Title: Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists
Venue: NeurIPS 2024
Accepted: yes
Keywords: collective action

platform power

sequential recommender systems

transformer models

music recommendation
Abstract: We investigate algorithmic collective action in transformer-based recommender systems. Our use case is a collective of fans aiming to promote the visibility of an underrepresented artist by strategically placing one of their songs in the existing playlists they control. We introduce two easily implementable strategies to select the position at which to insert the song and boost recommendations at test time. The strategies exploit statistical properties of the learner to leverage discontinuities in the recommendations, and the long-tail nature of song distributions. We evaluate the efficacy of our strategies using a publicly available recommender system model released by a major music streaming platform. Our findings reveal that even small collectives (controlling less than 0.01\% of the training data) can achieve up to $40\times$ more test time recommendations than songs with similar training set occurrences, on average. Focusing on the externalities of the strategy, we find that the recommendations of other songs are largely preserved, and the newly gained recommendations are distributed across various artists. Together, our findings demonstrate how carefully designed collective action strategies can be effective while not necessarily being adversarial.

Algorithmic Collective Action in Recommender Systems: Promoting Songs by Reordering Playlists

Abstract

We investigate algorithmic collective action in transformer-based recommender systems. Our use case is a collective of fans aiming to promote the visibility of an underrepresented artist by strategically placing one of their songs in the existing playlists they control. We introduce two easily implementable strategies to select the position at which to insert the song and boost recommendations at test time. The strategies exploit statistical properties of the learner to leverage discontinuities in the recommendations, and the long-tail nature of song distributions. We evaluate the efficacy of our strategies using a publicly available recommender system model released by a major music streaming platform. Our findings reveal that even small collectives (controlling less than 0.01% of the training data) can achieve up to 40 × more test time recommendations than songs with similar training set occurrences, on average. Focusing on the externalities of the strategy, we find that the recommendations of other songs are largely preserved, and the newly gained recommendations are distributed across various artists. Together, our findings demonstrate how carefully designed collective action strategies can be effective while not necessarily being adversarial.

1 Introduction

In the ever-evolving landscape of music discovery, the challenge of accessing and sifting through the overwhelming number of tracks released daily has become increasingly difficult. This has resulted in a strong dependence on platforms like Spotify, Deezer, and Apple Music, which distribute and promote music through algorithmic song recommendations. These systems rely on historical data to learn user preferences and predict future content consumption [21, 51, 34, 7, 6].

It has been widely documented that music recommendation systems suffer from popularity bias as they tend to concentrate recommendation exposure on a limited fraction of artists, often overlooking new and emerging talent [35, 4, 2, 15, 9, 28]. As the success and visibility of artists are deeply influenced by the algorithms of these platforms, this can lead to a considerable imbalance in the music industry [1, 41] and reinforce existing inequalities [50]. Thus, artists have started to fight for more transparency and fairer payments from online streaming services. The 'Justice at Spotify' campaign, launched by the Union of Musicians and Allied Workers [54], has been signed by more than 28,000 artists. At the same time, the International Society for Music Information Retrieval has been arguing for promoting the discovery of less popular artists by recommending 'long-tail' items [3], as have other researchers [11, 53, 16, 40].

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In this work, we explore algorithmic collective action as an alternative means for emerging artists to gain exposure in machine learning-powered recommender systems by mobilizing their fan base. Algorithmic collective action [24] refers to the coordinated effort of a group of platform participants who strategically report the part of the training data they control to influence prediction outcomes. Our work is situated in an emerging literature that recognizes data as a powerful lever for users to promote their interests on digital platforms [57, 24].

1.1 Our work

We study algorithmic collective action in transformer-based recommender systems. As a case study, we consider the task of automatic playlist continuation (APC), which is at the heart of many music streaming services. APC models take a seed playlist (an ordered list of unique songs) as input and recommend songs to follow. They are trained on the universe of playlists stored on the platform. The collective consists of platform users who can modify the subset of playlists they own. The goal of the collective is to promote a less popular artist by increasing the recommendations of their songs at test time. To this end, we consider collective action strategies where participants of the collective agree on a target song s ∗ to strategically place in their playlists.

We motivate and discuss two strategies to choose the position of s ∗ within any given playlist. Both strategies are derived from a statistical optimality assumption on the recommender and do not require knowledge of the specifics of the model architecture or the model weights. Instead, they use that the model is trained to fit sequential patterns in existing data and build on aggregate song statistics that are feasible to gather from public information. We empirically test our strategies using an industry-scale APC model that has been deployed to provide recommendations for millions of users on Deezer-one of the biggest streaming platforms in the world. To train the model, we use the Spotify Million Playlist Dataset, treating each playlist as a user and randomly sampling a fraction to compose the collective.

Wefind that by strategically choosing the position of the target song, collectives can achieve significant over-representation at test time, see Figure 1 for a teaser. We experiment with collectives composed of a random sample of users owning between 0 . 001% and 2% of the training data instances. Interestingly, even tiny user collectives, controlling as few as 60 playlists, can achieve an amplification of up to 25 × , referring to the song's recommendation frequency relative to the training frequency. This is 40 × more than an average song occurring at the same frequency in the training data. In contrast, placing the song in a fixed position in every playlist is largely ineffective.

Our strategy satisfies a strict authenticity constraint and thus preserves user experience at training time by design. Interestingly, we find that also at test time recommendations are largely preserved; not only on aggregate but also for members of the collective. As a consequence, the strategies come with small externalities for users, and at the same time, they also have a relatively small effect on model performance. For large collectives controlling > 3% of the playlists, the effect corresponds to every target

song recommendation replacing an otherwise relevant song in less than 15% of the cases, leaving other recommendations unaltered. Thus, in the hypothetical case where the promoted song is indeed relevant, this could lead to an overall gain in more than 85% of the cases, even though the total number of test-time recommendations is fixed. Lastly, we show that the newly gained recommendations are taken from artists of diverse popularity without any indication that a specific artist suffers disproportionally.

Taken together, our work demonstrates a first example of collective action in sequential recommender systems. We show how collective action goals can be achieved while largely preserving service quality and user experience. The feasibility of such strategies raises many interesting questions, challenges, and opportunities for future work.

1.2 Related work

The fairness of recommendation systems on online platforms remains a pressing issue for both content consumers and producers [10, 32, 19, 26]-see Zehlike et al. [61] for a detailed overview. Several recent works study individual strategic users attempting to influence their own recommendations [5, 25, 12, 13]. Other works consider adding antidote data to fight polarization and unfairness [42, 18].

Beyond recommender systems, a related line of work centers the users in the study of machine learning systems. Vincent and Hecht [55] call for conscious data contribution , Vincent et al. [56] discuss data strikes, and Vincent et al. [57] emphasize the potential of data levers as a means to gain back power over platforms. Hardt et al. [24] introduce the framework of algorithmic collective action for formally studying coordinated strategies of users against algorithmic systems. They empirically demonstrate the effectiveness of collective action in correlating a signal function with a target label. Sigg et al. [46] inspect collective action at inference time in combinatorial systems. Complementing these findings, we demonstrate that collective action can be effective even without control over samples at inference time. We highlight a so far understudied dimension of algorithmic collective action by discussing and illuminating the externalities of algorithmic collective action strategies.

At a technical level, our findings most closely relate to shilling attacks , or more broadly, data poisoning attacks [c.f., 49]. Shilling attacks are usually realized by injecting fake user profiles and ratings in order to push the predictions of some targeted items [45, 47]. Due to the fraudulent nature of these attacks, there are little design restrictions on the profiles, and they often come with considerable negative effects for the firm [38, 20]. Data poisoning attacks in recommender systems predominantly focus on collaborative filtering-based models, with a few exceptions; Zhang et al. [62] propose a reinforcement learning-based framework to promote a target item, Yue et al. [58] provide a solution to extract a black-box model's weights through API queries to then generate fake users for promoting an item, and Yue et al. [59] propose injecting fake items into seemingly real item sequences (at inference time and without retraining) with a gradient-guided algorithm, requiring full access to the model weights. Taking the perspective of collective action, we focus on easy-to-implement strategies that require minimal knowledge of the model and operate under an authenticity constraint to preserve the utility of altered playlists while seamlessly integrating into natural interaction with the platform.

Further, our work pertains to a broader scholarly literature interested in improving labor conditions for gig workers on digital platforms [e.g., 29, 52], optimizing long-term social welfare in online systems [33], and understanding dynamics in digital marketplaces [27]. The type of strategic data modification we consider falls under the umbrella of adversarial feature feedback loops [39]. Taking advantage of collective strategies to change model outcomes more broadly has been studied in tabular data [17], computer vision [43], and recently in generative AI [44].

2 Preliminaries on automatic playlist continuation

We use automatic playlist continuation (APC) as a running example of a sequential recommendation task. APC forms the backbone of major streaming platforms, such as Spotify and Deezer. To formally define the recommendation task, let S = { s 1 , ..., s n } denote the universe of songs, where n ≥ 1 denotes the number of unique songs. A playlist p is composed of an ordered list of songs selected from S without replacement. Given a seed playlist p , the firm's goal is to predict follow-up songs that the user likely listens to. We consider a topK recommender system that outputs a personalized ordered list of K ≥ 1 songs. We write Rec K ( p ) for the set of K songs recommended for a seed playlist p .

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2.1 Transformer-based recommender

Over the past years, most large platforms have shifted from relying on collaborative filtering-based models for APC to building deep learning-based recommenders that account for sequential and session-based information [21, 51, 34]. In this work, we focus on transformer-based recommender systems that posit the following structure: Each song s is mapped to a song embedding vector h s = ϕ ( s ) , where ϕ denotes the embedding function. Each playlist p = [ s 1 , s 2 , ..., s L ] is mapped to an embedding vector h p by aggregating the embeddings of the songs contained in the playlist as h p = g ( h s 1 , h s 2 , ..., h s L ) , where g is a sequence-aware attention function. We assume all playlists have length L smaller than the attention window for the purpose of exposition. At inference time, the recommendation of the next K songs for a given playlist p is determined by evaluating the similarity between the playlist seed p and all potential follow up songs s ∈ S \ p as

Then, the K songs with the largest similarity value are recommended in descending order of similarity. We denote the set of recommended songs as

The embeddings ϕ and the attention function g are parameterized by neural networks. They are trained from existing user-generated playlists in a self-supervised manner by repeatedly splitting playlists into a seed context and a target sequence and employing a contrastive loss function for training.

Statistical abstraction. We do not assume the collective has knowledge of the parameters of either ϕ or g . Instead, the design of the strategy builds on the assumption that sequential, transformer-based models are trained such that SIM( s, p ) is large for songs s that frequently follow context p in the training data, and small otherwise. This approximately is robust to nuances in hyperparameter choices or architecture design and applies to any sufficiently expressive and well trained model.

2.2 Typical imbalances in recommendations

On today's music streaming platforms, a small number of artists receive the vast majority of recommendations, while the majority receive few or none. This imbalance is illustrated by the Lorenz curve in Figure 2, which is based on recommendations derived from the Deezer model on the Spotify MPD dataset (see Section 4). The Gini coefficient measuring inequality corresponds to 0 . 87 . Streaming and radio statistics reveal an even more severe imbalance: the top 1% of newly released songs receive 99.99% of radio plays and 90% of streams go to just 1% of artists [9].

Considering Figure 1 we can also see that songs with high prevalence in the training data are recommended disproportionately often at test time compared to their training set frequency (referring to the slope of ∼ 1 . 8 of the blue point cloud). This gain in exposure through the recommender comes at the expense of many low-frequency songs that receive no recommendations at test time,

further amplifying existing imbalances. Considering Spotify's substantial power to influence song consumption among platform users [1], withholding initial exposure for these songs limits their potential to reach a broader audience, significantly impacting an artist's career. In this work, we focus on collective efforts to boost recommendations for one of these underrepresented songs.

3 Algorithmic collective action for promoting songs

We build on the framework of Hardt et al. [24] and consider collectives that are composed of a fraction α ∈ (0 , 1] of randomly sampled users on the platform. We assume each user controls a single playlist. Members of the collective can strategically manipulate their playlists. We use µ ( · ) to describe the strategy of mapping an original playlist to a modified playlist.

Success and amplification. Let P 0 denote the distribution over playlists. The goal of the collective is to increase the exposure of a target song s ∗ for a randomly sampled playlist from P 0 at test time. We measure the success of collective action as

The recommender system Rec K is trained on a partially manipulated training dataset D , composed of N samples from P 0 , among which αN have been transformed under µ .

We are particularly interested in measuring the effectiveness of a strategy relative to the effort of the collective. Therefore, we define amplification (Amp) as the fraction of newly gained target recommendations at test time divided by the fraction of manipulated playlists in the training set:

An amplification of 0 means that the strategy is ineffective, an amplification of 1 means that the song frequency in the training set is proportionally represented in the model's predictions, and an amplification larger than 1 means that collective action achieves a disproportionate influence on the recommender. In the following, we choose a song s ∗ that does not currently appear in the training data, hence S (0) = 0 .

3.1 Authenticity constraint

Participants of the collective are users of the platform. We design collective action strategies under the following authenticity constraint, not to compromise user experience:

Definition 1 (Authenticity constraint) . We say a strategy µ : p → p ′ is authentic iff the Levenshtein distance between p and p ′ = µ ( p ) satisfies Lev( p, p ′ ) ≤ 1 for any p .

The Levhenstein distance [30], also known as edit distance in information theory, counts the number of operations needed to transform one sequence into another. The song insertion strategy we propose in this work is one concrete instantiation of µ that satisfies this constraint. More specifically, our strategy consists of inserting an agreed-upon target song s ∗ at a specific position in every playlist p . In contrast, existing adversarial strategies typically perform larger modifications to playlists and would not satisfy this constraint [62, 58].

3.2 Algorithmic lever

The algorithmic lever of the collective is to strategically choose, for each playlist, the position i ∗ at which to insert the target song. Under our probabilistic assumption about the learner, strategically placing s ∗ after p means that the similarity between p and s ∗ is increased. Thus, by choosing the index i ∗ , the collective targets context p -i ∗ referring to the sequence of songs s j in p up to index j = i . Recall that the collective aims to be among the top K songs with high frequency over a randomly sampled context p at test time. Our strategies exploit two different algorithmic levers towards this goal: Indirectly targeting Clusters of similar contexts ( InClust ) or Directly targeting Low-Frequency contexts ( DirLoF ). Pseudocode for the two strategies can be found in Figure 3.

Strategies: (a) InClust and (b) DirLoF

Input: s ∗ , collectively owned playlists D ∗ = { p 1 , p 2 , ..., p n } ⊆ D

2: (a) r s ← for every s in D ∗ pool information to count song frequencies in D ∗ .

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5: Define anchor s 0 : find song s 0 ∈ p such that (a) r s 0 ≥ r s ∀ s ∈ p or (b) q s 0 ≤ q s ∀ s ∈ p

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Concentrating effort. Inclusion in the set Rec K ( p ) leads to a song's recommendation for context p at test time. In turn, being ranked in position K +1 does not yield any recommendations. Instead, the probability mass in the tails is reallocated to the top K songs at test time. This discontinuity can be exploited by the members of the collective to target specific contexts in a coordinated fashion to increase the likelihood of inclusion. Compared to random song placement, the collective can increase the mass on a particular context by a factor of L . The InClust strategy implements a way for selecting contexts to target, projecting this intuition from the non-parametric setting to the embedding space of the recommender. Namely, it systematically places s ∗ directly before each occurrence of a popular song s 0 . In that way, it targets the region in the context embedding space around h s 0 in a coordinated fashion. To implement this strategy, the collective repeatedly determines the most frequent song in their playlists, places s ∗ before every occurrence of this song, and then repeats this with the remaining playlists until all of them are used.

Strategically exploiting overrepresentation. An alternative lever the collective has available is to strategically target contexts that are overrepresented among the playlists the collective controls. Meaning that the frequency of the context among the playlists owned by the collective is larger than the overall frequency in the training data due to finite sample artifacts. The DirLoF strategy aims to identify such contexts by targeting infrequently occurring songs and exploiting the long-tail nature of the overall song frequency distribution (see Figure 2). The core intuition is that if they manage to target low-frequency contexts, a single song placement might be sufficient to overpower existing signals. To identify low-frequency contexts, the collective uses the frequency of the last song as a proxy. For each playlist, it selects the anchor songs s 0 with the smallest overall song frequency and places s ∗ right after s 0 .

3.3 Obtaining song statistics

The InClust strategy targets high-frequency contexts, whereas the DirLoF strategy targets lowfrequency contexts. However, there is an important difference when implementing the strategies. InClust can be implemented from only statistics obtained from the songs in playlists the collective owns; all it requires is participants to set up infrastructure for pooling this information, either through an app, an online service, or other means. In contrast, to effectively implement the DirLoF strategy, the collective needs statistics about the full training data to identify the songs that are least popular overall. However, they typically do not have direct access to this information. Instead, as a proxy, they can leverage publicly available user-generated playlists, which are often accessible through official APIs (e.g., Spotify). Additionally, scraping external data sources can provide supplementary information. We evaluate the use of scraped song streams in Section 4.2.

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4 Empirical evaluation

We evaluate our collective action strategies against a public version of Deezer's transformers-based APC solution that 'has been deployed to all users' [7, p. 472]. To train the model, we use the Spotify Million Playlist Dataset (MPD), which is currently the largest public dataset for APC [14]. It contains one million playlists generated by US Spotify users between 2010 and 2017, with an average length of 66.35 tracks from nearly 300,000 unique artists.

Model training and evaluation. Weuse the standard methodologies used in APC for model training and testing. 2 We start by randomly selecting 20,000 playlists to build a test and validation set of equal sizes. The remaining 980,000 playlists are used for training the model. The collective intervenes by strategically modifying an α fraction of the playlists composing the training and validation set. We consider collectives of size α ∈ [0 . 00001 , 0 . 02] which corresponds to 10 to 20000 playlists. For evaluation on the test set, every playlist p is split into a seed context and a masked end. The length of the seed context is chosen randomly in [1 , 10] for each playlist and models are evaluated by comparing the model's recommendations based on the seed playlist to the masked ground truth. We employ five-fold cross-validation, using different random seeds for sampling the playlists designated for training, validation, and testing, as well as for selecting the subset controlled by the collective. We use bootstrapped 95% confidence intervals (CI) over folds when reporting results.

Baselines. We consider four baseline strategies to compare with our collective strategies, each performing the same number of target song insertions. The Random strategy inserts s ∗ at a Random position in the playlist, Insert@ i inserts the song always at position i in every playlist, the AtTheEnd strategy places s ∗ as the last song of the playlist, and Random@ i -j inserts s ∗ at a random position between indices i and j . Unlike our collective strategies, these baselines do not require coordination among participants beyond the shared goal of promoting s ∗ .

4.1 Success of collective action

We start by evaluating the success of the proposed strategies, assuming full information about song frequencies in the training set to illustrate the potential. In Figure 4, we plot the amplification for different α . In particular, we observe that strategic song placement allows very small collectives ( α ≤ 0 . 1% ) to be successful, whereas Random or fixed placement of s ∗ is ineffective.

For α = 0 . 025% , the DirLoF strategy achieves amplification of up to 25 . In contrast to an average song that naturally occurs in 0 . 025% of the playlists, the number of recommendations is 40 × larger, as these low-frequency songs are typically ignored by the recommender. This suggests that collective action could make a tremendous difference for these artists: suppose an artist's song is streamed

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10,000 times, yielding a revenue of $40 at a royalty rate of $0 . 004 per stream [31]; an amplification of 25 would hypothetically increase this revenue to $1 , 000 . While this example is purely illustrative (as actual royalties depend on the platform and payment model used), it emphasizes the link between recommendations and potential revenue.

For collective sizes of α ≥ 0 . 1% the InClust starts being effective, as it has enough mass to effectively compete with existing signals associated with a cluster of similar context embeddings. As the strategy can target several such clusters at the same time, amplification increases with α though with diminishing returns, achieving Amp = 10 for α ≈ 2% . From Figure 1, we can see that in the regime of 2% training data frequency, a typical song enjoys an amplification of 1 . 8 .

We also observe that the success of the random strategy increases with the collective size. This implies that even minimal coordination, in which members agree to all insert the same song s ∗ , independent of the playlist they own, can already lead to significant amplification. Amplification values for the other baselines inserting s ∗ at a fixed position are all close to 0 (see Table 1 in Appendix C.4).

Robustness to hyperparameters. Our strategies are designed based on a statistical intuition of sequential generation and should not be sensitive to specifics of the model architecture. We demonstrate the robustness with additional experiments where we vary the hyperparameters of the model (see Table 2 in Appendix C.5). However, the design of our strategies relies on the assumption that the model approximates the conditional probabilities in the training data sufficiently well. Accordingly, the effectiveness of the strategy decreases if model training is stopped early (see Table 3).

Hybrid strategy. Building on these observations, we construct a hybrid strategy that interleaves the two approaches by first using InClust to target indirect anchors that appear at least λ times in the collective and then deviates to DirLoF for playlists where no such anchor is present (we use λ = 10 ). This corresponds to the dashed line in Figure 4. We come back to this strategy in Section 4.3.

4.2 DirLoF strategy with approximate song statistics

The DirLoF strategy critically relies on training data song frequency estimates to determine the low-frequency anchor songs. We investigate the strategy's success with partially available song information in Figure 5. We find that if a collective of size α = 1% has access to 1% of the remaining training data they do not control, they can already achieve ≈ 30% of the amplification in the full information setting, with 10% of the data, it is > 50% of the achievable amplification.

By default, user-generated playlists on streaming platforms are often publicly accessible, enabling researchers to gather song frequency data through API calls. However, the amount of training data that can be aggregated is limited by the platform's API rate limits. Alternatively, proxy statistics can be used to increase the fraction of songs for which estimates are available. To illustrate the feasibility of this approach, we implemented a scraper to obtain current stream counts from Spotify. Although these counts are visible in the Spotify browser version, they are not accessible through the Spotify API. The scraped data reflects song popularity as of 2024, which is not ideal given our experiment relies on the much older Spotify MPD dataset (collected between 2010 and 2017). Nonetheless, these counts serve as effective proxies, as Figure 5 impressively shows.

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Despite the temporal gap, a collective of size α = 1% can achieve over 85% of the amplification achievable in a full-information setting simply by using 2024 stream data to approximate past popularity levels. Even a smaller collective of α = 0 . 1% can reach about 50% of the amplification seen in the full-information scenario. In practice, scraped stream counts are likely to be more accurate proxies, as models are typically trained on more recent data. However, within the scope of our study, it remains impossible to access historical stream counts that would reflect popularity as of the time the playlists were originally generated. Thus our proof of concept should be seen as a lower bound.

4.3 Internalities and externalities of algorithmic collective action

We now inspect the effect of our strategies on other participants in the system, including the firm, other artists, and the members of the collective. For this investigation, we focus on the hybrid strategy.

First, we gauge the impact of collective action on the firm. This helps us understand the overall quality degradation of the service and the incentives of the firm to protect against collective action. We compare the performance under a recommender trained on the clean data and a recommender trained on the manipulated data. Figure 6 shows the corresponding loss in performance due to collective action for three different evaluation metrics. We find that our strategy (solid lines) only affects the recommender's performance marginally. We also show a conservative adversarial baseline (dashed lines), which simulates a scenario where successful collective action results in the first relevant item in playlist recommendations being replaced by the target song while leaving other recommendations unaltered. The considerably larger performance loss of this baseline indicates that our strategy only rarely affects relevant songs. Finally, as a thought experiment, consider s ∗ as a relevant recommendation (dotted lines). Then, collective action even enhances the system's performance. This reference is meant to illustrate an optimistic scenario where collective action helps the recommender detect underrepresented but emerging and popular artists.

Second, we inspect the effect of collective action on other artists. To this end, Figure 7 depicts the change in recommendations for individual songs of different popularity. Songs are binned by frequency and the bars indicate variation across songs. The star shows the target song s ∗ , and the corresponding increase in recommendations. We see that recommendations replaced by the target song seem to span songs of all popularity levels. In particular, our strategy does not harm specific songs or artists disproportionally and, as intended, has by far the largest effect on the targeted song s ∗ .

Finally, we focus on the experience for participants who listen to the playlists. At training time our strategies are designed to only ask for minimal modifications with the goal to preserve user experience for members of the collective. We envision this to be an important factor for incentivizing participation in practice. Non-participating individuals are not affected at this stage. At test time, we find that user recommendations are largely preserved for both participating and non-participating individuals. More precisely, participating in collective action does not deteriorate the fraction of relevant songs participants get recommended, i.e., performance remains equivalent across all three recommendation quality metrics (see Figure 13 in Appendix C.6).

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5 Conclusion

This work studies how collective action strategies can empower participants to exert a targeted influence on platform-deployed algorithms. By experimenting with an industry-scale transformerbased APC model, we demonstrate how strategically inserting a single song within randomly sampled playlists in the training data, can effectively increase recommendations of that song. Intriguingly, we find that the strategy only minimally interferes with service quality, and the recommendations for other users on the platform are largely preserved.

The proposed concept of participating in collective action to steer recommender system outcomes is grounded in the idea that users on online platforms should leave their digital traces more consciously. Thereby, their consumption behavior functions as a lever to reclaim some control over the data that platforms use to predict and recommend future content. Our emphasis on authenticity stands in clear contrast to adversarial machine learning techniques, which are often artificially designed and sometimes malicious in intent.

While altering a single playlist alone has little impact, the true power of algorithmic collective action lies in mobilizing a sufficiently large number of participants around a shared objective. This allows underrepresented artists to gain visibility through coordination. In our case, coordination corresponds to agreeing on a target song and an insertion procedure. The actual implementation of the strategy is possible with very limited technical skills and knowledge of the algorithm. We demonstrate how information for setting the parameters of the strategy can effectively be gathered using web scraping techniques. What we leave for future work is the actual implementation of an app to orchestrate collective action and share all the relevant information with the participants.

Our work suggests a widely unexplored design space for effective collective action strategies that differ from typical adversarial data poisoning attacks [c.f. 49, 62, 58, 59]. They can offer a powerful data lever to counter existing power imbalances [56, 57], and a community-centric approach to participatory AI [8]. Thus, understanding the role of economic power [23, 22], formalizing incentives [37], as well as quantifying long-term payoffs, dynamics, and equilibria, under collective action promises to be a fruitful direction for future work.

6 Limitations and potential for misuse

Grounding algorithmic collective action means identifying both its opportunities and challenges. The power that arises from gaining control over the learning algorithm through collective action can also be abused by individuals controlling a substantial number of playlists. Instead of collective goals, these individuals could leverage similar methods to pursue individualistic goals, creating a different incentive structure and potentially posing a risk to the system. Similarly, popular artists could use our strategy to gain additional exposure and reinforce inequalities among artists. Thus, incentive structures will crucially determine the desirability of the resulting market outcome. Designing larger-scale collective action strategies that promote fairness and equity on online platforms as well as mechanisms that disincentivize malicious use remains a crucial open question.

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A Song recommendation inequality

Figure 8 visualizes the track-level distribution of algorithmic exposure with the cumulative share of recommendations (y-axis) plotted against the percentiles of tracks (x-axis). The recommendations are derived from the Deezer model [7] on the Spotify MPD dataset [14]. More precisely, they are based on the outputs generated for a random selection of 10,000 seed playlists for testing, produced by a model that has been trained on the remainder of the dataset, without any collective action-see Section 4 for more details. Similar to the artist-based Lorenz curve in Figure 2, we observe a very high level of inequality with a Gini coefficient of 0 . 8 (measuring the gap between the line of equality and the Lorenz curve).

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All experiments were run as jobs submitted to a centralized cluster, using the open-source HTCondor job scheduler [48]. All jobs utilized the same computing resources: For data preprocessing and performing the data modifications as per a strategic collective action, 1 CPU was used with an allocated 100GB of RAM. In a subsequent step, transformer models were trained using a single NVIDIA A100-SXM4-80GB GPU. For each job, data preprocessing takes roughly 1-2 hours to complete (with coordinated strategies taking longer than uncoordinated ones). Models are trained for 18 epochs, using the optimal hyperparameters provided by Bendada et al. [7], which takes roughly 6 hours.

A total of 1195 experiments were run: We investigated 10 strategies (including 6 DirLoF strategies with varying levels of song statistics knowledge and 8 simple baselines, as well as 6 different hyperparameter configurations), across 12 collective sizes ( α ), and an additional baseline without collective action ( α = 0 ) as a reference for the main experiments. For the ablation study, 6 strategies were tested over 13 α values. Each experiment was conducted with five folds using different random seeds. This resulted in approximately 2390 CPU hours and 7170 GPU hours of total compute usage. The complete code is available at https://github.com/joebaumann/ recsys-collectiveaction .

C Additional experiments

C.1 Ablation study for InClust strategy

We perform an additional empirical investigation to provide insights into the inner workings of the strategy. In particular, the effect of strategic positioning using indirect anchors on the attention function. Recall that the recommender is trained such that SIM( s, p ) = ⟨ h s 0 , g ( p ) ⟩ is large for songs s that frequently follow context p in the training data, and small otherwise. The embedding function ϕ (described in Section 2.1) is insensitive to the song ordering within playlists, and strategic positioning will only surface on the attention function g .

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In Figure 9, we visualize the similarity scores of the songs ( s ∗ , s 0 , s 1 , and s 2 ) with the three context clusters that have been targeted. More specifically, for every anchor song s i , we use all seed contexts p s -i that have been targeted in the training data and compare the similarity score of these contexts with (yes) and without (no) collective action. This results in a distribution over scores, as visualized by the violin plot. The left and the middle panel show that the similarity scores of the anchor songs and their associated contexts are not altered for either of the two strategies ( Random and InClust ). Furthermore, as can be seen in the right panel, the InCLust strategy is very effective in getting the target song to be among the top 50 most similar tracks for the targeted contexts (corresponding to the probability mass above the purple threshold). In contrast, for the random placement that does not specifically target these contexts, s ∗ generally fails to achieve a ranking in the top 50.

Targetting multiple context clusters. The InCLust strategy is effective on all three distinct context clusters that are targeted simultaneously with a single target song s ∗ . We confirm the effectiveness of this strategy by assessing its success with respect to a test set, which contains a randomly drawn set of playlists (each split into a seed context and a ground truth) that have not been seen during training. Figure 10 shows that for any number of indirect anchors, the InClust strategy significantly outperforms the Random placement strategy. Furthermore, it also clearly shows that it is possible to effectively target multiple context clusters using the same target song. In conclusion, it is possible to compete with several context clusters simultaneously.

Overall, this ablation indicates that inserting a single song in a subset of playlists can be effective in associating s ∗ with specific contexts while preserving recommendations for songs S\ s ∗ .

C.2 Information bottleneck

Figure 11 displays the 100 targeted direct anchors for α = 0 . 01% under different levels of knowledge about the song frequencies in the training set. Less information results in the selection of more frequent songs, as the gap between the estimated probability (relative occurrences of songs in the known fraction of the dataset or estimated using external information) and the true probability (relative occurrences of songs in the entire training data) widens. This difference provides insight into the reduced amplification observed in Figure 5 for a specific value of α .

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Figure 12 illustrates the anchor song selection for three distinct strategies. The InClust strategy identifies anchors based on their prevalence within the collective, targeting songs frequently listened to by its members. Highlighted in red in the left panel of Figure 12, this method repeatedly employs indirect anchors, relying solely on internal playlist statistics without needing broader song frequency insights. Conversely, the DirLoF strategy targets a specific cluster of direct anchors (resulting in the red-colored cluster of anchors in the bottom left corner of the middle panel in Figure 12) targeting each only once. This method requires external data to ensure the disproportionately represented anchors in the collective match those less prevalent in the training data, as shown on the x-axis. The Hybrid strategy, illustrated in the right panel of Figure 12, combines these approaches, targeting

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C.4 Additional baselines

Table 1 demonstrates that simple baselines, which insert s ∗ at a fixed position, are much less effective than a random insertion. Additionally, inserting s ∗ at random within the first 10 positions of any playlist is much worse than a random insertion at any position.

C.5 Robustness of collective strategies

Table 2 shows that the DirLoF strategy is robust against hyperparameter ( hp ) changes. It consistently outperforms the Random strategy across all configurations. Finally, Table 3 illustrates the effectiveness of DirLoF and Random across different numbers of training epochs for the recommender. Notice that the effectiveness of the DirLoF strategy decreases if model training is stopped early.

C.6 Internalities and externalities of algorithmic collective action

Here we provide more details on the results presented in Section 4.3.

Metrics for model accuracy. To assess the quality of recommendations we follow Chen et al. [14] and Bendada et al. [7] in using the following three popular performance metrics: R-precision measures the fraction of recommended items present in the masked ground truth, augmented by

artist matches. The Normalized Discounted Cumulative Gain ( NDCG ) measures the ranking quality by rewarding relevant tracks placed higher in the recommendation list. The number of clicks ( #C ) quantifies how many batches of ten song recommendations (starting with top candidates) are needed to find one relevant track. The Deezer model trained on the unmanipulated data achieves comparable results to the winning solutions of the RecSys 2018 APC challenge along these metrics [60].

Impact on other artists. Unlike the partially aligned interests of the firm and the collective, the dynamics among artists differ, since boosting recommendations for one artist inevitably reduces the visibility of others. We are interested in understanding who is affected by our strategy. For the purpose of this analysis, we hold the total number of recommendations at inference time constant, making it a zero-sum game.

To complement Figure 7, in Table 4, we show the effect of collective action ( hybrid strategy with α = 1% ) on other songs. In line with the result presented in Section 4.3 (showing bins of songs with similar frequencies for just one fold), we do not find any evidence that any other songs experience a systematic change in exposure due to collective action, not even the targeted (in)direct anchor songs.

User experience of collective participants Collective participants are platform users who continue consuming content on the platform during and after performing strategic actions. To understand the price of collective action, we investigate the downstream effect on their own user experience. After all, users are likely to engage in collective action only if it does not result in significant detriment to their own content consumption experience. While a perfect measure of user satisfaction is outside the scope of this work, we utilize participant's known preferences as a basis for the experienced quality of recommendations. More precisely, for any collective participant that manipulated their playlist with h ( p ) = ( p -i ∗ , s ∗ , p + i ∗ ) , we use the targeted context ( p -i ∗ ) as a seed for the recommender and evaluate the output recommendations using the user-generated continuation of the playlist ( p + i ∗ ) as the ground truth.

We revisit the collective strategy outlined in the ablation study (Section C.1) to scrutinize the internalities of collective action. We find that the recommendations for collective participants are stable and robust under authentic strategic playlist manipulations. Figure 13 illustrates the precision scoremeasuring the similarity of recommendations for different strategies-across attacked contexts. It is around 80% on average, with little variation across strategies. This is likely attributed to inherent instabilities in the top K recommendations rather than the specifics of the strategies [36].

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper explores the possibility of boosting the recommendation of a song in an automatic playlist continuation system by using a collective strategy of adding the song into the training playlists of the APC at a specific position. The paper shows that adopting a strategy that targets low frequency context makes it possible to very significantly boost the exposition of the song in the output of the APC.\n\nSTRENGTHS:\n- Very interesting idea which is novel in the domain of music recommendation\n- Quite surprising experimental result that is worth sharing (possibility of boosting exposition of a song with the DirLof strategy).\n\nWEAKNESSES:\n- Limited scope:\n    - The idea is tested in the limited context of automatic playlist continuation and is likely not adaptable to broader applications.\n    - Only one APC model was tested, so the results may be specific to this model. It would have been a good idea to test the impact of the collective action on other models (such as the baseline proposed in the reference paper for the APC model).  Also, it’s unclear whether the effect would be robust to hyperparameter changes in the APC model.\n    - the method is tested in a static context (not in the real world), so the actual impact of the method on usage (for instance, whether a user exposed through the APC to a song boosted by the collective strategy would listen to it or not) is not tested (this would require access to the production of a streaming music service, though)\n- Very limited insights on the design of the efficient strategy (DirLof) and why it works. The presentation of the strategy is actually quite unclear, while it’s central to the paper.\n- There may be other simpler baselines that would be worth testing. The paper shows that inserting the song at the end of the playlists is less performance than inserting the song randomly, which suggests that inserting it earlier in the playlist sequence may help. So a baseline that would always insert the song at the beginning of the sequence could have decent results (the first place is likely to be avoided as it would mean that the song would never appear as the target in the training of the APC, but low positions that appear regularly as targets in the training could be considered).\n\nQUESTIONS:\n- Could the authors rephrase / clarify the definition of the DirLof strategy and rationale behind it? something which is very surprising is that “the collective selects the anchor songs s0 with the smallest overall song frequency”. But it’s likely that smallest song frequency is for song appearing only once among playlists and these song are then mostly noise in playlists and it’s hard to get why those anchors would help improving recommandation of the inserted song. Also, statistics on songs with low frequency are likely much more difficult to obtain (the statistics will be much less reliable than for popular songs).\n- It’s very unclear what the distributions over playlists are (P,  P0, P*). It seems it’s almost used as a synonym of dataset, and the expectations are actually just empirical expectations. I’d like the author to clarify this point and to explain why it’s necessary to introduce such notations.\n- In table 2, it’s unclear how statistics are estimated when there is no training data to compute them from (while there is still a significant amplification).\n\nLIMITATIONS:\n- As mentioned in the weaknesses, there are other simple baselines that would be worth testing. That would help support the claim that the specially designed DirLof strategy is actually efficient.\n- Also the authors should comment on the possible transferability of their results to other APC models, and to modifications of hyperparameters of the model.\n\nOn the ethical side, the method is a bit too much presented as a way for artists to attack a recommender system, somewhat rerank their songs and make money out of it, which has some important ethical implications in terms of fairness among artists: As the payment system of most streaming services is based on subscription (so a fixed amount of money), artificially boosting an artist comes at the detriment of other artists.\nI would then rather avoid this message of \"opportunity for artists\" in the paper (and even in the title) and turn it instead as a warning to music streaming platforms that recommender systems may lack robustness to attacks and that they should be aware of it and take action. I think presenting the paper in this second way would solve this ethical issue, but the current message is, to me, ethically borderline.\n\nMinor comments\n- The authors claim that “most large platforms have shifted from relying on collaborative-based models for APC to building deep learning based recommenders …” but 1) the references are only Spotify based 2) the references are research papers which doesn’t mean that the shift actually happened and that platforms no longer use collaborative-based models part in their complex architecture.\n\n- In equation (1):\n    - The authors likely want to specify that the recommended song shouldn’t be part of the playlist i.e S’ **∩** p = **∅** in the argmax\n    - The argmax actually corresponds to the top K elements in terms of similarity, which is quite straightforward. But the sum in the equation makes it not very clear at first read. It could be worth it to state it explicitly before the equation.\n- There are several notations that use the letter h (song embedding, playlist embedding, playlist mapping). I think using different letters may help make things clearer.\n- In Figure 3, it seems like s* is subtituted to s_i (there is no s_i in (b)) while just before equation 4, it’s said that only insertions are considered.\n- “*Existing data poisoning techniques, for example, operate in different settings, not complying with Constraint 1 and typically assuming white-box access to the model or involving test-time profile manipulations.*” ⇒ this needs references.\n- *“Thus, collective action can make a tremendous difference for these artists: suppose an artist’s song is streamed 10,000 times, yielding a revenue for the artist of $40 for royalties of $0.004 per stream [32]; an amplification of 25 would increase this revenue to $1, 000.”* I understand this is purely illustrative, but the figures won’t reflect much the truth. First 0.004$ per stream is an average that depends on the platform, but also on the kind of registration of the user and on the country of the user with quite important variations. Second, several platforms (Spotify, Deezer…) are shifting to a payment model were all streams don’t have the same value, especially recommended streams are “discounted”, and some “real” artist may get a boost. Once again, I get it’s for illustrative purpose, but it brings confusion on how music payment system works, and then should be avoided in my opinion.\n- *“Moreover, when considering s* as a relevant recommendation, collective action even enhances the system’s performance”* Isn’t this completely obvious? if presentation of s* is boosted (which happens, given the amplification), other recommendations are barely affected (as shown on figure 6, solid line), and you consider s* a valid recommendation, then for sure recommendation metrics will increase, won’t they?", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-06-19"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThey proposed a strategy for the streaming platform users to collectively act in order to promote targeted songs. The promotion efficacy is measured by the targeted songs' recommendation frequency boost at testing time. This strategy is approved to be effective through simulation experiment.  Another finding is that this strategy has minimum impact to the performance of the recommendation system as a whole, i.e., by preserving user experience to other non-targeted songs.\n\nSTRENGTHS:\n* This is a novel idea, presented in an interesting domain with a good amount of related work.\n* The writing of the paper is clear. The motivation is sound. \n* The experiment performed by the author successfully verified the efficacy of the collective strategy.\n\nWEAKNESSES:\n* Limited scope. See limitations below.\n* Lack of technical novelty and contribution. The evaluation result would be a good report but I would recommend the author to seek publication in a different conference.\n\nQUESTIONS:\nThe context selection methods discussed in section 3.2 seems pretty arbitrary to me. What reason or motivation does it make you choose the `InClust` or `DirLoF` approach? How would you approve these are guaranteed to work?\n\nWhat if I say a good approach could be to find the most similar song (embedding similar higher than x)  that is at least y popular and place our targeted song z ( z could be -1, 1, 2, 3) positions after it?\n\nLIMITATIONS:\nThe strategy could be effective but it is built on top of the assumption that the serving platform has deployed minimum control against collective behavior. As I am aware, there are various user-side anomaly detection mechanisms usually deployed in production in streaming services like what is described in the paper. As an example, there could be real time monitoring of such collective behaviors. Such an anomaly detection system could be constructed by building a user-entity graph. In this use case, we could use artist or songs as the entity. When in a short period of time, there are bursty events that are related to an entity that happens (user promoting a song in their playlist), it could be an important indicator that something unusual happens (because this is not a popular song, it does not receive so many promotes on average). A follow-up action in the system could tag those relevant data as \"spurious\" thus they never go to the training data of the transformer model (for continuous training). As a result, the collective action could have a significantly less effect when such monitoring is turned on; or depending on what threshold they are set up.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-08"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper shows that strategic collective action by a small fraction of the population can lead to significant amplification of a particular song in a recommender system.  The authors propose two strategies for the collective (for a transformer-based song recommender) that achieve this amplification.\n\nSTRENGTHS:\n- The setup is original and focuses on strategic collective action by a fraction of users in a recommender system and how this can increase a target song’s reach.\n- The performance loss for the platform is negligible, the collective’s strategies are not adversarial (for e.g. fake profiles, artificial perturbations) and based on 1-edit distance to the original playlist. The paper shows that recommendations are largely preserved.\n\nWEAKNESSES:\n- The experiments follow the MF-Transformer in [7], to make the paper self-contained it would be beneficial to have a description of $\\phi(.)$ and of $g(.)$ and the loss function in Section 2.1 or the Appendix. \n- I found the strategies in Sec 3.2 hard to parse, perhaps a figure showing the original playlist and the possible changes a user in the collective can do under the two strategies would be helpful. \n- Minor:  I think the notation h(.) is overloaded for the song/playlist embedding in 2.1 and for the strategy mapping which inserts s* into a playlist. \tAlso, fig 6 could use different colors for the different strategies.\n\nQUESTIONS:\n- Can you clarify the intuition and communication required by the collective for the two playlist modification strategies in 3.2? \nInClust seems to place the target song before the most popular songs in the collective’s playlist whereas DirLoF places the target song after a low popularity song? \nBoth strategies provide amplification in the experiments. How much communication among the collective do both require, is one modification easier practically?\n- Section 1.1 mentions “Our strategies exploit the properties of the attention function in the transformer model, without requiring knowledge of the model parameters”, can you elaborate?\n\nLIMITATIONS:\nThe authors discuss limitations in Appendix  A.", "TITLE": "Official Review", "RECOMMENDATION": 8, "REVIEWER_CONFIDENCE": 5, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2024-07-08"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis research work proposes a novel solution to promote songs on music streaming platforms strategically. \n\nUnder the following assumptions:\n\n1. Fans can collaborate to promote a specific song by collectively reordering playlists.\n2. The visibility of a song in a playlist affects its recommendation frequency.\n3. Users are influenced by the position of songs in playlists when making listening choices.\n4. The impact of collective action on song visibility is measurable and significant.\n\nThe authors suggest that fans strategically reorder playlists to promote a targeted song, thereby increasing its visibility in the recommender system. By leveraging algorithmic collective action, even small groups of fans can substantially impact the recommendation frequency of the promoted song. This strategy aims to enhance the visibility (capability of being discovered) of songs and artists, which will benefit both fans and musicians in the music streaming industry. \n\n The evaluation focuses on quantifying the amplification of recommendations achieved by strategically placing songs in playlists, using metrics such as recommendation probability and change in the number of recommendations for a song. The evaluation also includes the impact on the recommendations of other songs and the overall performance of the recommender system. The analysis of results reveals that the collective action strategies can lead to a substantial increase in the recommendation frequency of the targeted song, with up to 25x  higher recommendation probability compared to average songs.\n\nThe main contributions are:\n\n1. The paper introduces **two innovative collective** action strategies where participants strategically insert a target song into their playlists to promote an emerging artist. These strategies aim to increase the recommendations of the targeted song at test time, thereby boosting the artist's visibility on the platform.\n\n2. The research demonstrates that even **small collectives**, controlling less than 0.01% of the training data, **can achieve significant amplification of recommendations** by strategically placing songs in playlists. This finding highlights the effectiveness of algorithmic collective action in promoting songs without major disruptions to the user experience.\n\n3. Preservation of Other Recommendations, as the study reveals that while promoting a specific song through collective action, the recommendations of other songs are largely preserved. This indicates that the proposed strategies can enhance the visibility of targeted songs without significantly compromising the overall recommendation system's performance.\n\nSTRENGTHS:\n- Its innovation is a significant strength as it provides a new approach to increasing the visibility of emerging artists in music streaming platforms.\n- Empirical Validation and Real-World Application: the research is empirically validated using an open-source APC model deployed on Deezer, a platform with millions of users. \n- important result: the study demonstrates that even small collectives can achieve substantial amplification of recommendations by strategically placing songs in playlists\n- The findings show the potential for diverse artist promotion, which can make fairer use of the platforms but also fights against the long-tail problem in recommender systems. It can also help the serendipity effect.\n\nWEAKNESSES:\n-  The paper assumes that users are influenced by the position of songs in playlists when making listening choices. This assumption **may oversimplify user behavior and overlook other factors** that influence song recommendations and user engagement, potentially leading to biased results.\n\nQUESTIONS:\nEven if the problem is not exactly the same, could you relate your work with the one described in this cite: Walid Bendada, Guillaume Salha-Galvan, Thomas Bouabça, and Tristan Cazenave. 2023. A Scalable Framework for Automatic Playlist Continuation on Music Streaming Services. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '23). Association for Computing Machinery, New York, NY, USA, 464–474. https://doi.org/10.1145/3539618.3591628 ?\n\nLIMITATIONS:\nThe paper does not explicitly discuss possible limitations of the approach to addressing problems of privacy and fairness. However, considering the ethical implications of data manipulation and collective action in recommender systems is crucial for ensuring transparency and equity in algorithmic interventions.", "TITLE": "Official Review", "RECOMMENDATION": 8, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-13"}