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Secure data deletion is of critical importance for complying with retention regulations and safeguarding user privacy. In this work, we have proposed the first full-stack secure deletion design addressing both external storage and internal memory for ...

This letter investigates the sample efficiency property of recommender systems enhanced by large language models. We propose a simple yet effective framework (i.e., Laser) to validate the core viewpoint - large language models make sample-...

In this letter, we propose E2URec, the efficient and effective unlearning method for LLMRec. Our method enables LLMRec to efficiently forget the specific data by only updating the lightweight LoRA modules. Besides, to enhance the effectiveness, ...

The multi-camera capture system is an emerging visual sensing modality. It facilitates the production of various immersive contents ranging from regular to neural videos. Although the delivery of immersive content is popular and promising, it suffers ...

High-quality datasets are crucial for advancing research in automatic text summarization. At present, summarization models for resource-rich languages like Chinese and English have made significant progress. However, for low-resource languages ...

We propose an interactive and intelligent hybrid teleconferencing system compatible with Virtual Reality devices. Our system understands meeting contexts and leverages user interactions to enhance better system configuration. Employing interactive scene ...

Image captioning evaluation metrics can be divided into two categories, reference-based metrics and reference-free metrics. However, reference-based approaches may struggle to evaluate descriptive captions with abundant visual details produced by ...

Click-through rate (CTR) prediction is crucial for personalized online services. Sample-level retrieval-based models, such as RIM, have demonstrated remarkable performance. However, they face challenges including inference inefficiency and high resource ...

Large language models have been flourishing in the natural language processing (NLP) domain, and their potential for recommendation has been paid much attention to. Despite the intelligence shown by the recommendation-oriented finetuned models, LLMs ...

Click-Through Rate (CTR) prediction is a fundamental technique in recommendation and advertising systems. Recent studies have shown that implementing multi-scenario recommendations contributes to strengthening information sharing and improving overall ...

Conversational recommender systems (CRSs) aim to capture user preferences and provide personalized recommendations through multi-round natural language dialogues. However, most existing CRS models mainly focus on dialogue comprehension and preferences ...

Click-Through Rate (CTR) prediction is a fundamental technique for online advertising recommendation and the complex online competitive auction process also brings many difficulties to CTR optimization. Recent studies have shown that introducing ...

Click-through rate (CTR) prediction plays as a core function module in various personalized online services. The traditional ID-based models for CTR prediction take as inputs the one-hot encoded ID features of tabular modality, which capture the ...

Recommender system plays a vital role in various online services. However, its insulated nature of training and deploying separately within a specific closed domain limits its access to open-world knowledge. Recently, the emergence of large language ...

As the open community of large language models (LLMs) matures, multimodal LLMs (MLLMs) have promised an elegant bridge between vision and language. However, current research is inherently constrained by challenges such as the need for high-quality ...

Recent advances in computer vision algorithms and video streaming technologies have facilitated the development of edge-server-based video analytics systems, enabling them to process sophisticated real-world tasks, such as traffic surveillance and ...

With the rapid proliferation of scientific literature, versatile academic knowledge services increasingly rely on comprehensive academic graph mining. Despite the availability of public academic graphs, benchmarks, and datasets, these resources often ...

Recommender systems play important roles in various applications such as e-commerce, social media, etc. Conventional recommendation methods usually model the collaborative signals within the tabular representation space. Despite the personalization ...

Deep Recommender Systems (DRS) are increasingly dependent on a large number of feature fields for more precise recommendations. Effective feature selection methods are consequently becoming critical for further enhancing the accuracy and optimizing ...

Convolutional Neural Networks (CNN) have given rise to numerous visual analytics applications at the edge of the Internet. The image is typically captured by cameras and then live-streamed to edge servers for analytics due to the prohibitive cost of ...