Alessandro B. Melchiorre

2022

1. 

   ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable Recommendations

   Alessandro B. Melchiorre, Navid Rekabsaz, Christian Ganhör, and 1 more author

   In Proceedings of the 16th ACM Conference on Recommender Systems (RecSys), 2022

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   Recent studies show the benefits of reformulating common machine learning models through the concept of prototypes – representatives of the underlying data, used to calculate the prediction score as a linear combination of similarities of a data point to prototypes. Such prototype-based formulation of a model, in addition to preserving (sometimes enhancing) the performance, enables explainability of the model’s decisions, as the prediction can be linearly broken down into the contributions of distinct definable prototypes. Following this direction, we extend the idea of prototypes to the recommender system domain by introducing ProtoMF, a novel collaborative filtering algorithm. ProtoMF learns sets of user/item prototypes that represent the general consumption characteristics of users/items in the underlying dataset. Using these prototypes, ProtoMF then represents users and items as vectors of similarities to the corresponding prototypes. These user/item representations are ultimately leveraged to make recommendations that are both effective in terms of accuracy metrics, and explainable through the interpretation of prototypes’ contributions to the affinity scores. We conduct experiments on three datasets to assess both the effectiveness and the explainability of ProtoMF. Addressing the former, we show that ProtoMF exhibits higher Hit Ratio and NDCG compared to other relevant collaborative filtering approaches. As for the latter, we qualitatively show how ProtoMF can provide explainable recommendations and how its explanation capabilities can expose the existence of statistical biases in the learned representations, which we exemplify for the case of gender bias.

   @inproceedings{melchiorre2022protomf,
     title = {ProtoMF: Prototype-based Matrix Factorization for Effective and Explainable Recommendations},
     author = {Melchiorre, Alessandro B. and Rekabsaz, Navid and Ganh\"or, Christian and Schedl, Markus},
     booktitle = {Proceedings of the 16th ACM Conference on Recommender Systems (RecSys)},
     year = {2022},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     doi = {10.1145/3523227.3546756},
     isbn = {9781450392785},
     pages = {246–256},
     numpages = {11},
     keywords = {prototypes, bias, recommender systems, explainability},
     location = {Seattle, WA, USA},
     series = {RecSys '22}
   }

2. 

   Explainability in Music Recommender Systems

   Darius* Afchar, Alessandro B.* Melchiorre, Markus Schedl, and 3 more authors

   AI Magazine, 2022

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   The most common way to listen to recorded music nowadays is via streaming platforms which provide access to tens of millions of tracks. To assist users in effectively browsing these large catalogs, the integration of Music Recommender Systems (MRSs) has become essential. Current real-world MRSs are often quite complex and optimized for recommendation accuracy. They combine several building blocks based on collaborative filtering and content-based recommendation. This complexity can hinder the ability to explain recommendations to end users, which is particularly important for recommendations perceived as unexpected or inappropriate. While pure recommendation performance often correlates with user satisfaction, explainability has a positive impact on other factors such as trust and forgiveness, which are ultimately essential to maintain user loyalty. In this article, we discuss how explainability can be addressed in the context of MRSs. We provide perspectives on how explainability could improve music recommendation algorithms and enhance user experience. First, we review common dimensions and goals of recommenders’ explainability and in general of eXplainable Artificial Intelligence (XAI), and elaborate on the extent to which these apply – or need to be adapted – to the specific characteristics of music consumption and recommendation. Then, we show how explainability components can be integrated within a MRS and in what form explanations can be provided. Since the evaluation of explanation quality is decoupled from pure accuracy-based evaluation criteria, we also discuss requirements and strategies for evaluating explanations of music recommendations. Finally, we describe the current challenges for introducing explainability within a large-scale industrial music recommender system and provide research perspectives.

   @article{afchar2022explainability,
     title = {Explainability in Music Recommender Systems},
     author = {Afchar, Darius* and Melchiorre, Alessandro B.* and Schedl, Markus and Hennequin, Romain and Epure, Elena V. and Moussallam, Manuel},
     journal = {AI Magazine},
     volume = {43},
     issue = {2},
     year = {2022},
     pages = {190--208},
     doi = {10.1002/aaai.12056},
   }

2021

1. 

   Investigating Gender Fairness of Recommendation Algorithms in the Music Domain

   Alessandro B. Melchiorre, Navid Rekabsaz, Emilia Parada-Cabaleiro, and 3 more authors

   Information Processing & Management (IPM), 2021

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   Although recommender systems (RSs) play a crucial role in our society, previous studies have revealed that the performance of RSs may considerably differ between groups of individuals with different characteristics or from different demographics. In this case, a RS is considered to be unfair when it does not perform equally well for different groups of users. Considering the importance of RSs in the distribution and consumption of musical content worldwide, a careful evaluation of fairness in the context of music RSs is crucial. To this end, we first introduce LFM-2b, a novel large-scale real-world dataset of music listening records, comprising a subset to investigate bias of RSs regarding users’ demographics. We then define a notion of fairness based on the performance gap of a RS between the users with different demographics, and evaluate a variety of collaborative filtering algorithms in terms of accuracy and beyond-accuracy metrics to explore the fairness in the RS results toward a specific gender group. We observe the existence of significant discrepancies (unfairness) between the performance of algorithms across male and female user groups. Based on these discrepancies, we explore to what extent recommender algorithms lead to intensifying the underlying population bias in the final results. We also study the effect of a resampling strategy, commonly used as debiasing method , which yields slight improvements in the fairness measures of various algorithms while maintaining their accuracy and beyond-accuracy performance.

   @article{melchiorre2021investigating,
     title = {Investigating Gender Fairness of Recommendation Algorithms in the Music Domain},
     author = {Melchiorre, Alessandro B. and Rekabsaz, Navid and Parada-Cabaleiro, Emilia and Brandl, Stefan and Lesota, Oleg and Schedl, Markus},
     journal = {Information Processing \& Management (IPM)},
     volume = {58},
     number = {5},
     pages = {102666},
     year = {2021},
     publisher = {Elsevier},
     doi = {10.1016/j.ipm.2021.102666}
   }

2020

1. 

   Personality Bias of Music Recommendation Algorithms

   Alessandro B. Melchiorre, Eva Zangerle, and Markus Schedl

   In Fourteenth ACM Conference on Recommender Systems (RecSys), 2020

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   Recommender systems, like other tools that make use of machine learning, are known to create or increase certain biases. Earlier work has already unveiled different performance of recommender systems for different user groups, depending on gender, age, country, and consumption behavior. In this work, we study user bias in terms of another aspect, i.e., users’ personality. We investigate to which extent state-of-the-art recommendation algorithms yield different accuracy scores depending on the users’ personality traits. We focus on the music domain and create a dataset of Twitter users’ music consumption behavior and personality traits, measuring the latter in terms of the OCEAN model. Investigating recall@K and NDCG@K of the recommendation algorithms SLIM, embarrassingly shallow autoencoders for sparse data (EASE), and variational autoencoders for collaborative filtering (Mult-VAE) on this dataset, we find several significant differences in performance between user groups scoring high vs. groups scoring low on several personality traits.

   @inproceedings{melchiorre2020personalitybias,
     author = {Melchiorre, Alessandro B. and Zangerle, Eva and Schedl, Markus},
     title = {Personality Bias of Music Recommendation Algorithms},
     year = {2020},
     isbn = {9781450375832},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     booktitle = {Fourteenth ACM Conference on Recommender Systems (RecSys)},
     pages = {533–538},
     numpages = {6},
     doi = {10.1145/3383313.3412223}
   }