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  1. Article: Emotion-aware music tower blocks (EmoMTB ): an intelligent audiovisual interface for music discovery and recommendation.

    Melchiorre, Alessandro B / Penz, David / Ganhör, Christian / Lesota, Oleg / Fragoso, Vasco / Fritzl, Florian / Parada-Cabaleiro, Emilia / Schubert, Franz / Schedl, Markus

    International journal of multimedia information retrieval

    2023  Volume 12, Issue 1, Page(s) 13

    Abstract: Music listening has experienced a sharp increase during the last decade thanks to music streaming and recommendation services. While they offer text-based search functionality and provide recommendation lists of remarkable utility, their typical mode of ... ...

    Abstract Music listening has experienced a sharp increase during the last decade thanks to music streaming and recommendation services. While they offer text-based search functionality and provide recommendation lists of remarkable utility, their typical mode of interaction is unidimensional, i.e., they provide lists of consecutive tracks, which are commonly inspected in sequential order by the user. The user experience with such systems is heavily affected by cognition biases (e.g., position bias, human tendency to pay more attention to first positions of ordered lists) as well as algorithmic biases (e.g., popularity bias, the tendency of recommender systems to overrepresent popular items). This may cause dissatisfaction among the users by disabling them to find novel music to enjoy. In light of such systems and biases, we propose an intelligent audiovisual music exploration system named EmoMTB . It allows the user to browse the entirety of a given collection in a free nonlinear fashion. The navigation is assisted by a set of personalized emotion-aware recommendations, which serve as starting points for the exploration experience. EmoMTB  adopts the metaphor of a city, in which each track (visualized as a colored cube) represents one floor of a building. Highly similar tracks are located in the same building; moderately similar ones form neighborhoods that mostly correspond to genres. Tracks situated between distinct neighborhoods create a gradual transition between genres. Users can navigate this music city using their smartphones as control devices. They can explore districts of well-known music or decide to leave their comfort zone. In addition, EmoMTB   integrates an emotion-aware music recommendation system that re-ranks the list of suggested starting points for exploration according to the user's self-identified emotion or the collective emotion expressed in EmoMTB 's Twitter channel. Evaluation of EmoMTB   has been carried out in a threefold way: by quantifying the homogeneity of the clustering underlying the construction of the city, by measuring the accuracy of the emotion predictor, and by carrying out a web-based survey composed of open questions to obtain qualitative feedback from users.
    Language English
    Publishing date 2023-06-02
    Publishing country England
    Document type Journal Article
    ZDB-ID 2649485-1
    ISSN 2192-662X ; 2192-6611
    ISSN (online) 2192-662X
    ISSN 2192-6611
    DOI 10.1007/s13735-023-00275-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: Unlearning Protected User Attributes in Recommendations with Adversarial Training

    Ganhör, Christian / Penz, David / Rekabsaz, Navid / Lesota, Oleg / Schedl, Markus

    2022  

    Abstract: Collaborative filtering algorithms capture underlying consumption patterns, including the ones specific to particular demographics or protected information of users, e.g. gender, race, and location. These encoded biases can influence the decision of a ... ...

    Abstract Collaborative filtering algorithms capture underlying consumption patterns, including the ones specific to particular demographics or protected information of users, e.g. gender, race, and location. These encoded biases can influence the decision of a recommendation system (RS) towards further separation of the contents provided to various demographic subgroups, and raise privacy concerns regarding the disclosure of users' protected attributes. In this work, we investigate the possibility and challenges of removing specific protected information of users from the learned interaction representations of a RS algorithm, while maintaining its effectiveness. Specifically, we incorporate adversarial training into the state-of-the-art MultVAE architecture, resulting in a novel model, Adversarial Variational Auto-Encoder with Multinomial Likelihood (Adv-MultVAE), which aims at removing the implicit information of protected attributes while preserving recommendation performance. We conduct experiments on the MovieLens-1M and LFM-2b-DemoBias datasets, and evaluate the effectiveness of the bias mitigation method based on the inability of external attackers in revealing the users' gender information from the model. Comparing with baseline MultVAE, the results show that Adv-MultVAE, with marginal deterioration in performance (w.r.t. NDCG and recall), largely mitigates inherent biases in the model on both datasets.

    Comment: Accepted at SIGIR 2022
    Keywords Computer Science - Information Retrieval ; Computer Science - Artificial Intelligence ; Computer Science - Cryptography and Security ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2022-06-09
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: Parameter-efficient Modularised Bias Mitigation via AdapterFusion

    Kumar, Deepak / Lesota, Oleg / Zerveas, George / Cohen, Daniel / Eickhoff, Carsten / Schedl, Markus / Rekabsaz, Navid

    2023  

    Abstract: Large pre-trained language models contain societal biases and carry along these biases to downstream tasks. Current in-processing bias mitigation approaches (like adversarial training) impose debiasing by updating a model's parameters, effectively ... ...

    Abstract Large pre-trained language models contain societal biases and carry along these biases to downstream tasks. Current in-processing bias mitigation approaches (like adversarial training) impose debiasing by updating a model's parameters, effectively transferring the model to a new, irreversible debiased state. In this work, we propose a novel approach to develop stand-alone debiasing functionalities separate from the model, which can be integrated into the model on-demand, while keeping the core model untouched. Drawing from the concept of AdapterFusion in multi-task learning, we introduce DAM (Debiasing with Adapter Modules) - a debiasing approach to first encapsulate arbitrary bias mitigation functionalities into separate adapters, and then add them to the model on-demand in order to deliver fairness qualities. We conduct a large set of experiments on three classification tasks with gender, race, and age as protected attributes. Our results show that DAM improves or maintains the effectiveness of bias mitigation, avoids catastrophic forgetting in a multi-attribute scenario, and maintains on-par task performance, while granting parameter-efficiency and easy switching between the original and debiased models.

    Comment: Post EACL 2023 version
    Keywords Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
    Subject code 006 ; 004
    Publishing date 2023-02-13
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: Not All Relevance Scores are Equal

    Cohen, Daniel / Mitra, Bhaskar / Lesota, Oleg / Rekabsaz, Navid / Eickhoff, Carsten

    Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models

    2021  

    Abstract: In any ranking system, the retrieval model outputs a single score for a document based on its belief on how relevant it is to a given search query. While retrieval models have continued to improve with the introduction of increasingly complex ... ...

    Abstract In any ranking system, the retrieval model outputs a single score for a document based on its belief on how relevant it is to a given search query. While retrieval models have continued to improve with the introduction of increasingly complex architectures, few works have investigated a retrieval model's belief in the score beyond the scope of a single value. We argue that capturing the model's uncertainty with respect to its own scoring of a document is a critical aspect of retrieval that allows for greater use of current models across new document distributions, collections, or even improving effectiveness for down-stream tasks. In this paper, we address this problem via an efficient Bayesian framework for retrieval models which captures the model's belief in the relevance score through a stochastic process while adding only negligible computational overhead. We evaluate this belief via a ranking based calibration metric showing that our approximate Bayesian framework significantly improves a retrieval model's ranking effectiveness through a risk aware reranking as well as its confidence calibration. Lastly, we demonstrate that this additional uncertainty information is actionable and reliable on down-stream tasks represented via cutoff prediction.

    Comment: ACM SIGIR preprint
    Keywords Computer Science - Information Retrieval
    Subject code 006
    Publishing date 2021-05-10
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: TripClick

    Rekabsaz, Navid / Lesota, Oleg / Schedl, Markus / Brassey, Jon / Eickhoff, Carsten

    The Log Files of a Large Health Web Search Engine

    2021  

    Abstract: Click logs are valuable resources for a variety of information retrieval (IR) tasks. This includes query understanding/analysis, as well as learning effective IR models particularly when the models require large amounts of training data. We release a ... ...

    Abstract Click logs are valuable resources for a variety of information retrieval (IR) tasks. This includes query understanding/analysis, as well as learning effective IR models particularly when the models require large amounts of training data. We release a large-scale domain-specific dataset of click logs, obtained from user interactions of the Trip Database health web search engine. Our click log dataset comprises approximately 5.2 million user interactions collected between 2013 and 2020. We use this dataset to create a standard IR evaluation benchmark -- TripClick -- with around 700,000 unique free-text queries and 1.3 million pairs of query-document relevance signals, whose relevance is estimated by two click-through models. As such, the collection is one of the few datasets offering the necessary data richness and scale to train neural IR models with a large amount of parameters, and notably the first in the health domain. Using TripClick, we conduct experiments to evaluate a variety of IR models, showing the benefits of exploiting this data to train neural architectures. In particular, the evaluation results show that the best performing neural IR model significantly improves the performance by a large margin relative to classical IR models, especially for more frequent queries.

    Comment: Accepted at SIGIR 2021
    Keywords Computer Science - Information Retrieval
    Subject code 006
    Publishing date 2021-03-14
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Grep-BiasIR

    Krieg, Klara / Parada-Cabaleiro, Emilia / Medicus, Gertraud / Lesota, Oleg / Schedl, Markus / Rekabsaz, Navid

    A Dataset for Investigating Gender Representation-Bias in Information Retrieval Results

    2022  

    Abstract: The provided contents by information retrieval (IR) systems can reflect the existing societal biases and stereotypes. Such biases in retrieval results can lead to further establishing and strengthening stereotypes in society and also in the systems. To ... ...

    Abstract The provided contents by information retrieval (IR) systems can reflect the existing societal biases and stereotypes. Such biases in retrieval results can lead to further establishing and strengthening stereotypes in society and also in the systems. To facilitate the studies of gender bias in the retrieval results of IR systems, we introduce Gender Representation-Bias for Information Retrieval (Grep-BiasIR), a novel thoroughly-audited dataset consisting of 118 bias-sensitive neutral search queries. The set of queries covers a wide range of gender-related topics, for which a biased representation of genders in the search result can be considered as socially problematic. Each query is accompanied with one relevant and one non-relevant document, where the document is also provided in three variations of female, male, and neutral. The dataset is available at https://github.com/KlaraKrieg/GrepBiasIR.

    Comment: CHIIR 2023
    Keywords Computer Science - Information Retrieval
    Subject code 004
    Publishing date 2022-01-19
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Analyzing Item Popularity Bias of Music Recommender Systems

    Lesota, Oleg / Melchiorre, Alessandro B. / Rekabsaz, Navid / Brandl, Stefan / Kowald, Dominik / Lex, Elisabeth / Schedl, Markus

    Are Different Genders Equally Affected?

    2021  

    Abstract: Several studies have identified discrepancies between the popularity of items in user profiles and the corresponding recommendation lists. Such behavior, which concerns a variety of recommendation algorithms, is referred to as popularity bias. Existing ... ...

    Abstract Several studies have identified discrepancies between the popularity of items in user profiles and the corresponding recommendation lists. Such behavior, which concerns a variety of recommendation algorithms, is referred to as popularity bias. Existing work predominantly adopts simple statistical measures, such as the difference of mean or median popularity, to quantify popularity bias. Moreover, it does so irrespective of user characteristics other than the inclination to popular content. In this work, in contrast, we propose to investigate popularity differences (between the user profile and recommendation list) in terms of median, a variety of statistical moments, as well as similarity measures that consider the entire popularity distributions (Kullback-Leibler divergence and Kendall's tau rank-order correlation). This results in a more detailed picture of the characteristics of popularity bias. Furthermore, we investigate whether such algorithmic popularity bias affects users of different genders in the same way. We focus on music recommendation and conduct experiments on the recently released standardized LFM-2b dataset, containing listening profiles of Last.fm users. We investigate the algorithmic popularity bias of seven common recommendation algorithms (five collaborative filtering and two baselines). Our experiments show that (1) the studied metrics provide novel insights into popularity bias in comparison with only using average differences, (2) algorithms less inclined towards popularity bias amplification do not necessarily perform worse in terms of utility (NDCG), (3) the majority of the investigated recommenders intensify the popularity bias of the female users.

    Comment: RecSys 2021 - LBR
    Keywords Computer Science - Information Retrieval
    Subject code 006
    Publishing date 2021-08-16
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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