# What does BERT know about books, movies and music? Probing BERT for Conversational Recommendation

arXiv: https://arxiv.org/abs/2007.15356 · HTML: https://mlsanity.com/paper/2007.15356/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jul 2020.

## Built on by (most cited first, 30 of 36)

- [A Survey on Large Language Models for Recommendation](https://mlsanity.com/paper/2305.19860/) (2023)
- [Recommender AI Agent: Integrating Large Language Models for Interactive Recommendations](https://mlsanity.com/paper/2308.16505/) (2023)
- [Large Language Models as Zero-Shot Conversational Recommenders](https://mlsanity.com/paper/2308.10053/) (2023)
- [Advances and Challenges in Conversational Recommender Systems: A Survey](https://mlsanity.com/paper/2101.09459/) (2021)
- [GPT4Rec: A Generative Framework for Personalized Recommendation and User Interests Interpretation](https://mlsanity.com/paper/2304.03879/) (2023)
- [Language Model Behavior: A Comprehensive Survey](https://mlsanity.com/paper/2303.11504/) (2023)
- [Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences](https://mlsanity.com/paper/2307.14225/) (2023)
- [Leveraging Large Language Models in Conversational Recommender Systems](https://mlsanity.com/paper/2305.07961/) (2023)
- [A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)](https://mlsanity.com/paper/2404.00579/) (2024)
- [Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models](https://mlsanity.com/paper/2305.13112/) (2023)
- [Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations](https://mlsanity.com/paper/2307.05722/) (2023)
- [Zero-Shot Recommendation as Language Modeling](https://mlsanity.com/paper/2112.04184/) (2021)
- [LLM-Enhanced User-Item Interactions: Leveraging Edge Information for Optimized Recommendations](https://mlsanity.com/paper/2402.09617/) (2024)
- [Learning to Perform Complex Tasks through Compositional Fine-Tuning of Language Models](https://mlsanity.com/paper/2210.12607/) (2022)
- [Conversational Recommendation: A Grand AI Challenge](https://mlsanity.com/paper/2203.09126/) (2022)
- [Interpretation of Black Box NLP Models: A Survey](https://mlsanity.com/paper/2203.17081/) (2022)
- [Unveiling Code Pre-Trained Models: Investigating Syntax and Semantics Capacities](https://mlsanity.com/paper/2212.10017/) (2022)
- [Why Would You Suggest That? Human Trust in Language Model Responses](https://mlsanity.com/paper/2406.02018/) (2024)
- [What BERT Based Language Models Learn in Spoken Transcripts: An Empirical Study](https://mlsanity.com/paper/2109.09105/) (2021)
- [Alleviating the Long-Tail Problem in Conversational Recommender Systems](https://mlsanity.com/paper/2307.11650/) (2023)
- [Unintended Bias in Language Model-driven Conversational Recommendation](https://mlsanity.com/paper/2201.06224/) (2022)
- [Understanding User Intent Modeling for Conversational Recommender Systems: A Systematic Literature Review](https://mlsanity.com/paper/2308.08496/) (2023)
- ["It doesn't look good for a date": Transforming Critiques into Preferences for Conversational Recommendation Systems](https://mlsanity.com/paper/2109.07576/) (2021)
- [A Conversation is Worth A Thousand Recommendations: A Survey of Holistic Conversational Recommender Systems](https://mlsanity.com/paper/2309.07682/) (2023)
- [Multi-Task End-to-End Training Improves Conversational Recommendation](https://mlsanity.com/paper/2305.06218/) (2023)
- [Improving Content Recommendation: Knowledge Graph-Based Semantic Contrastive Learning for Diversity and Cold-Start Users](https://mlsanity.com/paper/2403.18667/) (2024)
- [Temporal User Profiling with LLMs: Balancing Short-Term and Long-Term Preferences for Recommendations](https://mlsanity.com/paper/2508.08454/) (2025)
- [ASR Error Detection via Audio-Transcript entailment](https://mlsanity.com/paper/2207.10849/) (2022)
- [Complexity-based partitioning of CSFI problem instances with Transformers](https://mlsanity.com/paper/2106.14481/) (2021)
- [Edge Data Based Trailer Inception Probabilistic Matrix Factorization for Context-Aware Movie Recommendation](https://mlsanity.com/paper/2202.10236/) (2022)

## Abstract (arXiv)

Heavily pre-trained transformer models such as BERT have recently shown to be remarkably powerful at language modelling by achieving impressive results on numerous downstream tasks. It has also been shown that they are able to implicitly store factual knowledge in their parameters after pre-training. Understanding what the pre-training procedure of LMs actually learns is a crucial step for using and improving them for Conversational Recommender Systems (CRS). We first study how much off-the-shelf pre-trained BERT "knows" about recommendation items such as books, movies and music. In order to analyze the knowledge stored in BERT's parameters, we use different probes that require different types of knowledge to solve, namely content-based and collaborative-based. Content-based knowledge is knowledge that requires the model to match the titles of items with their content information, such as textual descriptions and genres. In contrast, collaborative-based knowledge requires the model to match items with similar ones, according to community interactions such as ratings. We resort to BERT's Masked Language Modelling head to probe its knowledge about the genre of items, with cloze style prompts. In addition, we employ BERT's Next Sentence Prediction head and representations' similarity to compare relevant and non-relevant search and recommendation query-document inputs to explore whether BERT can, without any fine-tuning, rank relevant items first. Finally, we study how BERT performs in a conversational recommendation downstream task. Overall, our analyses and experiments show that: (i) BERT has knowledge stored in its parameters about the content of books, movies and music; (ii) it has more content-based knowledge than collaborative-based knowledge; and (iii) fails on conversational recommendation when faced with adversarial data.