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Found 36 papers, 10 papers with code
Serendipitous Recommendation with Multimodal LLM
Conventional recommendation systems succeed in identifying relevant content but often fail to provide users with surprising or novel items.
User Feedback Alignment for LLM-powered Exploration in Large-scale Recommendation Systems
A key challenge is aligning LLMs with user preferences while preserving their knowledge and reasoning.
STAR: A Simple Training-free Approach for Recommendations using Large Language Models
In this paper, we propose a Simple Training-free Approach for Recommendation (STAR), a framework that utilizes LLMs and can be applied to various recommendation tasks without the need for fine-tuning, while maintaining high quality recommendation performance.
Bridging the Gap: Unpacking the Hidden Challenges in Knowledge Distillation for Online Ranking Systems
Knowledge Distillation (KD) is a powerful approach for compressing a large model into a smaller, more efficient model, particularly beneficial for latency-sensitive applications like recommender systems.
Leveraging LLM Reasoning Enhances Personalized Recommender Systems
Recent advancements have showcased the potential of Large Language Models (LLMs) in executing reasoning tasks, particularly facilitated by Chain-of-Thought (CoT) prompting.
Aligning Large Language Models with Recommendation Knowledge
Operations such as Masked Item Modeling (MIM) and Bayesian Personalized Ranking (BPR) have found success in conventional recommender systems.
Wisdom of Committee: Distilling from Foundation Model to Specialized Application Model
Further, to accommodate the dissimilarity among the teachers in the committee, we introduce DiverseDistill, which allows the student to understand the expertise of each teacher and extract task knowledge.
LEVI: Generalizable Fine-tuning via Layer-wise Ensemble of Different Views
By combining two complementing models, LEVI effectively suppresses problematic features in both the fine-tuning data and pre-trained model and preserves useful features for new tasks.
Hiformer: Heterogeneous Feature Interactions Learning with Transformers for Recommender Systems
We identify two key challenges for applying the vanilla Transformer architecture to web-scale recommender systems: (1) Transformer architecture fails to capture the heterogeneous feature interactions in the self-attention layer; (2) The serving latency of Transformer architecture might be too high to be deployed in web-scale recommender systems.
Talking Models: Distill Pre-trained Knowledge to Downstream Models via Interactive Communication
In this paper, we extend KD with an interactive communication process to help students of downstream tasks learn effectively from pre-trained foundation models.
Density Weighting for Multi-Interest Personalized Recommendation
When the data distribution is highly skewed, the gains observed by learning multiple representations diminish since the model dominates on head items/interests, leading to poor performance on tail items.
Online Matching: A Real-time Bandit System for Large-scale Recommendations
In this paper, we introduce Online Matching: a scalable closed-loop bandit system learning from users' direct feedback on items in real time.
Better Generalization with Semantic IDs: A Case Study in Ranking for Recommendations
To strike a good balance of memorization and generalization, we propose to use Semantic IDs -- a compact discrete item representation learned from frozen content embeddings using RQ-VAE that captures the hierarchy of concepts in items -- as a replacement for random item ids.
HyperFormer: Learning Expressive Sparse Feature Representations via Hypergraph Transformer
Learning expressive representations for high-dimensional yet sparse features has been a longstanding problem in information retrieval.
Unified Embedding: Battle-Tested Feature Representations for Web-Scale ML Systems
Learning high-quality feature embeddings efficiently and effectively is critical for the performance of web-scale machine learning systems.
Do LLMs Understand User Preferences? Evaluating LLMs On User Rating Prediction
In this paper, we conduct a thorough examination of both CF and LLMs within the classic task of user rating prediction, which involves predicting a user's rating for a candidate item based on their past ratings.
Improving Training Stability for Multitask Ranking Models in Recommender Systems
Recommender systems play an important role in many content platforms.
Empowering Long-tail Item Recommendation through Cross Decoupling Network (CDN)
In this work, we aim to improve tail item recommendations while maintaining the overall performance with less training and serving cost.
Improving Multi-Task Generalization via Regularizing Spurious Correlation
First, the risk of having non-causal knowledge is higher, as the shared MTL model needs to encode all knowledge from different tasks, and causal knowledge for one task could be potentially spurious to the other.
DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning
State-of-the-art MoE models use a trainable sparse gate to select a subset of the experts for each input example.
A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation
It is also very encouraging that our framework further improves head items and overall performance on top of the gains on tail items.
Learning to Embed Categorical Features without Embedding Tables for Recommendation
Embedding learning of categorical features (e. g. user/item IDs) is at the core of various recommendation models including matrix factorization and neural collaborative filtering.
DCN V2: Improved Deep & Cross Network and Practical Lessons for Web-scale Learning to Rank Systems
Learning effective feature crosses is the key behind building recommender systems.
Ranked #5 on
Click-Through Rate Prediction
on KKBox
Beyond Point Estimate: Inferring Ensemble Prediction Variation from Neuron Activation Strength in Recommender Systems
Despite deep neural network (DNN)'s impressive prediction performance in various domains, it is well known now that a set of DNN models trained with the same model specification and the same data can produce very different prediction results.
Small Towers Make Big Differences
A delicate balance between multi-task generalization and multi-objective optimization is therefore needed for finding a better trade-off between efficiency and generalization.
Self-supervised Learning for Large-scale Item Recommendations
Our online results also verify our hypothesis that our framework indeed improves model performance even more on slices that lack supervision.
Learning-to-Rank with Partitioned Preference: Fast Estimation for the Plackett-Luce Model
We investigate the Plackett-Luce (PL) model based listwise learning-to-rank (LTR) on data with partitioned preference, where a set of items are sliced into ordered and disjoint partitions, but the ranking of items within a partition is unknown.
Learning Multi-granular Quantized Embeddings for Large-Vocab Categorical Features in Recommender Systems
In this paper, we seek to learn highly compact embeddings for large-vocab sparse features in recommender systems (recsys).
Sampling-Bias-Corrected Neural Modeling for Large Corpus Item Recommendations
However, batch loss is subject to sampling bias which could severely restrict model performance, particularly in the case of power-law distribution.
Recommending what video to watch next: a multitask ranking system
In this paper, we introduce a large scale multi-objective ranking system for recommending what video to watch next on an industrial video sharing platform.
Fairness in Recommendation Ranking through Pairwise Comparisons
Recommender systems are one of the most pervasive applications of machine learning in industry, with many services using them to match users to products or information.
Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
In this work, we propose a novel multi-task learning approach, Multi-gate Mixture-of-Experts (MMoE), which explicitly learns to model task relationships from data.
Efficient Training on Very Large Corpora via Gramian Estimation
We study the problem of learning similarity functions over very large corpora using neural network embedding models.
TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks
Our focus is on simplifying cutting edge machine learning for practitioners in order to bring such technologies into production.
Wide & Deep Learning for Recommender Systems
Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort.
Ranked #2 on
Click-Through Rate Prediction
on Bing News
