Artificial Intelligence concepts are often introduced through mathematical formulas and programming assignments. While effective for advanced learners, these approaches can present barriers for learners with non-technical backgrounds.

This paper analyzes the most common problems limiting reproducibility of Information Retrieval research and provides researchers with insights and guidelines to improve the reproducibility of experiments and to allow the verification of obtained results. We conducted a study on 45 reproduction reports off 17 different papers, which have been published at renowned IR conferences. We analyzed the reports qualitatively and quantitatively and looked into the different insights from different groups. Occurring problems are classified into three problem families and 13 categories and afre then analyzed with respect to their influence on the reproduction process as well as on their frequency of appearance over time and per conference. Of these 17 different papers, 14 papers were reproducible to a certain degree without significant differences to the original results, but in many cases not the whole experiment was reproducible due to missing code, information or data. Also, we look at assumptions that were made when reproducing the different papers, as some experiment workflows were incomplete and information was missing. In addition, we propose recommendations to make machine learning research more reproducible and FAIR.

Music recommendation has been relevant to the Recommender Systems (RecSys) community since the early days. With the growth of music streaming platforms, algorithmic recommendations have become critical in the music industry. However, many challenges are still wide open in the area of music recommender systems. Such challenges are currently being addressed in several research communities, including and beyond the RecSys and the Music Information Retrieval (MIR) communities. The RecSys conference has traditionally not focused very much on music content understanding. In contrast, while music content understanding is central to the MIR community, research on recommender systems is not prominent in MIR research. The Music Recommender Systems Workshop (MuRS) aims at bridging the existing gap between the diverse research communities focused on the specific challenges of music recommender systems. The workshop provides a space for researchers and practitioners from multiple disciplines to jointly discuss and exchange perspectives and solutions, and to promote discussion from both academia and industry upon future research directions in the area of music recommender systems.

Modern music streaming services are heavily based on recommendation engines to serve content to users. Sequential recommendation—continuously providing new items within a single session in a contextually coherent manner—has been an emerging topic in current literature. User feedback—a positive or negative response to the item presented—is used to drive content recommendations by learning user preferences. We extend this idea to session-based recommendation to provide context-coherent music recommendations by modelling negative user feedback, i.e., skips, in the loss function.

Music recommendation has been a prominent use case in the Rec-Sys community since the early days [4,14].With the growth of music streaming platforms in the last twenty years, algorithmic recommendation became critically important for the music industry.For consumers, when tenth of millions of music items are readily available, recommender systems are absolutely essential in helping to reduce the choice overload.Further, beyond assisting the listener in their music discovery, recommender systems have expanded to many aspects of the musical experience.A virtuous influential circle between the music industry and technological research drove improvements both in music listening experiences and in general scientific knowledge in recommender systems.Many fundamental topics in RecSys have matured together with their applications in music streaming (e.g.collaborative filtering, user modeling, etc.), while some distinctive aspects of the music medium (i.e.often consumed in sequence, passively, re-recommendation possible, etc. [13]) drove their own specific topics, such as playlist generation [5] or next-song recommendation [15].Today, music recommendation is a vibrant research area, prolific with respect to new topics [13] that led to novel contributions to the RecSys community.For example, in 2022 one of the best paper awards focused on understanding ways that recommendation

In 2019, the Recommender Systems Challenge [17] dealt for the first time with a real-world task from the area of e-tourism, namely the recommendation of hotels in booking sessions. In this context, we present the release of a new dataset that we believe is vitally important for recommendation systems research in the area of hotel search, from both academic and industry perspectives. In this article, we describe the qualitative characteristics of the dataset and present the comparison of several baseline algorithms trained on the data.

The Message Passing Interface (MPI) defines the semantics of data communication operations, while the implementing libraries provide several parameterized algorithms for each operation. Each algorithm of an MPI collective operation may work best on a particular system and may be dependent on the specific communication problem. Internally, MPI libraries employ heuristics to select the best algorithm for a given communication problem when being called by an MPI application. The majority of MPI libraries allow users to override the default algorithm selection, enabling the tuning of this selection process. The problem then becomes how to select the best possible algorithm for a specific case automatically. In this paper, we address the algorithm selection problem for MPI collective communication operations. To solve this problem, we propose an auto-tuning framework for collective MPI operations based on machine-learning techniques. First, we execute a set of benchmarks of an MPI library and its entire set of collective algorithms. Second, for each algorithm, we fit a performance model by applying regression learners. Last, we use the regression models to predict the best possible (fastest) algorithm for an unseen communication problem. We evaluate our approach for different MPI libraries and several parallel machines. The experimental results show that our approach outperforms the standard algorithm selection heuristics, which are hard-coded into the MPI libraries, by a significant margin.

We present Music Tower Blocks, a novel browsing interface for interactive music visualization, capable of dealing with web-scale music collections nowadays offered by major music streaming services. Based on a clustering created from fused metadata and acoustic features, a block-based skyline landscape is constructed. It can be navigated by the user in several ways (zooming, panning, changing angle of slope). User-adjustable color coding is used for highlighting various facets, e.g., visualizing the distributions of genres and acoustic features. Furthermore, several search and filtering capabilities are provided (e.g., search for artists and tracks; filtering with respect to track popularity to focus on top hits or discovering unknown gems). In addition, Music Tower Blocks offers the user to connect to their personal music streaming profiles and highlight on the landscape their favorite or recently-listened-to music, to support exploring parts of the landscape near to (or far-away from) their own taste.

This paper presents a novel approach to music representation learning. Triplet loss based networks have become popular for representation learning in various multimedia retrieval domains. Yet, one of the most crucial parts of this approach is the appropriate selection of triplets, which is indispensable, considering that the number of possible triplets grows cubically. We present an approach to harness multi-tag annotations for triplet selection, by using Latent Semantic Indexing to project the tags onto a high-dimensional space. From this we estimate tag-relatedness to select hard triplets. The approach is evaluated in a multi-task scenario for which we introduce four large multi-tag annotations for the Million Song Dataset for the music properties genres, styles, moods, and themes.

The workshop features presentations of accepted contributions to the RecSys Challenge 2019 organized by trivago, TU Wien, Politecnico di Bari, and Karlsruhe Institute of Technology. In the challenge, which originates from the domain of online travel recommender systems, participants had to build a click-prediction model based on user session interactions. Predictions were submitted in the form of a list of suggested accommodations and evaluated on an offline data set that contained the information what accommodation was clicked in the later part of a session. The data set contains anonymized information about almost 16 million session interactions of over 700.000 users visiting the trivago website.

Online activities such as social networking, online shopping, and consuming multi-media create digital traces, which are often analyzed and used to improve user experience and increase revenue, e. g., through better-fitting recommendations and more targeted marketing. Analyses of digital traces typically aim to find user traits such as age, gender, and nationality to derive common preferences. We investigate to which extent the music listening habits of users of the social music platform Last.fm can be used to predict their age, gender, and nationality. We propose a feature modeling approach building on Term Frequency-Inverse Document Frequency (TF-IDF) for artist listening information and artist tags combined with additionally extracted features. We show that we can substantially outperform a baseline majority voting approach and can compete with existing approaches. Further, regarding prediction accuracy vs. available listening data we show that even one single listening event per user is enough to outperform the baseline in all prediction tasks. We also compare the performance of our algorithm for different user groups and discuss possible prediction errors and how to mitigate them. We conclude that personal information can be derived from music listening information, which indeed can help better tailoring recommendations, as we illustrate with the use case of a music recommender system that can directly utilize the user attributes predicted by our algorithm to increase the quality of it’s recommendations.

This tutorial introduces multimedia recommender systems (MMRS), in particular, recommender systems that leverage multimedia content to recommend different media types. In contrast to the still most frequently adopted collaborative filtering approaches, we focus on content-based MMRS and on hybrids of collaborative filtering and content-based filtering. The target recommendation domains of the tutorial are movies, music and images. We present state-of-the-art approaches for multimedia feature extraction (text, audio, visual), including deep learning methods, and recommendation approaches tailored to the multimedia domain. Furthermore, by introducing common evaluation techniques, pointing to publicly available datasets specific to the multimedia domain, and discussing the grand challenges in MMRS research, this tutorial provides the audience with a profound introduction to MMRS and an inspiration to conduct further research.

Automatic drum transcription methods aim at extracting a symbolic representation of notes played by a drum kit in audio recordings. For automatic music analysis, this task is of particular interest as such a transcript can be used to extract high level information about the piece, e.g., tempo, downbeat positions, meter, and genre cues. In this work, an approach to transcribe drums from polyphonic audio signals based on a recurrent neural network is presented. Deep learning techniques like dropout and data augmentation are applied to improve the generalization capabilities of the system. The method is evaluated using established reference datasets consisting of solo drum tracks as well as drums mixed with accompaniment. The results are compared to state-of-the-art approaches on the same datasets. The evaluation reveals that F-measure values higher than state of the art can be achieved using the proposed method.

Considering the cultural background of users is known to improve recommender systems for multimedia items. In this work, we focus on music and analyze user demographics and music listening events in a large corpus (120,000 users, 109 events) from Last.fm to investigate whether similarity between countries in terms of cultural and socio-economic factors is reflected in music taste. To this end, we propose a tag-based model to describe the music taste of a country and correlate the resulting music profiles to Hofstede's cultural dimensions and the Quality of Government data. Spearman's rank-order correlation and Quadratic Assignment Procedure indeed indicate statistically significant weak to medium correlations of music taste and several cultural and socio-economic factors. The results will help elaborating culture-aware models of music listeners and in turn likely yield improved music recommender systems.

The particularities of musical data and its multiple modalities make original contributions possible in many core RecSys topics such as content-based and hybrid recommendation, user modeling, interfaces, and context-aware and mobile recommendations. But more urgently, the current revolution in the music industry represents major opportunities and challenges for recommendation systems in general. Recommendation systems are now central to music streaming platforms, which are rapidly increasing in listenership and becoming the top source of revenue for the music industry. It is increasingly more common for a music listener to simply access music than to purchase and own it in a personal collection. In this scenario, recommendation calls no longer for a one-shot recommendation for the purpose of a track or album purchase, but for a recommendation of a listening experience, comprising a very wide range of challenges, such as sequential recommendation, or conversational and contextual recommendations. Recommendation technologies now impact all actors in the rich and complex music industry ecosystem (listeners, labels, music makers and producers, concert halls, advertisers, etc.). To highlight these developments, we focus on three use cases: automatic playlist generation, context-aware music recommendation, and recommendation in the creative process of music making.

We propose a new paradigm for searching for sound by allowing users to graphically sketch their mental representation of sound as query. By conducting interviews with professional music producers and creators, we find that existing, text-based indexing and retrieval methods based on file names and tags to search for sound material in large collections (e.g., sample databases) do not reflect their mental concepts, which are often rooted in the visual domain and hence are far from their actual needs, work practices, and intuition. As a consequence, when creating new music on the basis of existing sounds, the process of finding these sounds is cumbersome and breaks their work flow by being forced to resort to browsing the collection. Prior work on organizing sound repositories aiming at bridging this conceptual gap between sound and vision builds upon psychological findings (often alluding to synaesthetic phenomena) or makes use of ad-hoc, technology-driven mappings. These methods foremost aim at visualizing the contents of collections or individual sounds and, again, facilitating browsing therein. For the purpose of indexing and querying, such methods have not been applied yet. We argue that the development of a search system that allows for visual queries to audio collections is desired by users and should inform and drive future research in audio retrieval. To explore this notion, we test the idea of a sketch interface with music producers in a semi-structured interview process by making use of a physical non-functional prototype. Based on the outcomes of this study, we propose a conceptual software prototype for visually querying sound repositories using image manipulation metaphors.

In this tutorial, we give an introduction to the field of and state of the art in music information retrieval (MIR). The tutorial particularly spotlights the question of music similarity, which is an essential aspect in music retrieval and recommendation. Three factors play a central role in MIR research: (1) the music content, i.e., the audio signal itself, (2) the music context, i.e., metadata in the widest sense, and (3) the listeners and their contexts, manifested in user-music interaction traces. We review approaches that extract features from all three data sources and combinations thereof and show how these features can be used for (large-scale) music indexing, music description, music similarity measurement, and recommendation. These methods are further showcased in a number of popular music applications, such as automatic playlist generation and personalized radio stationing, location-aware music recommendation, music search engines, and intelligent browsing interfaces. Additionally, related topics such as music identification, automatic music accompaniment and score following, and search and retrieval in the music production domain are discussed.

For recommending multimedia items, collaborative filtering (CF) denotes the technique of automatically predicting a user's rating or preference for an item by exploiting item preferences of a (large) group of other users. In traditional memory-based (or neighborhood-based) recommenders, this is accomplished by, first, selecting a number of similar users (or items) and, second, combining their ratings into a single user's predicted rating for an item. Strategies for both defining similarity (i.e., to identify nearest neighbors) and for combining ratings (i.e., to weight their impact) have been extensively studied and even resulted in inconsistent findings.

The SoMeRA workshop targets cutting edge research from all fields of retrieval, recommendation, and browsing in social media, as well as the analysis of user's multifaceted traces therein. Submissions to the workshop cover a broad range of topics including multimedia retrieval and exploration, user-aware recommender systems, network analysis, event detection, and computational linguistics.

This tutorial serves as an introductory course to the field of and state-of-the-art in music information retrieval (MIR) and in particular to music similarity estimation which is an essential component of music retrieval. Apart from explaining approaches that estimate similarity based on acoustic properties of an audio signal, we review methods that exploit (mostly textual) meta-data from the Web to build representations of music then used for similarity calculation. Additionally, topics such as (large-scale) music indexing, information extraction for music, personalization in music retrieval, and evaluation of MIR systems are addressed.

In this survey article, we give an overview of methods for music similarity estimation and music recommendation based on music context data. Unlike approaches that rely on music content and have been researched for almost two decades, music-context -based (or contextual ) approaches to music retrieval are a quite recent field of research within music information retrieval (MIR). Contextual data refers to all music-relevant information that is not included in the audio signal itself. In this article, we focus on contextual aspects of music primarily accessible through web technology. We discuss different sources of context-based data for individual music pieces and for music artists. We summarize various approaches for constructing similarity measures based on the collaborative or cultural knowledge incorporated into these data sources. In particular, we identify and review three main types of context-based similarity approaches: text-retrieval-based approaches (relying on web-texts, tags, or lyrics), co-occurrence-based approaches (relying on playlists, page counts, microblogs, or peer-to-peer-networks), and approaches based on user ratings or listening habits. This article elaborates the characteristics of the presented context-based measures and discusses their strengths as well as their weaknesses.

Mobile music consumption has been spiraling during the past couple of years. The interaction techniques provided to sift through the ever increasing amounts of music available on smart devices unfortunately have not. In this paper, we address this issue and present an intelligent mobile user interface that enables the user to browse her mobile music collection in a joyful and informed way.

We present a novel user interface that offers a fun way to explore music collections in virtual landscapes in a game-like manner. Extending previous work, special attention is paid to scalability and user interaction. In this vein, the ever growing size of today's music collections is addressed in two ways that allow for visualizing and browsing nearly arbitrarily sized music repositories. First, the proposed user interface deepTune employs a hierarchical version of the Self-Organizing Map (SOM) to cluster similar pieces of music using multiple, hierarchically aligned layers. Second, to facilitate orientation in the landscape by presenting well-known anchor points to the user, a combination of Web-based and audio signal-based information extraction techniques to determine cluster prototypes (songs) is proposed. Selecting representative and well-known prototypes -- the former is ensured by using signal-based features, the latter by using Web-based data -- is crucial for browsing large music collections. We further report on results of an evaluation carried out to assess the quality of the proposed cluster prototype ranking.

This article comprehensively addresses the problem of similarity measurement between music artists via text-based features extracted from Web pages. To this end, we present a thorough evaluation of different term-weighting strategies, normalization methods, aggregation functions, and similarity measurement techniques. In large-scale genre classification experiments carried out on real-world artist collections, we analyze several thousand combinations of settings/parameters that influence the similarity calculation process, and investigate in which way they impact the quality of the similarity estimates. Accurate similarity measures for music are vital for many applications, such as automated playlist generation, music recommender systems, music information systems, or intelligent user interfaces to access music collections by means beyond text-based browsing. Therefore, by exhaustively analyzing the potential of text-based features derived from artist-related Web pages, this article constitutes an important contribution to context-based music information research.

Travelling on a train and looking out of the window at the moving scenery reveals a composition of "visual music" with its own tempo and rhythm, its own colours and harmonies. The project sound/tracks aims at capturing these visual impressions and translates them into a musical composition in real-time - producing an immediate and unique soundtrack to the train journey based on the passing landscape. To this end, the outside impressions are captured with a camera and translated into instantaneously played back piano music. The immediately added sound dimension allows for reflection of the visual impression and deepening of the state of contemplation. For the resulting compositions, the passing scenery can be considered the score. "Re-transcription" of this score to an image gives a panoramic overview over the complete journey and exhibits some interesting effects caused by the movement of the train, such as compression and stretching of passing objects. In addition to intensifying the experience of a train journey, sound/tracks permits to persistently capture and archive the fleeting impressions of journey and composition and allows for re-experiencing the trip both visually and acoustically at a later point.

When travelling on a train, many people enjoy looking out of the window at the landscape passing by. We present sound/tracks, an application that translates the perceived movement of the scenery and other visual impressions, such as passing trains, into music. The continuously changing view outside the window is captured with a camera and translated into MIDI events that are replayed instantaneously. This allows for a reflection of the visual impression, adding a sound dimension to the visual experience and deepening the state of contemplation. The application is intended to be run on both mobile phones (with built-in camera) and on laptops (with a connected Web-cam). We propose and discuss different approaches to translating the video signal into an audio stream, present different application scenarios, and introduce a method to visualise the dynamics of complete train journeys by re-transcribing the captured video frames used to generate the music.

We present an approach that offers the user a convenient and meaningful way to access her music on a mobile device. By exploiting information on acoustic similarity and community-based music labels, a music collection is automatically structured and described to allow for easy orientation and navigation within the collection. To this end, the complete collection is arranged along a circular playlist path such that similar sounding pieces are grouped together. As a consequence, regions of musical styles emerge. Furthermore, we propose two approaches to derive informative descriptors that are displayed on the different regions, allowing an overview of the whole collection at a glance. For demonstration, we implemented our prototype interface on an Apple iPod.

An approach is presented to automatically build a search engine for large-scale music collections that can be queried through natural language. While existing approaches depend on explicit manual annotations and meta-data assigned to the individual audio pieces, we automatically derive descriptions by making use of methods from Web Retrieval and Music Information Retrieval. Based on the ID3 tags of a collection of mp3 files, we retrieve relevant Web pages via Google queries and use the contents of these pages to characterize the music pieces and represent them by term vectors. By incorporating complementary information about acous tic similarity we are able to both reduce the dimensionality of the vector space and improve the performance of retrieval, i.e. the quality of the results. Furthermore, the usage of audio similarity allows us to also characterize audio pieces when there is no associated information found on the Web.

We present CoMIRVA, which is an abbreviation for Collection of Music Information Retrieval and Visualization Applications. CoMIRVA is a Java framework and toolkit for information retrieval and visualization. It is licensed under the GNU GPL and can be downloaded from http://www.cp.jku.at/comirva/. At the moment, the main functionalities include music information retrieval, web retrieval, and visualization of the extracted information. In this paper, we focus on the visualization aspects of CoMIRVA. Since many of the information retrieval functions are intended to be applied to problems of the field of music information retrieval (MIR), we demonstrate the functions using data like similarity matrices of music artists gained by analyzing artist-related web pages. CoMIRVA is continuously being extended. Currently, it supports the following visualization techniques: Self-Organizing Map, Smoothed Data Histogram, Circled Bars, Circled Fans, Probabilistic Network, Continuous Similarity Ring, Sunburst, and Music Description Map. Since space is limited, we can only present a selected number of these in this paper. As one key feature of CoMIRVA is its easy extensibility, we further elaborate on how CoMIRVA was used for creating a novel user interface to digital music repositories.

We present a novel interface to (portable) music players that benefit from intelligently structured collections of audio files. For structuring, we calculate similarities between every pair of songs and model a travelling salesman problem (TSP) that is solved to obtain a playlist (i.e., the track ordering during playback) where the average distance between consecutive pieces of music is minimal according to the similarity measure. The similarities are determined using both audio signal analysis of the music tracks and Web-based artist profile comparison. Indeed, we show how to enhance the quality of the well-established methods based on audio signal processing with features derived from Web pages of music artists. Using TSP allows for creating circular playlists that can be easily browsed with a wheel as input device. We investigate the usefulness of four different TSP algorithms for this purpose. For evaluating the quality of the generated playlists, we apply a number of quality measures to two real-world music collections. It turns out that the proposed combination of audio and text-based similarity yields better results than the initial approach based on audio data only. We implemented an audio player as Java applet to demonstrate the benefits of our approach. Furthermore, we present the results of a small user study conducted to evaluate the quality of the generated playlists

We present a novel, innovative user interface to music repositories. Given an arbitrary collection of digital music files, our system creates a virtual landscape which allows the user to freely navigate in this collection. This is accomplished by automatically extracting features from the audio signal and training a Self-Organizing Map (SOM) on them to form clusters of similar sounding pieces of music. Subsequently, a Smoothed Data Histogram (SDH) is calculated on the SOM and interpreted as a three-dimensional height profile. This height profile is visualized as a three-dimensional island landscape containing the pieces of music. While moving through the terrain, the closest sounds with respect to the listener's current position can be heard. This is realized by anisotropic auralization using a 5.1 surround sound model. Additionally, we incorporate knowledge extracted automatically from the web to enrich the landscape with semantic information. More precisely, we display words and related images that describe the heard music on the landscape to support the exploration.