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Found 15 papers, 8 papers with code
T-Drive: Driving Directions Based on Taxi Trajectories
GPS-equipped taxis can be regarded as mobile sensors probing traffic flows on road surfaces, and taxi drivers are usually experienced in finding the fastest (quickest) route to a destination based on their knowledge.
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
On one hand, the xDeepFM is able to learn certain bounded-degree feature interactions explicitly; on the other hand, it can learn arbitrary low- and high-order feature interactions implicitly.
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on Dianping
KRED: Knowledge-Aware Document Representation for News Recommendations
News articles usually contain knowledge entities such as celebrities or organizations.
DebiasedRec: Bias-aware User Modeling and Click Prediction for Personalized News Recommendation
The core of our method includes a bias representation module, a bias-aware user modeling module, and a bias-aware click prediction module.
Hybrid Encoder: Towards Efficient and Precise Native AdsRecommendation via Hybrid Transformer Encoding Networks
For the sake of efficient recommendation, conventional methods would generate user and advertisement embeddings independently with a siamese transformer encoder, such that approximate nearest neighbour search (ANN) can be leveraged.
GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph
The representation learning on textual graph is to generate low-dimensional embeddings for the nodes based on the individual textual features and the neighbourhood information.
Efficient-FedRec: Efficient Federated Learning Framework for Privacy-Preserving News Recommendation
However, the computation and communication cost of directly learning many existing news recommendation models in a federated way are unacceptable for user clients.
UA-FedRec: Untargeted Attack on Federated News Recommendation
Our study reveals a critical security issue in existing federated news recommendation systems and calls for research efforts to address the issue.
A Mutually Reinforced Framework for Pretrained Sentence Embeddings
Instead of relying on annotation heuristics defined by humans, it leverages the sentence representation model itself and realizes the following iterative self-supervision process: on one hand, the improvement of sentence representation may contribute to the quality of data annotation; on the other hand, more effective data annotation helps to generate high-quality positive samples, which will further improve the current sentence representation model.
Robust Quantity-Aware Aggregation for Federated Learning
Federated learning (FL) enables multiple clients to collaboratively train models without sharing their local data, and becomes an important privacy-preserving machine learning framework.
Effective and Efficient Query-aware Snippet Extraction for Web Search
In this paper, we propose an effective query-aware webpage snippet extraction method named DeepQSE, aiming to select a few sentences which can best summarize the webpage content in the context of input query.
AdaSAM: Boosting Sharpness-Aware Minimization with Adaptive Learning Rate and Momentum for Training Deep Neural Networks
Integrating SAM with adaptive learning rate and momentum acceleration, dubbed AdaSAM, has already been explored empirically to train large-scale deep neural networks without theoretical guarantee due to the triple difficulties in analyzing the coupled perturbation step, adaptive learning rate and momentum step.
Are You Copying My Model? Protecting the Copyright of Large Language Models for EaaS via Backdoor Watermark
Companies have begun to offer Embedding as a Service (EaaS) based on these LLMs, which can benefit various natural language processing (NLP) tasks for customers.
FedLALR: Client-Specific Adaptive Learning Rates Achieve Linear Speedup for Non-IID Data
Federated learning is an emerging distributed machine learning method, enables a large number of clients to train a model without exchanging their local data.
Benchmarking and Defending Against Indirect Prompt Injection Attacks on Large Language Models
Based on the evaluation, our work makes a key analysis of the underlying reason for the success of the attack, namely the inability of LLMs to distinguish between instructions and external content and the absence of LLMs' awareness to not execute instructions within external content.
