracting and propagating transferable knowledge of prior users and learning a good initialization for new users. metaCSR holds the ability to learn the common patterns from regular users’ behaviors and optimize the initialization so that the model can quickly adapt to new users after one or a few gradient updates to achieve optimal performance. The extensive quantitative experiments on three widely used datasets show the remarkable performance of metaCSR in dealing with the user cold-start problem. Meanwhile, a series of qualitative analysis demonstrates that the proposed metaCSR has good generalization.

Microblogging data contains a wealth of information of trending events and has gained increased attention among users, organizations, and research scholars for social media mining in different disciplines. Event storyline generation is one typical task of social media mining, whose goal is to extract the development stages with associated description of events. Existing storyline generation methods either generate storyline with less integrity or fail to guarantee the coherence between the discovered stages. Secondly, there are no scientific method to evaluate the quality of the storyline. In this paper, we propose a comprehensive storyline generation framework to address the above disadvantages. Given Microblogging data related to the specified event, we first propose Hot-Word-Based stage detection algorithm to identify the potential stages of event, which can effectively avoid ignoring important stages and preventing inconsistent sequence between stages. Community detection algorithm is applied then to select representative data for each stage. Finally, we conduct graph optimization algorithm to generate the logically coherent storylines of the event. We also introduce a new evaluation metric, SLEU, to emphasize the importance of the integrity and coherence of the generated storyline. Extensive experiments on real-world Chinese microblogging data demonstrate the effectiveness of the proposed methods in each module and the overall framework.

With the rapid development of Online Social Networks (OSNs), it is crucial to construct users' portraits from their dynamic behaviors to address the increasing needs for customized information services. Previous work on user attribute inference mainly concentrated on developing advanced features/models or exploiting external information and knowledge but ignored the contradiction between dynamic behaviors and stable demographic attributes, which results in deviation of user understanding

Human Activity Recognition (HAR) automatically recognizes human activities such as daily life and work based on digital records, which is of great significance to medical and health fields. Egocentric video and human acceleration data comprehensively describe human activity patterns from different aspects, which have laid a foundation for activity recognition based on multimodal behavior data. However, on the one hand, the low-level multimodal signal structures differ greatly and the mapping to high-level activities is complicated. On the other hand, the activity labeling based on multimodal behavior data has high cost and limited data amount, which limits the technical development in this field. In this paper, an activity recognition model MAFE based on multimodal attribute feature embedding is proposed. Before the activity recognition, the middle-level attribute features are extracted from the low-level signals of different modes. On the one hand, the mapping complexity from the low-level signals to the high-level activities is reduced, and on the other hand, a large number of middle-level attribute labeling data can be used to reduce the dependency on the activity labeling data. We conducted experiments on Stanford-ECM datasets to verify the effectiveness of the proposed MAFE method.

The ever growing number of videos on YouTube makes recommendation an important way to help users explore interesting videos. Similar to general recommender systems, YouTube video recommendation suffers from typical problems like new user, cold-start, data sparsity, etc. In this paper, we propose a unified YouTube video recommendation solution via cross-network collaboration: users' auxiliary information on Twitter are exploited to address the typical problems in single network-based recommendation solutions. The proposed two-stage solution first transfers user preferences from auxiliary network by learning cross-network behavior correlations, and then integrates the transferred preferences with the observed behaviors on target network in an adaptive fashion. Experimental results show that the proposed cross-network collaborative solution achieves superior performance not only in term of accuracy, but also in improving the diversity and novelty of the recommended videos.

While on the go, people are using their phones as a personal concierge discovering what is around and deciding what to do. Mobile phone has become a recommendation terminal customized for individuals—capable of recommending activities and simplifying the accomplishment of related tasks. In this article, we conduct usage mining on the check-in data, with summarized statistics identifying the local recommendation challenges of huge solution space, sparse available data, and complicated user intent, and discovered observations to motivate the hierarchical, contextual, and sequential solution. We present a point-of-interest (POI) category-transition--based approach, with a goal of estimating the visiting probability of a series of successive POIs conditioned on current user context and sensor context. A mobile local recommendation demo application is deployed. The objective and subjective evaluations validate the effectiveness in providing mobile users both accurate recommendation and favorable user experience.

While on the go, people are using their phones as a personal concierge discovering what is around and deciding what to do. Mobile phone has become a recommendation terminal customized for individuals. While existing research predominantly focuses on one-step recommendation---recommending the next single activity according to current context, this work moves one step beyond by recommending a series of activities, which is a package of sequential Points of Interest (POIs). The recommended POIs are not only relevant to user context (i.e., current location, time, and check-in), but also personalized to his/her check-in history. We presents a probabilistic approach, which is highly motivated from a large-scale commercial mobile check-in data analysis, to ranking a list of sequential POI categories (e.g., "Japanese food" and "bar") and POIs (e.g., "I love sushi"). The approach enables users to plan consecutive activities on the move. Specifically, the probabilistic recommendation approach estimates the transition probability from one POI to another, conditioned on current context and check-in history in a Markov chain. To alleviate the discritization error and sparsity problem, we further introduce context collaboration and integrate prior information. Experiments on over 100k real-world check-in records and 20k POIs validate the effectiveness of the proposed approach.