Search Results for author:
Found 16 papers, 9 papers with code
MINER: Multi-Interest Matching Network for News Recommendation
Accurately matching user’s interests and candidate news is the key to news recommendation.
Expectation Confirmation Preference Optimization for Multi-Turn Conversational Recommendation Agent
Recent advancements in Large Language Models (LLMs) have significantly propelled the development of Conversational Recommendation Agents (CRAs).
A Contextual-Aware Position Encoding for Sequential Recommendation
To bridge this gap, we propose a novel Contextual-Aware Position Encoding method for sequential recommendation, abbreviated as CAPE.
ChinaTravel: A Real-World Benchmark for Language Agents in Chinese Travel Planning
Recent advances in LLMs, particularly in language reasoning and tool integration, have rapidly sparked the real-world development of Language Agents.
A Parameter Update Balancing Algorithm for Multi-task Ranking Models in Recommendation Systems
Training all tasks naively can result in inconsistent learning, highlighting the need for the development of multi-task optimization (MTO) methods to tackle this challenge.
Counteracting Duration Bias in Video Recommendation via Counterfactual Watch Time
In video recommendation, an ongoing effort is to satisfy users' personalized information needs by leveraging their logged watch time.
Recall-Augmented Ranking: Enhancing Click-Through Rate Prediction Accuracy with Cross-Stage Data
RAR consists of two key sub-modules, which synergistically gather information from a vast pool of look-alike users and recall items, resulting in enriched user representations.
Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation
In the video recommendation, watch time is commonly adopted as an indicator of user interest.
ReLoop2: Building Self-Adaptive Recommendation Models via Responsive Error Compensation Loop
The error memory module is designed with fast access capabilities and undergoes continual refreshing with newly observed data samples during the model serving phase to support fast model adaptation.
FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction
As such, many two-stream interaction models (e. g., DeepFM and DCN) have been proposed by integrating an MLP network with another dedicated network for enhanced CTR prediction.
Ranked #2 on
Click-Through Rate Prediction
on MovieLens
BARS: Towards Open Benchmarking for Recommender Systems
Despite significant progress made in both research and practice of recommender systems, to date, there is a lack of a widely-recognized benchmarking standard in this field.
ReLoop: A Self-Correction Continual Learning Loop for Recommender Systems
Deep learning-based recommendation has become a widely adopted technique in various online applications.
Unbiased Top-k Learning to Rank with Causal Likelihood Decomposition
An extension to the pairwise neural ranking is also developed.
PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation
Specifically, PEAR not only captures feature-level and item-level interactions, but also models item contexts from both the initial ranking list and the historical clicked item list.
Debiased Recommendation with User Feature Balancing
To alleviate these problems, in this paper, we propose a novel debiased recommendation framework based on user feature balancing.
Non-invasive Self-attention for Side Information Fusion in Sequential Recommendation
It is still an open question to leverage various types of information under the BERT framework.
