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Wuchao Li

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4 papers

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ICLR Conference 2025 Conference Paper

Making Transformer Decoders Better Differentiable Indexers

Wuchao Li
Kai Zheng 0001
Defu Lian
Qi Liu 0003
Wentian Bao
Yunen Yu
Yang Song 0008
Han Li 0005

Retrieval aims to find the top-k items most relevant to a query/user from a large dataset. Traditional retrieval models represent queries/users and items as embedding vectors and use Approximate Nearest Neighbor (ANN) search for retrieval. Recently, researchers have proposed a generative-based retrieval method that represents items as token sequences and uses a decoder model for autoregressive training. Compared to traditional methods, this approach uses more complex models and integrates index structure during training, leading to better performance. However, these methods remain two-stage processes, where index construction is separate from the retrieval model, limiting the model's overall capacity. Additionally, existing methods construct indices by clustering pre-trained item representations in Euclidean space. However, real-world scenarios are more complex, making this approach less accurate. To address these issues, we propose a \underline{U}nified framework for \underline{R}etrieval and \underline{I}ndexing, termed \textbf{URI}. URI ensures strong consistency between index construction and the retrieval model, typically a Transformer decoder. URI simultaneously builds the index and trains the decoder, constructing the index through the decoder itself. It no longer relies on one-sided item representations in Euclidean space but constructs the index within the interactive space between queries and items. Experimental comparisons on three real-world datasets show that URI significantly outperforms existing methods.

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ICLR Conference 2025 Conference Paper

RecFlow: An Industrial Full Flow Recommendation Dataset

Qi Liu 0003
Kai Zheng 0007
Rui Huang 0009
Wuchao Li
Kuo Cai
Yuan Chai
Yanan Niu
Yiqun Hui

Industrial recommendation systems (RS) rely on the multi-stage pipeline to balance effectiveness and efficiency when delivering items from a vast corpus to users. Existing RS benchmark datasets primarily focus on the exposure space, where novel RS algorithms are trained and evaluated. However, when these algorithms transition to real-world industrial RS, they face two critical challenges: (1) handling unexposed items—a significantly larger space than the exposed one, profoundly impacting their practical performance; and (2) overlooking the intricate interplay between multiple stages of the recommendation pipeline, resulting in suboptimal system performance. To bridge the gap between offline RS benchmarks and real-world online environments, we introduce RecFlow—an industrial full-flow recommendation dataset. Unlike existing datasets, RecFlow includes samples not only from the exposure space but also from unexposed items filtered at each stage of the RS funnel. RecFlow comprises 38 million interactions from 42,000 users across nearly 9 million items with additional 1.9 billion stage samples collected from 9.3 million online requests over 37 days and spanning 6 stages. Leveraging RecFlow, we conduct extensive experiments to demonstrate its potential in designing novel algorithms that enhance effectiveness by incorporating stage-specific samples. Some of these algorithms have already been deployed online at KuaiShou, consistently yielding significant gains. We propose RecFlow as the first comprehensive whole-pipeline benchmark dataset for the RS community, enabling research on algorithm design across the entire recommendation pipeline, including selection bias study, debiased algorithms, multi-stage consistency and optimality, multi-task recommendation, and user behavior modeling.

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ICLR Conference 2023 Conference Paper

Learned Index with Dynamic $\epsilon$

Daoyuan Chen
Wuchao Li
Yaliang Li
Bolin Ding
Kai Zeng 0002
Defu Lian
Jingren Zhou 0001

Index structure is a fundamental component in database and facilitates broad data retrieval applications. Recent learned index methods show superior performance by learning hidden yet useful data distribution with the help of machine learning, and provide a guarantee that the prediction error is no more than a pre-defined $\epsilon$. However, existing learned index methods adopt a fixed $\epsilon$ for all the learned segments, neglecting the diverse characteristics of different data localities. In this paper, we propose a mathematically-grounded learned index framework with dynamic $\epsilon$, which is efficient and pluggable to existing learned index methods. We theoretically analyze prediction error bounds that link $\epsilon$ with data characteristics for an illustrative learned index method. Under the guidance of the derived bounds, we learn how to vary $\epsilon$ and improve the index performance with a better space-time trade-off. Experiments with real-world datasets and several state-of-the-art methods demonstrate the efficiency, effectiveness and usability of the proposed framework.

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NeurIPS Conference 2022 Conference Paper

Recommender Forest for Efficient Retrieval

Chao Feng
Wuchao Li
Defu Lian
Zheng Liu
Enhong Chen

Recommender systems (RS) have to select the top-N items from a massive item set. For the sake of efficient recommendation, RS usually represents user and item as latent embeddings, and relies on approximate nearest neighbour search (ANNs) to retrieve the recommendation result. Despite the reduction of running time, the representation learning is independent of ANNs index construction; thus, the two operations can be incompatible, which results in potential loss of recommendation accuracy. To overcome the above problem, we propose the Recommender Forest (a. k. a. , RecForest), which jointly learns latent embedding and index for efficient and high-fidelity recommendation. RecForest consists of multiple k-ary trees, each of which is a partition of the item set via hierarchical balanced clustering such that each item is uniquely represented by a path from the root to a leaf. Given such a data structure, an encoder-decoder based routing network is developed: it first encodes the context, i. e. , user information, into hidden states; then, leveraging a transformer-based decoder, it identifies the top-N items via beam search. Compared with the existing methods, RecForest brings in the following advantages: 1) the false partition of the boundary items can be effectively alleviated by the use of multiple trees; 2) the routing operation becomes much more accurate thanks to the powerful transformer decoder; 3) the tree parameters are shared across different tree levels, making the index to be extremely memory-efficient. The experimental studies are performed on five popular recommendation datasets: with a significantly simplified training cost, RecForest outperforms competitive baseline approaches in terms of both recommendation accuracy and efficiency.

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