Session-based recommendation (SBR), which focuses on next-item prediction for anonymous users based on short-term interaction sequences, has garnered increasing attention from researchers. While graph neural networks (GNNs) have become predominant in modeling complex item transition patterns, our empirical study reveals two critical limitations in existing GNN-based SBR methods. On the one hand, they struggle to differentiate between sessions sharing the same last item, resulting in indistinguishable session representations. On the other hand, the inherent popularity bias in session data leads to the over-recommendation of popular items. Inspired by contrastive learning techniques, this paper presents a unified mitigation framework for Same lAst-item confusion in Graph lEarning (SAGE) for SBR. In SAGE, we first obtain normalized session embeddings on constructed session graphs. We then build positive and negative samples of sessions through dual forward propagations and a novel negative sample selection strategy, followed by calculating contrastive loss. Finally, the enhanced session embeddings are utilized for prediction. Extensive experiments on two real-world datasets demonstrate that integrating SAGE with various state-of-the-art GNN-based SBR methods significantly improves their original performances.

Next Point-of-interest (POI) recommendation has been widely used in real scenarios to predict the next possible location based on user behavior patterns. However, existing methods predominantly rely on spatio-temporal associations and check-in sequence relationships between users and POIs, which fall short for users with limited interactions with POIs. Moreover, user preferences are inherently multi-dimensional, rendering user selections often influenced by multiple factors such as location categories and multi-modal information. To mitigate these issues, we introduce aMulti-Modal Knowledge GraphModeling ofMulti-Dimensional User Preferences for Next-POIRecommendation (M4Recfor short). First, we define a multi-modal knowledge graph to organize the relationships among users, locations, categories, and multi-modal information. Subsequently, we use the multi-modal knowledge graph-based relation-aware network to derive comprehensive entity representations from the constructed knowledge graph. Next, employing the temporal knowledge prediction method, we predict the user's next-POI category and next-POI. Finally, the final recommendation results are obtained by enhancing the corresponding location prediction scores through category semantics. Extensive experimentation conducted on real-world datasets validates the superiority of our proposed method over state-of-the-art competitors.

Conversational recommender systems (CRSs) aim to proactively capture user preferences through natural language dialogue and recommend high-quality items. To achieve this, CRS gathers user preferences via a dialog module and builds user profiles through a recommendation module to generate appropriate recommendations. However, existing CRS faces challenges in capturing the deep semantics of user preferences and dialogue context. In particular, the efficient integration of external knowledge graph (KG) information into dialogue generation and recommendation remains a pressing issue. Traditional approaches typically combine KG information directly with dialogue content, which often struggles with complex semantic relationships, resulting in recommendations that may not align with user expectations.

Personalized recommendation systems play a critical role in helping users discover relevant content amidst information overload. This paper proposes DisenRec, a novel sequential recommendation framework that addresses key limitations in existing approaches. By constructing a heterogeneous graph that incorporates multidimensional contextual information, we first learn initial user/item representations using a Heterogeneous Graph Attention Network. We then disentangle user preferences into dynamic interest preferences (modeling temporal behavioral patterns) and static attribute preferences (capturing stable trait-based inclinations) through causal decomposition and orthogonal constraints. A context-aware fusion module dynamically balances these components during prediction. Experiments on Amazon-Books and MovieLens-1M datasets demonstrate that DisenRec significantly outperforms state-of-the-art baselines in HR@10 and NDCG@10 metrics. Our model reduces representation entanglement, enhances preference modeling granularity, and improves both recommendation accuracy and interpretability by uncovering the causal mechanisms driving user decisions.

Session-based Recommendation (SBR) aims to predict the next item a user will likely engage with, using their interaction sequence within an anonymous session. Existing SBR models often focus only on single-session information, ignoring inter-session relationships and valuable cross-session insights. Some methods try to include inter-session data but struggle with noise and irrelevant information, reducing performance. Additionally, most models rely on item ID co-occurrence and overlook rich semantic details, limiting their ability to capture fine-grained item features. To address these challenges, we propose a novel hierarchical intent-guided optimization approach with pluggable LLM-driven semantic learning for session-based recommendations, called HIPHOP. First, we introduce a pluggable embedding module based on large language models (LLMs) to generate high-quality semantic representations, enhancing item embeddings. Second, HIPHOP utilizes graph neural networks (GNNs) to model item transition relationships and incorporates a dynamic multi-intent capturing module to address users' diverse interests within a session. Additionally, we design a hierarchical inter-session similarity learning module, guided by user intent, to capture global and local session relationships, effectively exploring users' long-term and short-term interests. To mitigate noise, an intent-guided denoising strategy is applied during inter-session learning. Finally, we enhance the model's discriminative capability by using contrastive learning to optimize session representations. Experiments on multiple datasets show that HIPHOP significantly outperforms existing methods, demonstrating its effectiveness in improving recommendation quality. Our code is available: https://github.com/hjx159/HIPHOP.

Sequential recommendation has been receiving increasing attention from researchers. Existing sequential recommendation models leverage deep learning models to capture sequential features. However, these methods ignore confounders in the recommendation process, which can lead the model to learn incorrect correlations and fail to accurately capture users' true preferences. Moreover, these methods rely on extensive interaction sequences, but sequential data often suffers from sparsity issues. To address these limitations, this paper proposes a P reference- a ware C ausal I ntervention and Counter f a c tual Data Augmentation ( Pacific ) framework to enhance sequential recommendation. Initially, we model the causal graph of sequential recommendation and categorize user preferences into global long-term preferences, local long-term preferences, and short-term preferences. Then, we introduce the front-door criterion to eliminate the interference of confounders and design different self-attention mechanisms to estimate the causal effects, aiming to capture users' true preferences. In addition, based on counterfactual thinking, we design a counterfactual data augmentation module to generate enriched sequences. Experimental results on four real-world datasets demonstrate the superiority of our proposed approach over state-of-the-art sequential recommendation methods.

Recently, using machine learning technology to realize abnormal behavior recognition in video surveillance to replace human monitoring has become a hot academic topic. In that case, constructing an efficient and unified framework for multi-type abnormal behavior recognition is a worthy topic in machine learning research. This research aims to design a lightweight recognition framework that can recognize various abnormal behaviors in real-time. We propose a Novel framewOrk for the Multi-type Abnormal BEhavior Recognition (NOMABER), which consists of three parts. Firstly, the improved image pre-processing module annotates the abnormal behaviors of image data sets. Secondly, the improved YOLOv5 module is used to identify the multi-type abnormal behaviors, and then the abnormal behaviors are classified by the output module. Finally, experiments on real data sets show that NOMABER is superior to the current methods of real-time performance, identification accuracy, and types of abnormal behaviors.

The next-item recommendation problem has received more and more attention from researchers in recent years. Ignoring the implicit item semantic information, existing algorithms focus more on the user-item binary relationship and suffer from high data sparsity. Inspired by the fact that user's decision-making process is often influenced by both intention and preference, this paper presents a SequentiAl inTentiOn-aware Recommender based on a user Interaction graph (Satori). In Satori, we first use a novel user interaction graph to construct relationships between users, items, and categories. Then, we leverage a graph attention network to extract auxiliary features on the graph and generate the three embeddings. Next, we adopt self-attention mechanism to model user intention and preference respectively which are later combined to form a hybrid user representation. Finally, the hybrid user representation and previously obtained item representation are both sent to the prediction modul to calculate the predicted item score. Testing on real-world datasets, the results prove that our approach outperforms state-of-the-art methods.

Spatial keyword querying has attracted considerable research efforts in the past few years. A prototypical query takes a location and keywords as arguments and returns the k objects that score the highest according to a ranking function. While different scoring functions have been used, how to compare different ranking functions for spatial keyword querying still remains an open question with little investigation. We propose a crowdsourcing-based approach to evaluate and compare ranking functions for spatial keyword search. Given two ranking functions f1 and f2, we use a matrix to model all possible binary questions regarding the different results produced by f1 and f2. We propose a multi-step process to reduce the number of binary questions, identifying the most important questions to ask. Further, we design a crowdsourcing model that obtains the answers to those important binary questions from crowd workers. We also devise a global evaluation process that is able to quantitatively compare f1 and f2 based on a multitude of answers received. According to the results of empirical studies using real data, the proposed approach is efficient and able to draw reliable conclusions in comparing ranking functions for spatial keyword search.