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Found 24 papers, 12 papers with code
Pangu Light: Weight Re-Initialization for Pruning and Accelerating LLMs
While structured pruning offers a promising avenue for model compression, existing methods often struggle with the detrimental effects of aggressive, simultaneous width and depth reductions, leading to substantial performance degradation.
The Real Barrier to LLM Agent Usability is Agentic ROI
Large Language Model (LLM) agents represent a promising shift in human-AI interaction, moving beyond passive prompt-response systems to autonomous agents capable of reasoning, planning, and goal-directed action.
Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs
This study led to Pangu Ultra MoE, a sparse LLM with 718 billion parameters, and we conducted experiments on the model to verify the simulation results.
Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs
Our exploration demonstrates that Ascend NPUs are capable of efficiently and effectively training dense models with more than 100 billion parameters.
Beyond Graph Convolution: Multimodal Recommendation with Topology-aware MLPs
Given the large volume of side information from different modalities, multimodal recommender systems have become increasingly vital, as they exploit richer semantic information beyond user-item interactions.
Unleashing the Potential of Multi-Channel Fusion in Retrieval for Personalized Recommendations
Despite advancements in individual retrieval methods, multi-channel fusion, the process of efficiently merging multi-channel retrieval results, remains underexplored.
All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Era
Recommender systems (RS) are vital for managing information overload and delivering personalized content, responding to users' diverse information needs.
A Comprehensive Survey on Retrieval Methods in Recommender Systems
By detailing the retrieval stage, which is fundamental for effective recommendation, this survey aims to bridge the existing knowledge gap and serve as a cornerstone for researchers interested in optimizing this critical component of cascade recommender systems.
Retrieval-Oriented Knowledge for Click-Through Rate Prediction
The framework features a knowledge base that preserves and imitates the retrieved \& aggregated representations using a decomposition-reconstruction paradigm.
M-scan: A Multi-Scenario Causal-driven Adaptive Network for Recommendation
Furthermore, through causal graph analysis, we have discovered that the scenario itself directly influences click behavior, yet existing approaches directly incorporate data from other scenarios during the training of the current scenario, leading to prediction biases when they directly utilize click behaviors from other scenarios to train models.
D2K: Turning Historical Data into Retrievable Knowledge for Recommender Systems
A personalized knowledge adaptation unit is devised to effectively exploit the information from the knowledge base by adapting the retrieved knowledge to the target samples.
A Survey on User Behavior Modeling in Recommender Systems
Besides, we elaborate on the industrial practices of UBM methods with the hope of providing insights into the application value of existing UBM solutions.
Disentangling Past-Future Modeling in Sequential Recommendation via Dual Networks
Sequential recommendation (SR) plays an important role in personalized recommender systems because it captures dynamic and diverse preferences from users' real-time increasing behaviors.
Neural Re-ranking in Multi-stage Recommender Systems: A Review
As the final stage of the multi-stage recommender system (MRS), re-ranking directly affects user experience and satisfaction by rearranging the input ranking lists, and thereby plays a critical role in MRS. With the advances in deep learning, neural re-ranking has become a trending topic and been widely applied in industrial applications.
MISS: Multi-Interest Self-Supervised Learning Framework for Click-Through Rate Prediction
With the help of two novel CNN-based multi-interest extractors, self-supervision signals are discovered with full considerations of different interest representations (point-wise and union-wise), interest dependencies (short-range and long-range), and interest correlations (inter-item and intra-item).
Retrieval & Interaction Machine for Tabular Data Prediction
Prediction over tabular data is an essential task in many data science applications such as recommender systems, online advertising, medical treatment, etc.
Deep Learning for Click-Through Rate Estimation
In this survey, we provide a comprehensive review of deep learning models for CTR estimation tasks.
GraphHINGE: Learning Interaction Models of Structured Neighborhood on Heterogeneous Information Network
Heterogeneous information network (HIN) has been widely used to characterize entities of various types and their complex relations.
An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph
To the best of our knowledge, this is the first work providing an efficient neighborhood-based interaction model in the HIN-based recommendations.
User Behavior Retrieval for Click-Through Rate Prediction
These retrieved behaviors are then fed into a deep model to make the final prediction instead of simply using the most recent ones.
Deep Landscape Forecasting for Real-time Bidding Advertising
The problem is formulated as to forecast the probability distribution of market price for each ad auction.
Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction
In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user.
Deep Recurrent Survival Analysis
By capturing the time dependency through modeling the conditional probability of the event for each sample, our method predicts the likelihood of the true event occurrence and estimates the survival rate over time, i. e., the probability of the non-occurrence of the event, for the censored data.
