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Add to EdgeMultimodal Pretraining and Generation for Recommendation: A Tutorial
May 11, 2024
Personalized recommendation stands as a ubiquitous channel for users to explore information or items aligned with their interests. Nevertheless, prevailing recommendation models predominantly rely on unique IDs and categorical features for user-item matching. While this ID-centric approach has witnessed considerable success, it falls short in comprehensively grasping the essence of raw item contents across diverse modalities, such as text, image, audio, and video. This underutilization of multimodal data poses a limitation to recommender systems, particularly in the realm of multimedia services like news, music, and short-video platforms. The recent surge in pretraining and generation techniques presents both opportunities and challenges in the development of multimodal recommender systems. This tutorial seeks to provide a thorough exploration of the latest advancements and future trajectories in multimodal pretraining and generation techniques within the realm of recommender systems. The tutorial comprises three parts: multimodal pretraining, multimodal generation, and industrial applications and open challenges in the field of recommendation. Our target audience encompasses scholars, practitioners, and other parties interested in this domain. By providing a succinct overview of the field, we aspire to facilitate a swift understanding of multimodal recommendation and foster meaningful discussions on the future development of this evolving landscape.
Vector Quantization for Recommender Systems: A Review and Outlook
May 06, 2024Vector quantization, renowned for its unparalleled feature compression capabilities, has been a prominent topic in signal processing and machine learning research for several decades and remains widely utilized today. With the emergence of large models and generative AI, vector quantization has gained popularity in recommender systems, establishing itself as a preferred solution. This paper starts with a comprehensive review of vector quantization techniques. It then explores systematic taxonomies of vector quantization methods for recommender systems (VQ4Rec), examining their applications from multiple perspectives. Further, it provides a thorough introduction to research efforts in diverse recommendation scenarios, including efficiency-oriented approaches and quality-oriented approaches. Finally, the survey analyzes the remaining challenges and anticipates future trends in VQ4Rec, including the challenges associated with the training of vector quantization, the opportunities presented by large language models, and emerging trends in multimodal recommender systems. We hope this survey can pave the way for future researchers in the recommendation community and accelerate their exploration in this promising field.
Contrastive Quantization based Semantic Code for Generative Recommendation
Apr 23, 2024
With the success of large language models, generative retrieval has emerged as a new retrieval technique for recommendation. It can be divided into two stages: the first stage involves constructing discrete Codes (i.e., codes), and the second stage involves decoding the code sequentially via the transformer architecture. Current methods often construct item semantic codes by reconstructing based quantization on item textual representation, but they fail to capture item discrepancy that is essential in modeling item relationships in recommendation sytems. In this paper, we propose to construct the code representation of items by simultaneously considering both item relationships and semantic information. Specifically, we employ a pre-trained language model to extract item's textual description and translate it into item's embedding. Then we propose to enhance the encoder-decoder based RQVAE model with contrastive objectives to learn item code. To be specific, we employ the embeddings generated by the decoder from the samples themselves as positive instances and those from other samples as negative instances. Thus we effectively enhance the item discrepancy across all items, better preserving the item neighbourhood. Finally, we train and test semantic code with with generative retrieval on a sequential recommendation model. Our experiments demonstrate that our method improves NDCG@5 with 43.76% on the MIND dataset and Recall@10 with 80.95% on the Office dataset compared to the previous baselines.
A Survey on the Memory Mechanism of Large Language Model based Agents
Apr 21, 2024Large language model (LLM) based agents have recently attracted much attention from the research and industry communities. Compared with original LLMs, LLM-based agents are featured in their self-evolving capability, which is the basis for solving real-world problems that need long-term and complex agent-environment interactions. The key component to support agent-environment interactions is the memory of the agents. While previous studies have proposed many promising memory mechanisms, they are scattered in different papers, and there lacks a systematical review to summarize and compare these works from a holistic perspective, failing to abstract common and effective designing patterns for inspiring future studies. To bridge this gap, in this paper, we propose a comprehensive survey on the memory mechanism of LLM-based agents. In specific, we first discuss ''what is'' and ''why do we need'' the memory in LLM-based agents. Then, we systematically review previous studies on how to design and evaluate the memory module. In addition, we also present many agent applications, where the memory module plays an important role. At last, we analyze the limitations of existing work and show important future directions. To keep up with the latest advances in this field, we create a repository at \url{https://github.com/nuster1128/LLM_Agent_Memory_Survey}.
Recall-Augmented Ranking: Enhancing Click-Through Rate Prediction Accuracy with Cross-Stage Data
Apr 15, 2024
Click-through rate (CTR) prediction plays an indispensable role in online platforms. Numerous models have been proposed to capture users' shifting preferences by leveraging user behavior sequences. However, these historical sequences often suffer from severe homogeneity and scarcity compared to the extensive item pool. Relying solely on such sequences for user representations is inherently restrictive, as user interests extend beyond the scope of items they have previously engaged with. To address this challenge, we propose a data-driven approach to enrich user representations. We recognize user profiling and recall items as two ideal data sources within the cross-stage framework, encompassing the u2u (user-to-user) and i2i (item-to-item) aspects respectively. In this paper, we propose a novel architecture named Recall-Augmented Ranking (RAR). RAR consists of two key sub-modules, which synergistically gather information from a vast pool of look-alike users and recall items, resulting in enriched user representations. Notably, RAR is orthogonal to many existing CTR models, allowing for consistent performance improvements in a plug-and-play manner. Extensive experiments are conducted, which verify the efficacy and compatibility of RAR against the SOTA methods.
PMG : Personalized Multimodal Generation with Large Language Models
Apr 07, 2024The emergence of large language models (LLMs) has revolutionized the capabilities of text comprehension and generation. Multi-modal generation attracts great attention from both the industry and academia, but there is little work on personalized generation, which has important applications such as recommender systems. This paper proposes the first method for personalized multimodal generation using LLMs, showcases its applications and validates its performance via an extensive experimental study on two datasets. The proposed method, Personalized Multimodal Generation (PMG for short) first converts user behaviors (e.g., clicks in recommender systems or conversations with a virtual assistant) into natural language to facilitate LLM understanding and extract user preference descriptions. Such user preferences are then fed into a generator, such as a multimodal LLM or diffusion model, to produce personalized content. To capture user preferences comprehensively and accurately, we propose to let the LLM output a combination of explicit keywords and implicit embeddings to represent user preferences. Then the combination of keywords and embeddings are used as prompts to condition the generator. We optimize a weighted sum of the accuracy and preference scores so that the generated content has a good balance between them. Compared to a baseline method without personalization, PMG has a significant improvement on personalization for up to 8% in terms of LPIPS while retaining the accuracy of generation.
RAT: Retrieval-Augmented Transformer for Click-Through Rate Prediction
Apr 05, 2024Predicting click-through rates (CTR) is a fundamental task for Web applications, where a key issue is to devise effective models for feature interactions. Current methodologies predominantly concentrate on modeling feature interactions within an individual sample, while overlooking the potential cross-sample relationships that can serve as a reference context to enhance the prediction. To make up for such deficiency, this paper develops a Retrieval-Augmented Transformer (RAT), aiming to acquire fine-grained feature interactions within and across samples. By retrieving similar samples, we construct augmented input for each target sample. We then build Transformer layers with cascaded attention to capture both intra- and cross-sample feature interactions, facilitating comprehensive reasoning for improved CTR prediction while retaining efficiency. Extensive experiments on real-world datasets substantiate the effectiveness of RAT and suggest its advantage in long-tail scenarios. The code has been open-sourced at \url{https://github.com/YushenLi807/WWW24-RAT}.
Multimodal Pretraining, Adaptation, and Generation for Recommendation: A Survey
Mar 31, 2024Personalized recommendation serves as a ubiquitous channel for users to discover information or items tailored to their interests. However, traditional recommendation models primarily rely on unique IDs and categorical features for user-item matching, potentially overlooking the nuanced essence of raw item contents across multiple modalities such as text, image, audio, and video. This underutilization of multimodal data poses a limitation to recommender systems, especially in multimedia services like news, music, and short-video platforms. The recent advancements in pretrained multimodal models offer new opportunities and challenges in developing content-aware recommender systems. This survey seeks to provide a comprehensive exploration of the latest advancements and future trajectories in multimodal pretraining, adaptation, and generation techniques, as well as their applications to recommender systems. Furthermore, we discuss open challenges and opportunities for future research in this domain. We hope that this survey, along with our tutorial materials, will inspire further research efforts to advance this evolving landscape.
Discrete Semantic Tokenization for Deep CTR Prediction
Mar 21, 2024Incorporating item content information into click-through rate (CTR) prediction models remains a challenge, especially with the time and space constraints of industrial scenarios. The content-encoding paradigm, which integrates user and item encoders directly into CTR models, prioritizes space over time. In contrast, the embedding-based paradigm transforms item and user semantics into latent embeddings, subsequently caching them to optimize processing time at the expense of space. In this paper, we introduce a new semantic-token paradigm and propose a discrete semantic tokenization approach, namely UIST, for user and item representation. UIST facilitates swift training and inference while maintaining a conservative memory footprint. Specifically, UIST quantizes dense embedding vectors into discrete tokens with shorter lengths and employs a hierarchical mixture inference module to weigh the contribution of each user--item token pair. Our experimental results on news recommendation showcase the effectiveness and efficiency (about 200-fold space compression) of UIST for CTR prediction.
Benchmarking News Recommendation in the Era of Green AI
Mar 15, 2024
Over recent years, news recommender systems have gained significant attention in both academia and industry, emphasizing the need for a standardized benchmark to evaluate and compare the performance of these systems. Concurrently, Green AI advocates for reducing the energy consumption and environmental impact of machine learning. To address these concerns, we introduce the first Green AI benchmarking framework for news recommendation, known as GreenRec, and propose a metric for assessing the tradeoff between recommendation accuracy and efficiency. Our benchmark encompasses 30 base models and their variants, covering traditional end-to-end training paradigms as well as our proposed efficient only-encode-once (OLEO) paradigm. Through experiments consuming 2000 GPU hours, we observe that the OLEO paradigm achieves competitive accuracy compared to state-of-the-art end-to-end paradigms and delivers up to a 2992\% improvement in sustainability metrics.