Flow Moods: Recommending Music by Moods on Deezer

The music streaming service Deezer extensively relies on its Flow algorithm, which generates personalized radio-style playlists of songs, to help users discover musical content. Nonetheless, despite promising results over the past years, Flow used to ignore the moods of users when providing recommendations. In this paper, we present Flow Moods, an improved version of Flow that addresses this limitation. Flow Moods leverages collaborative filtering, audio content analysis, and mood annotations from professional music curators to generate personalized mood-specific playlists at scale. We detail the motivations, the development, and the deployment of this system on Deezer. Since its release in 2021, Flow Moods has been recommending music by moods to millions of users every day.Comment: 16th ACM Conference on Recommender Systems (RecSys 2022) - Industry pape

How to Perform Reproducible Experiments in the ELLIOT Recommendation Framework: Data Processing, Model Selection, and Performance Evaluation

Recommender Systems have shown to be an efective way to alleviate the over-choice problem and provide accurate and tailored recommendations. However, the impressive number of proposed recommendation algorithms, splitting strategies, evaluation protocols, metrics, and tasks, has made rigorous experimental evaluation particularly challenging. ELLIOT is a comprehensive recommendation framework that aims to run and reproduce an entire experimental pipeline by processing a simple confguration fle. The framework loads, flters, and splits the data considering a vast set of strategies. Then, it optimizes hyperparameters for several recommendation algorithms, selects the best models, compares them with the baselines, computes metrics spanning from accuracy to beyond-accuracy, bias, and fairness, and conducts statistical analysis. The aim is to provide researchers a tool to ease all the experimental evaluation phases (and make them reproducible), from data reading to results collection. ELLIOT is freely available on GitHub at https://github.com/sisinflab/ellio

A hybrid recommender system for improving automatic playlist continuation

Although widely used, the majority of current music recommender systems still focus on recommendations’ accuracy, userpreferences and isolated item characteristics, without evaluating other important factors, like the joint item selections and the recommendation moment. However, when it comes to playlist recommendations, additional dimensions, as well as the notion of user experience and perception, should be taken into account to improve recommendations’ quality. In this work, HybA, a hybrid recommender system for automatic playlist continuation, that combines Latent Dirichlet Allocation and Case-Based Reasoning, is proposed. This system aims to address “similar concepts” rather than similar users. More than generating a playlist based on user requirements, like automatic playlist generation methods, HybA identifies the semantic characteristics of a started playlist and reuses the most similar past ones, to recommend relevant playlist continuations. In addition, support to beyond accuracy dimensions, like increased coherence or diverse items’ discovery, is provided. To overcome the semantic gap between music descriptions and user preferences, identify playlist structures and capture songs’ similarity, a graph model is used. Experiments on real datasets have shown that the proposed algorithm is able to outperform other state of the art techniques, in terms of accuracy, while balancing between diversity and coherence.This work has been partially supported by the Catalan Agency for Management of University and Research Grants (AGAUR) (2017 SGR 574), by the European Regional Development Fund (ERDF), through the Incentive System to Research and Technological development, within the Portugal2020 Competitiveness and Internationalization Operational Program –COMPETE 2020– (POCI-01-0145-FEDER006961), and by the Portuguese Foundation for Science and Technology (FCT) (UID/EEA/50014/2013).Peer ReviewedPostprint (author's final draft

Improving accountability in recommender systems research through reproducibility

Reproducibility is a key requirement for scientific progress. It allows the reproduction of the works of others, and, as a consequence, to fully trust the reported claims and results. In this work, we argue that, by facilitating reproducibility of recommender systems experimentation, we indirectly address the issues of accountability and transparency in recommender systems research from the perspectives of practitioners, designers, and engineers aiming to assess the capabilities of published research works. These issues have become increasingly prevalent in recent literature. Reasons for this include societal movements around intelligent systems and artificial intelligence striving toward fair and objective use of human behavioral data (as in Machine Learning, Information Retrieval, or Human–Computer Interaction). Society has grown to expect explanations and transparency standards regarding the underlying algorithms making automated decisions for and around us. This work surveys existing definitions of these concepts and proposes a coherent terminology for recommender systems research, with the goal to connect reproducibility to accountability. We achieve this by introducing several guidelines and steps that lead to reproducible and, hence, accountable experimental workflows and research. We additionally analyze several instantiations of recommender system implementations available in the literature and discuss the extent to which they fit in the introduced framework. With this work, we aim to shed light on this important problem and facilitate progress in the field by increasing the accountability of researchThis work has been funded by the Ministerio de Ciencia, Innovación y Universidades (reference: PID2019-108965GB-I00

A Multi-Armed Bandit Model Selection for Cold-Start User Recommendation

International audienceHow can we effectively recommend items to a user about whom we have no information? This is the problem we focus on, known as the cold-start problem. In this paper, we focus on the cold user problem.In most existing works, the cold-start problem is handled through the use of many kinds of information available about the user. However, what happens if we do not have any information?Recommender systems usually keep a substantial amount of prediction models that are available for analysis. Moreover, recommendations to new users yield uncertain returns. Assuming a number of alternative prediction models is available to select items to recommend to a cold user, this paper introduces a multi-armed bandit based model selection, named PdMS.In comparison with two baselines, PdMS improves the performance as measured by the nDCG.These improvements are demonstrated on real, public datasets

RELISON: A Framework for Link Recommendation in Social Networks

Link recommendation is an important and compelling problem at the intersection of recommender systems and online social networks. Given a user, link recommenders identify people in the platform the user might be interested in interacting with. We present RELISON, an extensible framework for running link recommendation experiments. The library provides a wide range of algorithms, along with tools for evaluating the produced recommendations. RELISON includes algorithms and metrics that consider the potential effect of recommendations on the properties of online social networks. For this reason, the library also implements network structure analysis metrics, community detection algorithms, and network diffusion simulation functionalities. The library code and documentation is available at https://github.com/ir-uam/RELISON

A flexible framework for evaluating user and item fairness in recommender systems

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s11257-020-09285-1One common characteristic of research works focused on fairness evaluation (in machine learning) is that they call for some form of parity (equality) either in treatment—meaning they ignore the information about users’ memberships in protected classes during training—or in impact—by enforcing proportional beneficial outcomes to users in different protected classes. In the recommender systems community, fairness has been studied with respect to both users’ and items’ memberships in protected classes defined by some sensitive attributes (e.g., gender or race for users, revenue in a multi-stakeholder setting for items). Again here, the concept has been commonly interpreted as some form of equality—i.e., the degree to which the system is meeting the information needs of all its users in an equal sense. In this work, we propose a probabilistic framework based on generalized cross entropy (GCE) to measure fairness of a given recommendation model. The framework comes with a suite of advantages: first, it allows the system designer to define and measure fairness for both users and items and can be applied to any classification task; second, it can incorporate various notions of fairness as it does not rely on specific and predefined probability distributions and they can be defined at design time; finally, in its design it uses a gain factor, which can be flexibly defined to contemplate different accuracy-related metrics to measure fairness upon decision-support metrics (e.g., precision, recall) or rank-based measures (e.g., NDCG, MAP). An experimental evaluation on four real-world datasets shows the nuances captured by our proposed metric regarding fairness on different user and item attributes, where nearest-neighbor recommenders tend to obtain good results under equality constraints. We observed that when the users are clustered based on both their interaction with the system and other sensitive attributes, such as age or gender, algorithms with similar performance values get different behaviors with respect to user fairness due to the different way they process data for each user clusterThe authors thank the reviewers for their thoughtful comments and suggestions. This work was supported in part by the Ministerio de Ciencia, Innovacion y Universidades (Reference: 123496 Y. Deldjoo et al. PID2019-108965GB-I00) and in part by the Center for Intelligent Information Retrieval. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsor