Multi-modal sequential recommendation (SR) leverages multi-modal data to learn more comprehensive item features and user preferences than traditional SR methods, which has become a critical topic in both academia and industry. Existing methods typically focus on enhancing multi-modal information utility through adaptive modality fusion to capture the evolving of user preference from user-item interaction sequences. However, most of them overlook the interference caused by redundant interest-irrelevant information contained in rich multi-modal data. Additionally, they primarily rely on implicit temporal information based solely on chronological ordering, neglecting explicit temporal signals that could more effectively represent dynamic user interest over time. To address these limitations, we propose a Hierarchical time-aware Mixture of experts for multi-modal Sequential Recommendation (HM4SR) with a two-level Mixture of Experts (MoE) and a multi-task learning strategy. Specifically, the first MoE, named Interactive MoE, extracts essential user interest-related information from the multi-modal data of each item. Then, the second MoE, termed Temporal MoE, captures user dynamic interests by introducing explicit temporal embeddings from timestamps in modality encoding. To further address data sparsity, we propose three auxiliary supervision tasks: sequence-level category prediction (CP) for item feature understanding, contrastive learning on ID (IDCL) to align sequence context with user interests, and placeholder contrastive learning (PCL) to integrate temporal information with modalities for dynamic interest modeling. Extensive experiments on four public datasets verify the effectiveness of HM4SR compared to several state-of-the-art approaches.

Sequential recommendation is a crucial task in understanding users' evolving interests and predicting their future behaviors. While existing approaches on sequence or graph modeling to learn interaction sequences of users have shown promising performance, how to effectively exploit temporal information and deal with the uncertainty noise in evolving user behaviors is still quite challenging. To this end, in this paper, we propose a Temporal Graph Contrastive Learning method for Sequential Recommendation (TGCL4SR) which leverages not only local interaction sequences but also global temporal graphs to comprehend item correlations and analyze user behaviors from a temporal perspective. Specifically, we first devise a Temporal Item Transition Graph (TITG) to fully leverage global interactions to understand item correlations, and augment this graph by dual transformations based on neighbor sampling and time disturbance. Accordingly, we design a Temporal item Transition graph Convolutional network (TiTConv) to capture temporal item transition patterns in TITG. Then, a novel Temporal Graph Contrastive Learning (TGCL) mechanism is designed to enhance the uniformity of representations between augmented graphs from identical sequences. For local interaction sequences, we design a temporal sequence encoder to incorporate time interval embeddings into the architecture of Transformer. At the training stage, we take maximum mean discrepancy and TGCL losses as auxiliary objectives. Extensive experiments on several real-world datasets show the effectiveness of TGCL4SR against state-of-the-art baselines of sequential recommendation.

Recently, Temporal Knowledge Graph Forecasting (TKGF) has emerged as a pivotal domain for forecasting future events.Unlike black-box neural network methods, rule-based approaches are lauded for their efficiency and interpretability.For this line of work, it is crucial to correctly estimate the predictive effectiveness of the rules, i.e., the confidence.However, the existing literature lacks in-depth investigation into how confidence evolves with time.Moreover, inaccurate and heuristic confidence estimation limits the performance of rule-based methods.To alleviate such issues, we propose a framework named TempValid to explicitly model the temporal validity of rules for TKGF.Specifically, we design a time function to model the interaction between temporal information with confidence.TempValid conceptualizes confidence and other coefficients as learnable parameters to avoid inaccurate estimation and combinatorial explosion.Furthermore, we introduce a rule-adversarial negative sampling and a time-aware negative sampling strategies to facilitate TempValid learning.Extensive experiments show that TempValid significantly outperforms previous state-of-theart (SOTA) rule-based methods on six TKGF datasets.Moreover, it exhibits substantial advancements in cross-domain and resourceconstrained rule learning scenarios.

Recently, multi-modal entity alignment has emerged as a pivotal endeavor for the integration of Multi-Modal Knowledge Graphs (MMKGs) originating from diverse data sources. Existing works primarily focus on fully depicting entity features by designing various modality encoders or fusion approaches. However, uncertain correspondences between inter-modal or intra-modal cues, such as weak inter-modal associations, description diversity, and modality absence, still severely hinder the effective exploration of aligned entity similarities. To this end, in this paper, we propose a novel Tackling uncertain correspondences method for Multi-modal Entity Alignment (TMEA). Specifically, to handle diverse attribute knowledge descriptions, we design alignment-augmented abstract representation that incorporates the large language model and in-context learning into attribute alignment and filtering for generating and embedding the attribute abstract. In order to mitigate the influence of the modality absence, we propose to unify all modality features into a shared latent subspace and generate pseudo features via variational autoencoders according to existing modal features. Then, we develop an inter-modal commonality enhancement mechanism based on cross-attention with orthogonal constraints, to address weak semantic associations between modalities. Extensive experiments on two real-world datasets validate the effectiveness of TMEA with a clear improvement over competitive baselines.

The emergence of Transformer and its derivative models brings new opportunities to tasks of NLP (Natural Language Processing). Transformer is not only a separate model, but also the core of different text task systems. Therefore, Transformer has become an important component of many powerful models. However, Transformer is not without defects. Researchers are still puzzled by the huge amount of computation generated in the process of self-attention. Especially in long text data sets. We propose a new ProbSparse self-attention Transformer model based for text classification. In the following, we will call it SpaL Transformer. We query important attention factors through KL divergence and add Long Short Term Memory(LSTM) to positional encoding, and only focus on the main query. Then, we select the most important relevant attention based on the confidence score to focus the overall attention. At the same time, we propose LSTM positive encoding to obtain relative position information to optimize the model. In long text dataset IMDB, our model improves the accuracy of Transformer. And F1 score improved by 0.064.