Parameter Efficient Expert Retrieval (PEER)

• Parameter Efficient Expert Retrieval (PEER) is a set of techniques that optimize expert or product retrieval by leveraging compact dense representations and multimodal fusion.
• It employs dual- and multi-tower encoder architectures, adaptive attention mechanisms, and product quantization to balance high accuracy with minimal resource usage.
• PEER methods are pivotal for real-time applications in e-commerce and large-scale search, delivering enhanced relevance, reduced latency, and efficient storage.

Parameter Efficient Expert Retrieval (PEER) refers to the general family of techniques designed to maximize the efficiency, relevance, and scalability of expert (or product) retrieval systems, especially in large-scale or multimodal domains such as e-commerce. Although the term "Parameter Efficient Expert Retrieval" itself does not appear verbatim in the available literature block, it is addressed through several lines of work merging dense retrieval, contrastive pretraining, product quantization, modality fusion, and negative sampling to enable efficient and highly effective candidate selection under massive catalog and user volume constraints.

1. Background and Motivation

Expert retrieval traditionally refers to the automatic identification of domain experts (or key items) most relevant to a given query from a large corpus. In e-commerce, the analogous task is product key retrieval: returning the most semantically relevant products from billions of candidates given a user query. The dual challenges are (a) maintaining retrieval quality at scale, and (b) minimizing resource footprint—hence, parameter efficiency. Recent advances in embedding-based retrieval (EBR), multimodal fusion, and product quantization tackle these demands by compressing model size, leveraging shared representations, and optimizing trade-offs between accuracy and computational burden (Zheng et al., 2023, Xiao et al., 2021, Li et al., 2021).

2. Key Principles of Parameter Efficient Expert Retrieval

Parameter efficiency in expert retrieval systems is defined by several core design approaches:

• Dense Dual- and Multi-Tower Architectures: User/query and expert/product representations are generated via compact transformer- or CNN-based encoders, with decoupling between query and catalog sides for flexible, asynchronously indexable embeddings (e.g., MAKE's three-tower for user query, product title, product image (Zheng et al., 2023)).
• Contrastive Representation Learning: Embeddings are optimized to maximize similarity of actual matches vs. negatives, using batch-softmax, circle loss, or probabilistic matching objectives (see MoPQ and Multinoulli Contrastive Loss (Xiao et al., 2021), MAKE's KE module (Zheng et al., 2023)).
• Cross-modal and Self-supervised Fusion: Parameter-efficient fusion modules (attention-based, MLP, or weighted sum) combine different modalities (text, image) adaptively per context/query, as in Modal Adaptation (MA) (Zheng et al., 2023) and three-/four-tower fusion architectures (Liu et al., 13 Jan 2025).
• Product Quantization and Discretization: Vector quantizers such as product quantization (PQ) or VQ-VAE bottlenecks convert high-dimensional embeddings into short discrete codes, greatly reducing storage and accelerating approximate nearest neighbor search via lookup tables with little loss in accuracy (Wu et al., 2018, Xiao et al., 2021).
• Negative Sampling and Hard Negative Mining: Retrieval-specific training objectives benefit from large, diverse negatives; parameter-efficient methods exploit in-batch negatives, cross-device negatives (Differentiable Cross-device Sampling—DCS), and interpolation hard negatives to maximize discrimination power with minimal model growth (Xiao et al., 2021, Li et al., 2021).

3. Architectures and Fusion Strategies

The most effective parameter efficient retrieval systems employ specialized architectural modules to optimize modality handling, parameter reuse, and adaptive capacity:

Architecture / Module	Parameter Efficiency Strategy	Functionality
Multi-/Dual-Tower Encoders	Asynchronous, shared weights	Precompute catalog, enable fast query encoding; frequent in EBR
Modal Adaptation (MA)	Lightweight attention layers	Dynamically fuses modal signals (e.g., title, image) per query
Keyword Enhancement (KE)	No extra backbone, objective only	Leverages shared query context without adding encoder parameters
Product Quantizer (PQ/VQ-VAE)	Codebook sharing, fixed storage	Reduces vector size, enables fast lookup-based similarity search
MLP or Weighted Sum Fusion	Minimal additional parameters	Combines multi-modal representations (text/image)

Architectures such as MAKE's three-tower network use transformers but maintain fixed-size, independent encoders for each input stream, minimizing redundant parameters via separation (Zheng et al., 2023). Fusion modules such as MA (a self-attention plus cross-attention block) adapt relative weighting of title and image per product guided by the current user intent, while the KE mechanism enriches query representations with no extra encoder needed.

In large-scale product search, multimodal fusion modules are kept shallow (e.g., two layers) to maintain low latency (+2ms in MAKE (Zheng et al., 2023)) and high throughput.

4. Training Objectives and Contrastive Losses

Parameter efficiency is not only architectural but also objective-driven. Recent literature highlights direct optimization of retrieval likelihoods rather than surrogate tasks (e.g., reconstruction):

• Multinoulli Contrastive Loss (MCL): MoPQ (Xiao et al., 2021) introduces MCL, which directly maximizes the probability that a query matches its ground-truth quantized embedding, rather than minimizing vector distortion. The used objective:

$$\mathcal{L}_{MCL} = -\log \frac{\exp(\langle z^q, \hat{z}^k \rangle)}{\sum\limits_{k'} \exp(\langle z^q, \hat{z}^{k'} \rangle)}$$

This contrasts with conventional PQ approaches, where codebooks are optimized to minimize quantization loss, a strategy shown to be suboptimal for retrieval accuracy.

• Circle Loss for Query Enhancement: MAKE's KE module employs a circle loss-inspired formulation to minimize distances between semantically aligned (query, product) pairs, reducing the risk of false negatives and improving embedding space structure (Zheng et al., 2023):

$$\mathcal{L}_{QPM}^{KE} = \log\left( 1 + \sum_{j=1}^N \exp(\gamma(s_{neg}^j + \theta)) \sum_{m=1}^M \exp(-\gamma s_{pos}^m) \right)$$

• Contrastive Learning with Taxonomy-aware Sampling: For attribute value identification, fully parameter-efficient retrieval models such as TACLR (Su et al., 7 Jan 2025) use taxonomy-aware hard negative contrastive losses, ensuring the model discriminates fine-grained, in-category negatives.

These advanced objectives focus computation and parameter use solely on learning to separate true matches from informative (hard) negatives, driving parameter efficiency through task alignment.

5. Quantization and Memory Efficiency

Parameter and memory efficiency are further increased by compressing embeddings via product quantization schemes:

• Product Quantization (PQ): By splitting high-dimensional embeddings into $M$ sub-vectors, each quantized via its own sub-codebook of size $K$, the effective codebook size becomes $K^M$ (Wu et al., 2018). Storage per item is reduced to $M\log_2K$ bits, and search is accelerated by $M$ table lookups per candidate.
• VQ-VAE with Product Quantizer: This integrates PQ into the bottleneck of unsupervised VAEs, learning discrete, information-theoretically optimal codebooks. A tunable hyperparameter $\lambda$ balances quantizer and encoder/decoder strength, allowing practitioners to control generalization/overfitting (Wu et al., 2018).
• Supervised PQ with Matching-specific Objectives: MoPQ demonstrates that matching-oriented, rather than reconstruction-oriented, PQ yields higher retrieval accuracy at similar or lower parametric/quantization cost (Xiao et al., 2021).

The result is parameter-efficient retrieval at enterprise scale, enabling billions of candidates to be indexed, stored, and rapidly searched with compact infrastructure.

6. Deployment, Empirical Results, and Impact

Parameter-efficient expert retrieval frameworks have demonstrated substantial effectiveness and scalability in real-world deployments:

• Taobao Search: MAKE achieves +2.20% retrieval relevance and +0.79% GMV with only +2ms latency, in a billion-scale product corpus (Zheng et al., 2023). MGDSPR achieves further improvements by combining efficient embedding with relevance control filtering (Li et al., 2021).
• Recall and Quality: Multimodal models provide significant recall@K gains over text-only baselines (e.g., +11–20% in MAKE and up to +56% net recall advantage in exclusive matches for multimodal fusion (Liu et al., 13 Jan 2025)).
• Resource Utilization: Code quantization, as in PQ-VAE and MoPQ, permits both high-speed nearest neighbor search and compact storage with minimal loss in mAP.
• Industrial PAVI: TACLR supports tens of thousands of attributes and millions of values, with precomputed value/taxonomy embeddings and shared product encoders, providing 5–10x higher throughput than LLMs while supporting OOD and implicit values (Su et al., 7 Jan 2025).

7. Significance and Future Directions

Parameter Efficient Expert Retrieval bridges the gap between high-accuracy matching and large-scale, resource-optimized deployment. Innovations in contrastive learning objectives, adaptive multimodal fusion, and codebook design have replaced heuristic or high-parameter encoders, allowing dense retrieval to operate at previously unattainable scales and speeds in production environments.

Open challenges include further reducing memory/compute via more aggressive parameter sharing, pruning, or distillation; optimizing training dynamics to enable increasingly harder negative mining with large global batches; and continual learning to handle evolving expert (or product) corpora without full reindexing.

Parameter efficient expert/product retrieval has become central to the architecture of modern web-scale search, recommendation, and attribute identification systems, underpinning measurable impact on business metrics and user satisfaction.