# Addressing the Item Cold-start Problem by Attribute-driven Active Learning

arXiv: https://arxiv.org/abs/1805.09023 · HTML: https://mlsanity.com/paper/1805.09023/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2018.

## Built on by (most cited first, 30 of 33)

- [Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets](https://mlsanity.com/paper/2202.02794/) (2022)
- [Active Learning Through a Covering Lens](https://mlsanity.com/paper/2205.11320/) (2022)
- [Making Your First Choice: To Address Cold Start Problem in Vision Active Learning](https://mlsanity.com/paper/2210.02442/) (2022)
- [ResNorm: Tackling Long-tailed Degree Distribution Issue in Graph Neural Networks via Normalization](https://mlsanity.com/paper/2206.08181/) (2022)
- [Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation](https://mlsanity.com/paper/2310.20293/) (2023)
- [Reinforcement Learning to Optimize Lifetime Value in Cold-Start Recommendation](https://mlsanity.com/paper/2108.09141/) (2021)
- [COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image Segmentation](https://mlsanity.com/paper/2307.12004/) (2023)
- [Reenvisioning Collaborative Filtering vs Matrix Factorization](https://mlsanity.com/paper/2107.13472/) (2021)
- [Zero Shot on the Cold-Start Problem: Model-Agnostic Interest Learning for Recommender Systems](https://mlsanity.com/paper/2108.13592/) (2021)
- [GIFT: Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction](https://mlsanity.com/paper/2202.11525/) (2022)
- [A Survey on Dropout Methods and Experimental Verification in Recommendation](https://mlsanity.com/paper/2204.02027/) (2022)
- [Node Duplication Improves Cold-start Link Prediction](https://mlsanity.com/paper/2402.09711/) (2024)
- [Next Generation Active Learning: Mixture of LLMs in the Loop](https://mlsanity.com/paper/2601.15773/) (2026)
- [GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning](https://mlsanity.com/paper/2304.12537/) (2023)
- [Direct Acquisition Optimization for Low-Budget Active Learning](https://mlsanity.com/paper/2402.06045/) (2024)
- [Perception Without Vision for Trajectory Prediction: Ego Vehicle Dynamics as Scene Representation for Efficient Active Learning in Autonomous Driving](https://mlsanity.com/paper/2405.09049/) (2024)
- [Learning to Learn a Cold-start Sequential Recommender](https://mlsanity.com/paper/2110.09083/) (2021)
- [Towards Contrastive Learning in Music Video Domain](https://mlsanity.com/paper/2309.00347/) (2023)
- [Benchmarking of Query Strategies: Towards Future Deep Active Learning](https://mlsanity.com/paper/2312.05751/) (2023)
- [Towards Explainable Multi-Party Learning: A Contrastive Knowledge Sharing Framework](https://mlsanity.com/paper/2104.06670/) (2021)
- [Using Social Media Background to Improve Cold-start Recommendation Deep Models](https://mlsanity.com/paper/2106.02256/) (2021)
- [SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis](https://mlsanity.com/paper/2108.02572/) (2021)
- [VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search](https://mlsanity.com/paper/2108.02725/) (2021)
- [Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generation](https://mlsanity.com/paper/2109.05778/) (2021)
- [Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors](https://mlsanity.com/paper/2202.02830/) (2022)
- [Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks](https://mlsanity.com/paper/2203.11011/) (2022)
- [Explanation-Guided Fairness Testing through Genetic Algorithm](https://mlsanity.com/paper/2205.08335/) (2022)
- [Using Graph Neural Networks for Program Termination](https://mlsanity.com/paper/2207.14648/) (2022)
- [Tribe or Not? Critical Inspection of Group Differences Using TribalGram](https://mlsanity.com/paper/2303.09664/) (2023)
- [MeKB-Rec: Personal Knowledge Graph Learning for Cross-Domain Recommendation](https://mlsanity.com/paper/2310.11088/) (2023)

## Abstract (arXiv)

In recommender systems, cold-start issues are situations where no previous events, e.g. ratings, are known for certain users or items. In this paper, we focus on the item cold-start problem. Both content information (e.g. item attributes) and initial user ratings are valuable for seizing users' preferences on a new item. However, previous methods for the item cold-start problem either 1) incorporate content information into collaborative filtering to perform hybrid recommendation, or 2) actively select users to rate the new item without considering content information and then do collaborative filtering. In this paper, we propose a novel recommendation scheme for the item cold-start problem by leverage both active learning and items' attribute information. Specifically, we design useful user selection criteria based on items' attributes and users' rating history, and combine the criteria in an optimization framework for selecting users. By exploiting the feedback ratings, users' previous ratings and items' attributes, we then generate accurate rating predictions for the other unselected users. Experimental results on two real-world datasets show the superiority of our proposed method over traditional methods.