Weakly supervised video anomaly detection (WSVAD) presents a challenging task focused on detecting frame-level anomalies using only video-level labels. However, existing methods focus mainly on visual modalities, neglecting rich multi-modality information. This paper proposes a novel framework, Cross-Modality Heterogeneous Knowledge Fusion (CMHKF), that integrates cross-modality knowledge from video, audio, and text to improve anomaly detection and localization. To achieve adaptive cross-modality heterogeneous knowledge learning, we designed two components: Cross-Modality Video-Text Knowledge Alignment (CVKA) and Audio Modality Feature Adaptive Extraction (AFAE). They extract and aggregate features by exploring inter-modality correlations. By leveraging abundant cross-modality knowledge, our approach improves the discrimination between normal and anomalous segments. Extensive experiments on XD-Violence show our method significantly enhances accuracy and robustness in both coarse-grained and fine-grained anomaly detection.

The filter bubble is a notorious issue in Recommender Systems (RSs), characterized by users being confined to a limited corpus of information or content that strengthens and amplifies their pre-established preferences and beliefs. Most existing methods primarily aim to analyze filter bubbles in the relatively static recommendation environment. Nevertheless, the filter bubble phenomenon continues to exacerbate as users interact with the system over time. To address these issues, we propose a novel paradigm, Hypergraph-Aware Multi-Grained Preference Learning to Burst Filter Bubbles in Conversational Recommendation System (HyperCRS), aiming to burst filter bubbles by learning multi-grained user preferences during the dynamic user-system interactions via natural language conversations. HyperCRS develops Multi-Grained Hypergraph (user-, item-, and attribute-grained) to explore diverse relations and capture high-order connectivity. It employs Hypergraph-Empowered Policy Learning, which includes Multi-Grained Preference Modeling to model user preferences and Preference-based Decision Making to disrupt filter bubbles during user interactions. Extensive results on four publicly CRS-based datasets show that HyperCRS achieves new state-of-the-art performance, and the superior of bursting filter bubbles in the CRS.

Unfairness is a well-known challenge in Recommender Systems (RSs), often resulting in biased outcomes that disadvantage users or items based on attributes such as gender, race, age, or popularity. Although some approaches have started to improve fairness recommendation in offline or static contexts, the issue of unfairness often exacerbates over time, leading to significant problems like the Matthew effect, filter bubbles, and echo chambers. To address these challenges, we proposed a novel framework, Hypergraph Contrastive Multi-Interest Learning for Fair Conversational Recommender System (HyFairCRS), aiming to promote multi-interest diversity fairness in dynamic and interactive Conversational Recommender Systems (CRSs). HyFairCRS first captures a wide range of user interests by establishing diverse hypergraphs through contrastive learning. These interests are then utilized in conversations to generate informative responses and ensure fair item predictions within the dynamic user-system feedback loop. Experiments on two CRS-based datasets show that HyFairCRS achieves a new state-of-the-art performance while effectively alleviating unfairness.

The Matthew effect is a notorious issue in Recommender Systems (RSs), i.e., the rich get richer and the poor get poorer, wherein popular items are overexposed while less popular ones are regularly ignored. Most methods examine Matthew effect in static or nearly-static recommendation scenarios. However, the Matthew effect will be increasingly amplified when the user interacts with the system over time. To address these issues, we propose a novel paradigm, Hypergraph-Enhanced Multi-Preference Learning for Alleviating Matthew Effect in Conversational Recommendation (HyCoRec), which aims to alleviate the Matthew effect in conversational recommendation. Concretely, HyCoRec devotes to alleviate the Matthew effect by learning multi-aspect preferences, i.e., item-, entity-, word-, review-, and knowledge-aspect preferences, to effectively generate responses in the conversational task and accurately predict items in the recommendation task when the user chats with the system over time. Extensive experiments conducted on two benchmarks validate that HyCoRec achieves new state-of-the-art performance and the superior of alleviating Matthew effect.

The Matthew effect is a big challenge in Recommender Systems (RSs), where popular items tend to receive increasing attention, while less popular ones are often overlooked, perpetuating existing disparities. Although many existing methods attempt to mitigate Matthew effect in the static or quasi-static recommendation scenarios, such issue will be more pronounced as users engage with the system over time. To this end, we propose a novel framework, Multi-Hypergraph Boosted Multi-Interest Self-Supervised Learning for Conversational Recommendation (HiCore), aiming to address Matthew effect in the Conversational Recommender System (CRS) involving the dynamic user-system feedback loop. It devotes to learn multi-level user interests by building a set of hypergraphs (i.e., item-, entity-, word-oriented multiple-channel hypergraphs) to alleviate the Matthew effec. Extensive experiments on four CRS-based datasets showcase that HiCore attains a new state-of-the-art performance, underscoring its superiority in mitigating the Matthew effect effectively. Our code is available at https://github.com/zysensmile/HiCore.

Conversational Recommender System (CRS) aims to explicitly acquire user preferences towards items and attributes through natural language conversations. However, existing CRS methods ask users to provide explicit answers (yes/no) for each attribute they require, regardless of users’ knowledge or interest, which may significantly reduce the user experience and semantic consistency. Furthermore, these methods assume that users like all attributes of the target item and dislike those unrelated to it, which can introduce bias in attribute-level feedback and impede the system’s ability to accurately identify the target item. To address these issues, we propose a more realistic, user-friendly, and explainable CRS framework called Hierarchical User-Interest Tracking for Conversational Recommender System (HutCRS). HutCRS portrays the conversation as a hierarchical interest tree that consists of two stages. In stage I, the system identifies the aspects that the user prefers while the system asks about attributes related to these positive aspects or recommends items in stage II. In addition, we develop a Hierarchical-Interest Policy Learning (HIPL) module to integrate the decision-making process of which aspects to ask and when to ask about attributes or recommend items. Moreover, we classify the attribute-level feedback results to further enhance the system’s ability to capture special information, such as attribute instances that are accepted by users but not presented in their historical interactive data. Extensive experiments on four benchmark datasets demonstrate the superiority of our method. The implementation of HutCRS is publicly available at https://github.com/xinle1129/HutCRS.