---
title: 'Reddit2Deezer: Music Recommendation Dataset'
url: https://www.emergentmind.com/topics/reddit2deezer
type: topic
---

# Reddit2Deezer: Music Recommendation Dataset

Searching arXiv for the specified paper and closely related Deezer/Reddit music-recommendation work to ground the article in current literature.
arXiv search query: "Reddit2Deezer conversational music recommendation Deezer Reddit dataset"
Reddit2Deezer is a grounded conversational music recommendation resource derived from naturally occurring Reddit exchanges and linked to the Deezer catalog. It was introduced to address a persistent tradeoff in conversational music recommendation between authentic but small corpora and large but synthetic corpora. The resource is built from **190k unique \{thread, leaf-comment\} pairs**, is released in both **raw** and **paraphrased** forms, and associates each recommended musical entity with a **Deezer identifier**, thereby exposing audio previews and catalog metadata such as genre tags, popularity, BPM, release date, and duration [2605.09120]. The dataset also functions as a benchmark: recommendation models are evaluated against a full Deezer-grounded catalog of **130,010 items**, spanning both tracks and albums, which makes Reddit2Deezer not merely a dialogue corpus but a content-grounded CMR testbed [2605.09120].

## 1. Scope, motivation, and problem setting

Reddit2Deezer was designed for a setting in which conversational recommendation research had bifurcated into two unsatisfactory regimes: small authentic datasets collected from real interactions, and scalable synthetic datasets generated through templates, taxonomies, multi-agent simulations, or LLM pipelines. The former preserve genuine recommendation intent, conversational asymmetry, and naturally expressed taste constraints, but are limited in scale; the latter scale to modern training regimes but risk distribution shift because the conversations are artificially constructed rather than socially observed [2605.09120].

Within that landscape, Reddit2Deezer is defined by four properties. First, it is **reality-grounded**, because the source conversations are mined from Reddit rather than authored for the benchmark. Second, it is **catalog-grounded**, because each extracted recommendation is linked to a Deezer item. Third, it is **bimodal in release format**, providing both a raw version that preserves Reddit discourse characteristics and a paraphrased version aimed at long-term reproducibility. Fourth, it is structurally oriented toward recommendation rather than generic music discussion: the unit of extraction is a pairing between a recommendation-seeking Reddit thread and a leaf comment containing a recommendation [2605.09120].

The Deezer linkage is central. By grounding conversational targets to Deezer IDs rather than volatile web links, the resource supports retrieval and generation over a durable music catalog and enables item-side representations based on **30-second audio previews** and structured metadata. This is a material distinction from prior YouTube-grounded resources because the paper explicitly motivates Deezer IDs as being **less deletion-prone than YouTube links** and as providing richer metadata for future **content-grounded** and **multimodal** CMR [2605.09120].

## 2. Corpus construction from Reddit

The Reddit side of the dataset is not based on a narrow hand-picked set of communities. The crawl begins from **`r/Music/wiki/musicsubreddits`**, a community-maintained index containing **695 music-related subreddits**. After removing subreddits that were unreachable at crawl time and manually excluding communities centered on music production, industry news, self-promotion, hardware, and other topics not focused on music discovery or recommendation, the final crawl spans **200 subreddits**. Data are collected through the **arctic-shift archive API** over the period from **January 2008 to mid-April 2026** [2605.09120].

The filtering pipeline has both structural and semantic stages. Structurally, the corpus excludes posts with no comments and comments whose parent post is missing. It then removes posts and comments whose body is under **5 characters** after whitespace stripping, removes comments with a **negative Reddit score**, and filters broad-opinion prompt threads whose titles contain terms such as **“favorite” / “favourite”** or **“your opinion”**. The stated rationale is that such threads invite diffuse opinion aggregation rather than focused recommendation exchanges grounded in a specific need [2605.09120].

Semantic extraction is performed with **Qwen3.6-35B-A3B-FP8** in two stages. At the thread level, the model determines whether a post is **explicitly seeking music recommendation** through cues such as taste, mood, context, reference tracks, or criteria like era and genre. At the comment level, the model determines whether a comment recommends a specific music item and, if so, extracts a structured tuple **\{artist, title, type\}**. Both stages produce a self-reported confidence score in \([0,1]\), and the paper retains only predictions above **0.95** [2605.09120].

Deduplication operates twice: first within subreddits, then across subreddits. In both cases, duplicates are collapsed by **normalized title** and **user**. Importantly, duplicates are not simply discarded. Comments are **unioned**, and the retained record stores the **post IDs of absorbed duplicates**, preserving traceability to source material [2605.09120].

## 3. Deezer grounding and the two released versions

Once the LLM has extracted **artist** and **title**, the item is grounded to Deezer through the **Deezer API**. A recommendation is linked only when **both artist name and title match case-insensitively**. The extracted `type` distinguishes at least **track** versus **album**, which matters because Reddit2Deezer contains substantial album-level recommendation rather than being exclusively track-centered [2605.09120].

The paper gives four explicit reasons for choosing Deezer as the grounding target: **no API key required to download audio previews**, identifiers that are **less deletion-prone than YouTube links**, **rich metadata availability**, and better support for future **content-grounded** and **multimodal** recommendation. The accessible metadata include **audio previews**, **release date**, **track length / duration**, **artist popularity**, **track popularity**, **album popularity**, **BPM**, and **genre tags**; experimental text renderings also use **gain** and an **explicit-lyrics flag** [2605.09120].

The resource is released in two forms. The **raw** version preserves Reddit structure and language as directly as possible, thereby maximizing authenticity. The **paraphrased** version is created with Qwen3.6-35B-A3B-FP8 under explicit rules: preserve every music-relevant detail verbatim, do not alter artist names, do not alter track or album titles, resolve relative time references using the post year, strip or substitute overt personal information, and restyle the exchange as a one-on-one music recommendation chat. The paraphrasing prompt includes **9 human-written examples** and has a total length of **10,336 words** [2605.09120].

A consequential design choice is that the paraphrased release emits **one paraphrase per \{thread, leaf-comment, verified-item\} triple**, rather than one paraphrase per source comment. As a result, the paraphrased set expands the number of conversations while reducing the number of items per recommender turn to essentially one. This converts many raw set-valued recommendation turns into single-item supervised instances [2605.09120].

| Metric | Raw | Paraphrased |
|---|---:|---:|
| Conversations | 186,380 | 535,592 |
| Unique thread IDs | 41,286 | 41,251 |
| Average recommendation turns | 1.01 | 1.02 |
| Average items per turn | 1.51 | 1.00 |
| Unique artists | 42,497 | 42,393 |
| Unique track IDs | 100,832 | 100,439 |
| Unique album IDs | 29,178 | 28,911 |

The paper reports the relative change as
$$
\Delta \% = \frac{\text{paraphrased} - \text{raw}}{\text{raw}} \times 100,
$$
yielding **+187.3%** for conversations and **-33.7%** for average items per turn, while unique prompts and unique catalog items decline only slightly. The authors attribute the small residual loss in unique prompts and items to occasional failures of the LLM to emit a parsable artist-title pair in paraphrasing [2605.09120].

## 4. Quality control, validation, and known limitations

Because the resource is automatically constructed, the paper includes a targeted human validation protocol. One author first screened **100 random raw samples** and observed **3** negative cases for conversation validity and **2** for item mapping validity. Using an assumed prevalence \(p = 0.05\), a **95%** confidence level, \(Z = 1.96\), and margin of error \(e = 0.05\), the study applies Cochran’s formula
$$
n = \frac{Z^2 p(1-p)}{e^2}
$$
and derives a minimum sample size of **73**. Two non-author annotators then independently labeled **73 randomly sampled raw-paraphrased pairs** matched by \(\{thread\_id, leaf\_id\}\) [2605.09120].

Three annotation axes are used. The first is whether the exchange is a genuine music recommendation conversation. The second is whether the Deezer identifier is correctly mapped to the recommended track or album. The third is a paraphrase faithfulness score on a **0–5** scale. For raw conversations, annotator 1 judged **71/73** valid and annotator 2 judged **72/73** valid; for paraphrased conversations, both judged **73/73** valid. The agreement rate is **99.3%** with **Cohen’s \(\kappa = 0.66\)**. Deezer identifier mapping correctness averages **93.84%**, with **95.89% agreement** and **Cohen’s \(\kappa = 0.65\)**. Mean paraphrase faithfulness is **\(4.84 \pm 0.21\)** for annotator 1 and **\(4.82 \pm 0.33\)** for annotator 2, with **Quadratic Weighted Kappa = 0.30** [2605.09120].

The paper also identifies characteristic failure modes. Mapping errors typically involve **remix versus canonical version**, **remastered version**, **live version**, or **instrumental version** mismatches. Disagreements often reflect whether a live or instrumental version should count as an acceptable grounding of the recommendation [2605.09120].

Several limitations are explicit. Reddit2Deezer is **strongly single-turn dominant**: the raw release contains **184,048 single-turn conversations**, and the paper notes that there are only about **2k multi-turn conversations**. The source is Reddit, so demographic and discourse biases intrinsic to Reddit likely remain, although they are not quantified. Subreddit coverage depends on the `r/Music` wiki index plus manual filtering. The raw/paraphrased dual release mitigates but does not eliminate the authenticity-versus-reproducibility tradeoff: raw preserves platform-specific discourse, whereas paraphrasing yields cleaner and more portable supervision at the cost of slight target loss [2605.09120].

## 5. Benchmark formulation and empirical findings

The benchmark task is to predict the recommended music item from the conversation context preceding the recommendation, searching over the full Deezer-grounded catalog of **130,010 items**, consisting of **100,832 tracks** and **29,178 albums**. Evaluation is chronological, with **train** conversations before **August 2025**, **validation** conversations from **August 2025 to January 2026**, and **test** conversations after **January 2026**. Metrics are **Hit@k**, **Recall@k**, and **nDCG@k** for \(k \in \{1,5,20\}\), with all values in the main result table multiplied by **100** [2605.09120].

The retrieval setup uses **CLAP**. In **CLAP-Audio**, each catalog item is represented by its Deezer preview embedding; item audio is chunked into **3 × 10 second windows**, pooled, and re-normalized. In **CLAP-Text**, the item is represented by a textual rendering of Deezer metadata. Ranking is by cosine similarity between the conversation embedding and the item embedding. The generative setup uses **Qwen3.5-2B**, either zero-shot or fine-tuned. Fine-tuning employs **LoRA** with rank \(r = 16\), \(\alpha = 32\), dropout \(= 0.05\), **AdamW**, learning rate \(2 \times 10^{-4}\), a cosine schedule, **3% linear warmup**, effective batch size **256**, and maximum sequence length **512**. For raw training, the tokenizer is augmented with `<subreddit>`, `<title>`, and `<body>`; these markers are omitted for paraphrased training because the paraphrases do not preserve Reddit title/body structure [2605.09120].

The benchmark is deliberately difficult. On the **raw** test set, **CLAP-Audio** reaches **H@20 = 0.62**, **R@20 = 0.31**, and **N@20 = 0.15**. Zero-shot **Qwen3.5-2B** improves to **H@20 = 2.39**, **R@20 = 0.65**, and **N@20 = 0.52**. Fine-tuned **FT-Raw** reaches **H@20 = 17.67**, **R@20 = 3.93**, and **N@20 = 3.23** on the raw test set, while **FT-Para** reaches **H@20 = 17.36**, **R@20 = 4.00**, and **N@20 = 3.28** on the paraphrased test set [2605.09120].

Several comparative conclusions are explicitly drawn in the paper. **CLAP-Audio** outperforms **CLAP-Text** across nearly every setting, indicating that direct acoustic grounding is more discriminative than metadata-text grounding for this task. Zero-shot Qwen substantially outperforms the CLAP retrieval baselines despite receiving no task-specific training. Fine-tuning helps markedly: **FT-Raw** is best on the raw test set, and **FT-Para** is best on the paraphrased test set. The paper also notes asymmetric cross-style transfer: **FT-Para** transfers to raw better than **FT-Raw** transfers to paraphrased, which the authors interpret as evidence that paraphrase training encourages more platform-agnostic conversational semantics [2605.09120].

Although multi-turn coverage is sparse, the paper reports a turn-position analysis using **nDCG@5** with per-turn sample sizes of **1220** for turn 1, **81** for turn 2, and **2** for turn 3. The observed trend is that performance improves as additional turns accumulate, though the turn-3 estimate is extremely unstable because it is supported by only two conversations [2605.09120].

## 6. Position within Reddit–Deezer research

Reddit2Deezer occupies a specific niche within a broader research stack linking online discourse, catalog grounding, recommendation, and presentation. It differs sharply from **“Muse-it”** [2509.20228], which is a query-centric Reddit analysis pipeline with four modules—Reddit retrieval, NLP metadata generation, topic clustering and visualization, and track metadata extraction—but whose music-link layer is currently **Spotify-specific**, based on explicit Spotify URL filtering, **SpotDL**, and **Spotify URI** foreign-key linkage. By contrast, Reddit2Deezer does not depend on explicit music hyperlinks in Reddit posts; it extracts recommendation items from conversational text and grounds them through artist-title matching to the Deezer API [2509.20228][2605.09120].

On the Deezer side, several production papers define plausible downstream consumers of Reddit2Deezer-style supervision. **“A Scalable Framework for Automatic Playlist Continuation on Music Streaming Services”** [2304.09061] formalizes a **represent-then-aggregate** APC architecture in which song embeddings are precomputed offline, playlist or seed embeddings are aggregated online, and ranking is done by inner product. **“Track Mix Generation on Music Streaming Services using Transformers”** [2307.03045] shows a production **track-to-playlist** expansion system in which a clicked seed track is treated as a one-track playlist and expanded into a **40-track mix playlist** via a decoder-only Transformer and nearest-neighbor retrieval. **“A Semi-Personalized System for User Cold Start Recommendation on Music Streaming Apps”** [2106.03819] demonstrates a Deezer cold-start pipeline that maps heterogeneous side information into an existing music latent space and then stabilizes output by assigning the user to one of **1000** warm-user clusters. **“Transformers Meet ACT-R: Repeat-Aware and Sequential Listening Session Recommendation”** [2408.16578] adds a repeat-aware Deezer-side session model and publicly releases an anonymized Deezer listening dataset. These works are not Reddit-grounded, but they define operational back ends for seed expansion, cold-start inference, and repeat-aware session continuation once a conversational signal has been resolved into catalog entities [2304.09061][2307.03045][2106.03819][2408.16578].

A separate Deezer line concerns presentation and affordance rather than retrieval. **“Music Playlist Captioning at Scale with Large Language Models”** [2606.22460] describes an offline caption-generation system deployed on Deezer in 2025 that generates short multilingual titles for approximately **5,000 artist clusters** using **Gemini 2.0 Flash**, with reported gains of **+24.9%** in adoption, **+16.9%** in reconnection, and **+11.5%** in satisfaction. **“Tracing Affordance and Item Adoption on Music Streaming Platforms”** [2109.03538] shows that Deezer users differ materially in how they adopt **organic**, **algorithmic**, and **editorial** affordances, and in whether items first encountered through recommendation-like channels later become part of organic listening. A plausible implication is that Reddit2Deezer is best understood not as a standalone recommender, but as a reality-grounded supervision layer that can be coupled to candidate generation, cold-start mapping, session modeling, and presentation modules whose effects on downstream adoption may vary strongly across user types [2606.22460][2109.03538].

In that sense, Reddit2Deezer is neither a universal music entity linker nor a complete end-to-end recommendation product. It is a large-scale, catalog-grounded dataset and benchmark for authentic conversational music recommendation, with enough scale to train modern models and enough item grounding to support audio-aware, metadata-aware, and downstream Deezer-integrated research [2605.09120].

Source: https://www.emergentmind.com/topics/reddit2deezer