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Found 18 papers, 7 papers with code
Unify Graph Learning with Text: Unleashing LLM Potentials for Session Search
Session search involves a series of interactive queries and actions to fulfill user's complex information need.
CPRM: A LLM-based Continual Pre-training Framework for Relevance Modeling in Commercial Search
Our CPRM framework includes three modules: 1) employing both queries and multi-field item to jointly pre-train for enhancing domain knowledge, 2) applying in-context pre-training, a novel approach where LLMs are pre-trained on a sequence of related queries or items, and 3) conducting reading comprehension on items to produce associated domain knowledge and background information (e. g., generating summaries and corresponding queries) to further strengthen LLMs.
Towards Boosting LLMs-driven Relevance Modeling with Progressive Retrieved Behavior-augmented Prompting
In response, we propose ProRBP, a novel Progressive Retrieved Behavior-augmented Prompting framework for integrating search scenario-oriented knowledge with LLMs effectively.
SEMINAR: Search Enhanced Multi-modal Interest Network and Approximate Retrieval for Lifelong Sequential Recommendation
To address these challenges, we propose a unified lifelong multi-modal sequence model called SEMINAR-Search Enhanced Multi-Modal Interest Network and Approximate Retrieval.
Feature-based Low-Rank Compression of Large Language Models via Bayesian Optimization
To address the challenges of low-rank compression in LLMs, we conduct empirical research on the low-rank characteristics of large models.
Multi-Intent Attribute-Aware Text Matching in Searching
Since attributes from two ends are often not aligned in terms of number and type, we propose to exploit the benefit of attributes by multiple-intent modeling.
A Multi-Granularity-Aware Aspect Learning Model for Multi-Aspect Dense Retrieval
Dense retrieval methods have been mostly focused on unstructured text and less attention has been drawn to structured data with various aspects, e. g., products with aspects such as category and brand.
Dual-Modal Attention-Enhanced Text-Video Retrieval with Triplet Partial Margin Contrastive Learning
We thereby present a new Triplet Partial Margin Contrastive Learning (TPM-CL) module to construct partial order triplet samples by automatically generating fine-grained hard negatives for matched text-video pairs.
Ranked #4 on
Video Retrieval
on MSR-VTT-1kA
An Unified Search and Recommendation Foundation Model for Cold-Start Scenario
With the development of large language models, LLM can extract global domain-invariant text features that serve both search and recommendation tasks.
AntM$^{2}$C: A Large Scale Dataset For Multi-Scenario Multi-Modal CTR Prediction
3) AntM$^{2}$C provides 1 billion CTR data with 200 features, including 200 million users and 6 million items.
Harnessing the Power of David against Goliath: Exploring Instruction Data Generation without Using Closed-Source Models
Instruction tuning is instrumental in enabling Large Language Models~(LLMs) to follow user instructions to complete various open-domain tasks.
Pre-training with Aspect-Content Text Mutual Prediction for Multi-Aspect Dense Retrieval
Extensive experiments on two real-world datasets (product and mini-program search) show that our approach can outperform competitive baselines both treating aspect values as classes and conducting the same MLM for aspect and content strings.
Beyond Semantics: Learning a Behavior Augmented Relevance Model with Self-supervised Learning
Drawing inspiration from this, we devise a novel Behavior Augmented Relevance Learning model for Alipay Search (BARL-ASe) that leverages neighbor queries of target item and neighbor items of target query to complement target query-item semantic matching.
VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs
To address this issue, we propose to learn a new powerful graph representation space by directly labeling nodes' diverse local structures for GNN-to-MLP distillation.
Individual and Structural Graph Information Bottlenecks for Out-of-Distribution Generalization
To remove class spurious feature caused by distribution shifts, we propose Individual Graph Information Bottleneck (I-GIB) which discards irrelevant information by minimizing the mutual information between the input graph and its embeddings.
GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning
At last, we conduct extensive experiments on both offline and online environments, which demonstrates the superior capability of GARCIA in improving tail queries and overall performance in service search scenarios.
Automatic Expert Selection for Multi-Scenario and Multi-Task Search
At each multi-scenario/multi-task layer, a novel expert selection algorithm is proposed to automatically identify scenario-/task-specific and shared experts for each input.
Keywords and Instances: A Hierarchical Contrastive Learning Framework Unifying Hybrid Granularities for Text Generation
Contrastive learning has achieved impressive success in generation tasks to militate the "exposure bias" problem and discriminatively exploit the different quality of references.
