Log parsing is a critical step for automated log analysis in complex systems. Traditional heuristic-based methods offer high efficiency but are limited in accuracy due to overlooking semantic context. In contrast, recent LLM-based parsers improve accuracy via semantic understanding but incur high latency from frequent model calls. To address this, we propose SCOPE, the first self-correcting online log parsing method that integrates the strengths of both heuristic and LLM-based paradigms. SCOPE introduces a novel bi-directional tree structure that enables efficient template matching from both forward and reverse directions, resulting in a higher overall matching rate. Additionally, it adopts a two-stage syntactic-semantic collaboration framework: a lightweight NLP model first utilizes part-of-speech (POS) information for syntax-based matching, while the LLM is selectively invoked as a fallback to handle semantically complex cases when uncertainty remains. This design significantly reduces LLM API usage while maintaining high accuracy, achieving a balance between efficiency and effectiveness. Extensive evaluations on diverse benchmark datasets show that SCOPE outperforms state-of-the-art methods in both accuracy and efficiency. The implementation and datasets are publicly released to facilitate further research.

In e-commerce and online content platforms, user behaviors typically exhibit multi-scale characteristics, including long-term preferences, periodic habits, and short-term responses, while users’ decision-making processes heavily rely on multimodal item content. However, existing methods often focus on a single temporal scale or consider multimodal information only as independent features, making it difficult to achieve effective collaborative modeling under multi-scale dynamics. To address this issue, we propose a Cross-View Collaborative Recommendation with Multimodal and Multi-Scale User Behaviors (MM-MCSRec). Specifically, we first construct multi-view heterogeneous graphs based on brand, category, time, and price. Then, spectral filters are employed to decompose user behavioral signals into three frequency bands: long-term preferences, periodic habits, and short-term responses, while a node-level gating mechanism is introduced to enhance feature selectivity. Furthermore, periodic modulation and dynamic reweighting strategies are designed in the time and price views to better capture periodic patterns and short-term responses. Experiments on multiple real-world datasets demonstrate that MM-MCSRec outperforms existing recommendation methods.

With the rapid development of e-Commerce, product recommendation systems play a crucial role in enhancing user experience and increasing the volume of transaction on the platform. However, existing recommendation systems generally fail to fully consider the fine-grained features of products and primarily focus on users' positive preference features while neglecting potential negative preference features. This limitation constrains the accuracy and diversity of recommendation systems. To address this, we propose a novel multimedia recommendation model called ''Temporal Causal Fine-grained Recommendation'' (TCFRec). Specifically, we first perform fine-grained feature extraction and classification of products based on the CLIP model and a multi-level complementary attention mechanism. Subsequently, we leverage a personalized time decay strategy and causal contrastive learning to deeply explore both users' positive and negative preferences. Furthermore, counterfactual reasoning is utilized to identify and eliminate spurious correlations in multimodal features. Finally, by integrating users' fine-grained positive preference classification, negative preference classification, and the influence of social networks, we achieve accurate and diversified personalized recommendations. We conducted extensive experiments to verify the effectiveness and rationality of TCFRec.