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Traditional click-through rate (CTR) prediction models convert the tabular data into one-hot vectors and leverage the collaborative relations among features for inferring the user’s preference over items. This modeling paradigm discards essential semantic ...

With the rapid development of online services and web applications, recommender systems (RS) have become increasingly indispensable for mitigating information overload and matching users’ information needs by providing personalized suggestions over items. ...

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, ...

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 ...

Knowledge Tracing (KT) aims to determine whether students will respond correctly to the next question, which is a crucial task in intelligent tutoring systems (ITS). In educational KT scenarios, transductive ID-based methods often face severe data ...

As the demand for more personalized recommendation grows and a dramatic boom in commercial scenarios arises, the study on multi-scenario recommendation (MSR) has attracted much attention, which uses the data from all scenarios to simultaneously improve ...

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 ...

Multi-Domain Click-Through Rate (MDCTR) prediction is crucial for online recommendation platforms, which involves providing personalized recommendation services to users in different domains. However, current MDCTR models are confronted with the following ...

Estimating the post-click conversion rate (CVR) accurately in ranking systems is crucial in industrial applications. However, this task is often challenged by data sparsity and selection bias, which hinder accurate ranking. Previous approaches to ...

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 ...

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 ...

As a critical task for large-scale commercial recommender systems, reranking rearranges items in the initial ranking lists from the previous ranking stage to better meet users’ demands. Foundational work in reranking has shown the potential of improving ...

Click-through rate (CTR) prediction plays an indispensable role in online recommendation and advertising platforms. Numerous deep learning based models have been proposed to improve CTR prediction accuracy, and they typically leverage user behavior ...

Integrated ranking is a critical component in industrial recommendation platforms. It combines candidate lists from different upstream channels or sources and ranks them into an integrated list, which will be exposed to users. During this process, to ...