PDF Viewer

BUILDER'S SANDBOX

Build This Paper

Use an AI coding agent to implement this research.

OpenAI Codex
OpenAI CodexAI Agent

Lightweight coding agent in your terminal.

Claude Code
Claude CodeAI Agent

Agentic coding tool for terminal workflows.

AntiGravity IDE
AntiGravity IDEScaffolding

AI agent mindset installer and workflow scaffolder.

Cursor
CursorIDE

AI-first code editor built on VS Code.

VS Code
VS CodeIDE

Free, open-source editor by Microsoft.

Estimated $9K - $13K over 6-10 weeks.

See exactly what it costs to build this -- with 3 comparable funded startups.

7-day free trial. Cancel anytime.

Discover the researchers behind this paper and find similar experts.

7-day free trial. Cancel anytime.

References (50)

[1]
HatLLM: Hierarchical Attention Masking for Enhanced Collaborative Modeling in LLM-based Recommendation
2025Yu Cui, Feng Liu et al.
[2]
Breaking the Top-K Barrier: Advancing Top-K Ranking Metrics Optimization in Recommender Systems
2025Weiqin Yang, Jiawei Chen et al.
[3]
Understanding the Effect of Loss Functions on the Generalization of Recommendations
2025Yuanhao Pu, Defu Lian et al.
[4]
Field Matters: A lightweight LLM-enhanced Method for CTR Prediction
2025Yu Cui, Feng Liu et al.
[5]
Advancing Loss Functions in Recommender Systems: A Comparative Study with a Rényi Divergence-Based Solution
2025Shengjia Zhang, Jiawei Chen et al.
[6]
MSL: Not All Tokens Are What You Need for Tuning LLM as a Recommender
2025Bohao Wang, Feng Liu et al.
[7]
Rankformer: A Graph Transformer for Recommendation based on Ranking Objective
2025Sirui Chen, Shen Han et al.
[8]
PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation
2024Weiqin Yang, Jiawei Chen et al.
[9]
How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective
2024Siyi Lin, Chongming Gao et al.
[10]
SIGformer: Sign-aware Graph Transformer for Recommendation
2024Sirui Chen, Jiawei Chen et al.
[11]
Lower-Left Partial AUC: An Effective and Efficient Optimization Metric for Recommendation
2024Wentao Shi, Chenxu Wang et al.
[12]
Empowering Collaborative Filtering with Principled Adversarial Contrastive Loss
2023An Zhang, Leheng Sheng et al.
[13]
Adaptive Popularity Debiasing Aggregator for Graph Collaborative Filtering
2023Huachi Zhou, Hao Chen et al.
[14]
Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation
2023Chongming Gao, Kexin Huang et al.
[15]
Adap-τ : Adaptively Modulating Embedding Magnitude for Recommendation
2023Jiawei Chen, Junkang Wu et al.
[16]
On the Theories Behind Hard Negative Sampling for Recommendation
2023Wentao Shi, Jiawei Chen et al.
[17]
Joint Optimization of Ranking and Calibration with Contextualized Hybrid Model
2022Xiang-Rong Sheng, Jingyue Gao et al.
[18]
Causal Representation Learning for Out-of-Distribution Recommendation
2022Wenjie Wang, Xinyu Lin et al.
[19]
When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee
2022Dixian Zhu, Gang Li et al.
[20]
Recall@k Surrogate Loss with Large Batches and Similarity Mixup
2021Yash J. Patel, Giorgos Tolias et al.

Showing 20 of 50 references

Founder's Pitch

"Talos is a recommendation optimization tool that improves Top-K accuracy efficiently using a novel loss function and sampling-based regression algorithm."

Recommender SystemsScore: 6View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

4/4 signals

10

Series A Potential

1/4 signals

2.5

Sources used for this analysis

arXiv Paper

Full-text PDF analysis of the research paper

GitHub Repository

Code availability, stars, and contributor activity

Citation Network

Semantic Scholar citations and co-citation patterns

Community Predictions

Crowd-sourced unicorn probability assessments

Analysis model: GPT-4o · Last scored: 1/27/2026

Explore the full citation network and related research.

7-day free trial. Cancel anytime.

Understand the commercial significance and market impact.

7-day free trial. Cancel anytime.

Get detailed profiles of the research team.

7-day free trial. Cancel anytime.