# KB4Rec: A Dataset for Linking Knowledge Bases with Recommender Systems

arXiv: https://arxiv.org/abs/1807.11141 · HTML: https://mlsanity.com/paper/1807.11141/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2018.

## Built on by (most cited first, 19 of 19)

- [Knowledge Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2205.00976/) (2022)
- [DiffKG: Knowledge Graph Diffusion Model for Recommendation](https://mlsanity.com/paper/2312.16890/) (2023)
- [Knowledge Graph Self-Supervised Rationalization for Recommendation](https://mlsanity.com/paper/2307.02759/) (2023)
- [Knowledge Prompt-tuning for Sequential Recommendation](https://mlsanity.com/paper/2308.08459/) (2023)
- [Knowledge-based Review Generation by Coherence Enhanced Text Planning](https://mlsanity.com/paper/2105.03815/) (2021)
- [Relphormer: Relational Graph Transformer for Knowledge Graph Representations](https://mlsanity.com/paper/2205.10852/) (2022)
- [Bridging the User-side Knowledge Gap in Knowledge-aware Recommendations with Large Language Models](https://mlsanity.com/paper/2412.13544/) (2024)
- [LambdaKG: A Library for Pre-trained Language Model-Based Knowledge Graph Embeddings](https://mlsanity.com/paper/2210.00305/) (2022)
- [KERAG_R: Knowledge-Enhanced Retrieval-Augmented Generation for Recommendation](https://mlsanity.com/paper/2507.05863/) (2025)
- [Knowledge is Power, Understanding is Impact: Utility and Beyond Goals, Explanation Quality, and Fairness in Path Reasoning Recommendation](https://mlsanity.com/paper/2301.05944/) (2023)
- [RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation](https://mlsanity.com/paper/2309.10469/) (2023)
- [KG4RecEval: Does Knowledge Graph Really Matter for Recommender Systems?](https://mlsanity.com/paper/2404.03164/) (2024)
- [Knowledge Graph Pruning for Recommendation](https://mlsanity.com/paper/2405.11531/) (2024)
- [Review of Explainable Graph-Based Recommender Systems](https://mlsanity.com/paper/2408.00166/) (2024)
- [Knowledge Graph-enhanced Sampling for Conversational Recommender System](https://mlsanity.com/paper/2110.06637/) (2021)
- [Knowledge Enhanced Multi-Domain Recommendations in an AI Assistant Application](https://mlsanity.com/paper/2306.06302/) (2023)
- [On the Sweet Spot of Contrastive Views for Knowledge-enhanced Recommendation](https://mlsanity.com/paper/2309.13384/) (2023)
- [Knowledge-Aware Multi-Intent Contrastive Learning for Multi-Behavior Recommendation](https://mlsanity.com/paper/2404.11993/) (2024)
- [Beyond Factual Correctness: Mitigating Preference-Inconsistent Explanations in Explainable Recommendation](https://mlsanity.com/paper/2603.03080/) (2026)

## Abstract (arXiv)

To develop a knowledge-aware recommender system, a key data problem is how we can obtain rich and structured knowledge information for recommender system (RS) items. Existing datasets or methods either use side information from original recommender systems (containing very few kinds of useful information) or utilize private knowledge base (KB). In this paper, we present the first public linked KB dataset for recommender systems, named KB4Rec v1.0, which has linked three widely used RS datasets with the popular KB Freebase. Based on our linked dataset, we first preform some interesting qualitative analysis experiments, in which we discuss the effect of two important factors (i.e. popularity and recency) on whether a RS item can be linked to a KB entity. Finally, we present the comparison of several knowledge-aware recommendation algorithms on our linked dataset.