Reddit2Deezer: Music Recommendation Dataset

• Reddit2Deezer is a conversational music recommendation dataset derived from Reddit exchanges, grounded in Deezer catalog metadata.
• It contains 190k unique thread-comment pairs available in raw and paraphrased forms, mapped to over 130k musical items.
• The resource bridges authentic social interactions with scalable music metadata, enabling robust benchmarks and multimodal recommendation research.

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 (Kim et al., 9 May 2026). 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 (Kim et al., 9 May 2026).

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 (Kim et al., 9 May 2026).

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 (Kim et al., 9 May 2026).

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 (Kim et al., 9 May 2026).

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 (Kim et al., 9 May 2026).

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 (Kim et al., 9 May 2026).

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 (Kim et al., 9 May 2026).

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 (Kim et al., 9 May 2026).

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 (Kim et al., 9 May 2026).

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 (Kim et al., 9 May 2026).

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 (Kim et al., 9 May 2026).

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 (Kim et al., 9 May 2026).

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 (Kim et al., 9 May 2026).

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 $\{thread\_id, leaf\_id\}$ (Kim et al., 9 May 2026).

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 $$\Delta \% = \frac{\text{paraphrased} - \text{raw}}{\text{raw}} \times 100,$$0 for annotator 2, with Quadratic Weighted Kappa = 0.30 (Kim et al., 9 May 2026).

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 (Kim et al., 9 May 2026).

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 (Kim et al., 9 May 2026).

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 $$\Delta \% = \frac{\text{paraphrased} - \text{raw}}{\text{raw}} \times 100,$$1, with all values in the main result table multiplied by 100 (Kim et al., 9 May 2026).

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 $$\Delta \% = \frac{\text{paraphrased} - \text{raw}}{\text{raw}} \times 100,$$2, $$\Delta \% = \frac{\text{paraphrased} - \text{raw}}{\text{raw}} \times 100,$$3, dropout $$\Delta \% = \frac{\text{paraphrased} - \text{raw}}{\text{raw}} \times 100,$$4, AdamW, learning rate $$\Delta \% = \frac{\text{paraphrased} - \text{raw}}{\text{raw}} \times 100,$$5, 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 (Kim et al., 9 May 2026).

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 (Kim et al., 9 May 2026).

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 (Kim et al., 9 May 2026).

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 (Kim et al., 9 May 2026).

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” (Agarwala et al., 24 Sep 2025), 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 (Agarwala et al., 24 Sep 2025, Kim et al., 9 May 2026).

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” (Bendada et al., 2023) 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” (Bendada et al., 2023) 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” (Briand et al., 2021) 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” (Tran et al., 2024) 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 (Bendada et al., 2023, Bendada et al., 2023, Briand et al., 2021, Tran et al., 2024).

A separate Deezer line concerns presentation and affordance rather than retrieval. “Music Playlist Captioning at Scale with LLMs” (Delcluze et al., 21 Jun 2026) 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” (Shakespeare et al., 2021) 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 (Delcluze et al., 21 Jun 2026, Shakespeare et al., 2021).

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 (Kim et al., 9 May 2026).