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Drug-induced liver injury (DILI) is a common reason for the withdrawal of a drug from the market. Early assessment of DILI risk is an essential part of drug development, but it is rendered challenging prior to clinical trials by the complex factors that give rise to liver damage. Artificial intelligence (AI) approaches, particularly those building on machine learning, range from random forests to more recent techniques such as deep learning, and provide tools that can analyze chemical compounds and accurately predict some of their properties based purely on their structure. This article reviews existing AI approaches to predicting DILI and elaborates on the challenges that arise from the as yet limited availability of data. Future directions are discussed focusing on rich data modalities, such as 3D spheroids, and the slow but steady increase in drugs annotated with DILI risk labels.

Due to the current severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, there is an urgent need for novel therapies and drugs. We conducted a large-scale virtual screening for small molecules that are potential CoV-2 inhibitors. To this end, we utilized "ChemAI", a deep neural network trained on more than 220M data points across 3.6M molecules from three public drug-discovery databases. With ChemAI, we screened and ranked one billion molecules from the ZINC database for favourable effects against CoV-2. We then reduced the result to the 30,000 top-ranked compounds, which are readily accessible and purchasable via the ZINC database. Additionally, we screened the DrugBank using ChemAI to allow for drug repurposing, which would be a fast way towards a therapy. We provide these top-ranked compounds of ZINC and DrugBank as a library for further screening with bioassays at https://github.com/ml-jku/sars-cov-inhibitors-chemai., Additional results added. Various corrections to formulations and typos

Music recommender systems have become a key technology to support the interaction of users with the increasingly larger music catalogs of on-line music streaming services, on-line music shops, and personal devices. An important task in music recommender systems is the automated continuation of music playlists, that enables the recommendation of music streams adapting to given (possibly short) listening sessions. Previous works have shown that applying collaborative filtering to collections of curated music playlists reveals underlying playlist-song co-occurrence patterns that are useful to predict playlist continuations. However, most music collections exhibit a pronounced long-tailed distribution. The majority of songs occur only in few playlists and, as a consequence, they are poorly represented by collaborative filtering. We introduce two feature-combination hybrid recommender systems that extend collaborative filtering by integrating the collaborative information encoded in curated music playlists with any type of song feature vector representation. We conduct off-line experiments to assess the performance of the proposed systems to recover withheld playlist continuations, and we compare them to competitive pure and hybrid collaborative filtering baselines. The results of the experiments indicate that the introduced feature-combination hybrid recommender systems can more accurately predict fitting playlist continuations as a result of their improved representation of songs occurring in few playlists.

The availability of increasingly larger multimedia collections has fostered extensive research in recommender systems. Instead of capturing general user preferences, the task of next-item recommendation focuses on revealing specific session preferences encoded in the most recent user interactions. This study focuses on the music domain, particularly on the task of music playlist continuation, a paradigmatic case of next-item recommendation. While the accuracy achieved in next-song recommendations is important, in this work we shift our focus toward a deeper understanding of fundamental playlist characteristics, namely the song order, the song context and the song popularity, and their relation to the recommendation of playlist continuations. We also propose an approach to assess the quality of the recommendations that mitigates known problems of off-line experiments for music recommender systems. Our results indicate that knowing a longer song context has a positive impact on next-song recommendations. We find that the long-tailed nature of the playlist datasets makes simple and highly expressive playlist models appear to perform comparably, but further analysis reveals the advantage of using highly expressive models. Finally, our experiments suggest that the song order is not crucial to accurately predict next-song recommendations.

Music recommender systems have become a key technology supporting the access to increasingly larger music catalogs in on-line music streaming services, on-line music shops, and private collections. The interaction of users with large music catalogs is a complex phenomenon researched from different disciplines. We survey our works investigating the machine learning and data mining aspects of hybrid music recommender systems (i.e., systems that integrate different recommendation techniques). We proposed hybrid music recommender systems robust to the so-called “cold-start problem” for new music items, favoring the discovery of relevant but non-popular music. We thoroughly studied the specific task of music playlist continuation, by analyzing fundamental playlist characteristics, song feature representations, and the relationship between playlists and the songs therein.

Automated music playlist continuation is a common task of music recommender systems, that generally consists in providing a fitting extension to a given playlist. Collaborative filtering models, that extract abstract patterns from curated music playlists, tend to provide better playlist continuations than content-based approaches. However, pure collaborative filtering models have at least one of the following limitations: (1) they can only extend playlists profiled at training time; (2) they misrepresent songs that occur in very few playlists. We introduce a novel hybrid playlist continuation model based on what we name "playlist-song membership", that is, whether a given playlist and a given song fit together. The proposed model regards any playlist-song pair exclusively in terms of feature vectors. In light of this information, and after having been trained on a collection of labeled playlist-song pairs, the proposed model decides whether a playlist-song pair fits together or not. Experimental results on two datasets of curated music playlists show that the proposed playlist continuation model compares to a state-of-the-art collaborative filtering model in the ideal situation of extending playlists profiled at training time and where songs occurred frequently in training playlists. In contrast to the collaborative filtering model, and as a result of its general understanding of the playlist-song pairs in terms of feature vectors, the proposed model is additionally able to (1) extend non-profiled playlists and (2) recommend songs that occurred seldom or never in training~playlists.

Cross-modality retrieval encompasses retrieval tasks where the fetched items are of a different type than the search query, e.g., retrieving pictures relevant to a given text query. The state-of-the-art approach to cross-modality retrieval relies on learning a joint embedding space of the two modalities, where items from either modality are retrieved using nearest-neighbor search. In this work, we introduce a neural network layer based on Canonical Correlation Analysis (CCA) that learns better embedding spaces by analytically computing projections that maximize correlation. In contrast to previous approaches, the CCA Layer (CCAL) allows us to combine existing objectives for embedding space learning, such as pairwise ranking losses, with the optimal projections of CCA. We show the effectiveness of our approach for cross-modality retrieval on three different scenarios (text-to-image, audio-sheet-music and zero-shot retrieval), surpassing both Deep CCA and a multi-view network using freely learned projections optimized by a pairwise ranking loss, especially when little training data is available (the code for all three methods is released at: https://github.com/CPJKU/cca_layer)., Comment: Preliminary version of a paper published in the International Journal of Multimedia Information Retrieval

Automated music playlist generation is a specific form of music recommendation. Generally stated, the user receives a set of song suggestions defining a coherent listening session. We hypothesize that the best way to convey such playlist coherence to new recommendations is by learning it from actual curated examples, in contrast to imposing ad hoc constraints. Collaborative filtering methods can be used to capture underlying patterns in hand-curated playlists. However, the scarcity of thoroughly curated playlists and the bias towards popular songs result in the vast majority of songs occurring in very few playlists and thus being poorly recommended. To overcome this issue, we propose an alternative model based on a song-to-playlist classifier, which learns the underlying structure from actual playlists while leveraging song features derived from audio, social tags and independent listening logs. Experiments on two datasets of hand-curated playlists show competitive performance compared to collaborative filtering when sufficient training data is available and more robust performance when recommending rare and out-of-set songs. For example, both approaches achieve a recall@100 of roughly 35% for songs occurring in 5 or more training playists, whereas the proposed model achieves a recall@100 of roughly 15% for songs occurring in 4 or less training playlists, compared to the 3% achieved by collaborative filtering.

Applying diversity to a recommendation list has been shown to positively influence the user experience. A higher perceived diversity is argued to have a positive effect on the attractiveness of the recommendation list and a negative effect on the difficulty to make a choice. In a user study we presented 100 participants with several personalized lists of recommended music artists varying in levels of diversity. Participants were asked to assess these lists on perceived diversity and attractiveness, the experienced choice difficulty and discovery (i.e., the extent the list enriches their taste). We found that recommendation list attractiveness is influenced by two effects: 1) by diversity mediated through discovery; diverse recommendation lists are perceived to be more attractive if they enrich the user's taste or 2) by the list familiarity; a higher list familiarity contributes to a higher list attractiveness. We additionally revealed how individual differences (i.e., familiarity) moderate the effects found. Our results have implications on the composition of diversified recommendation lists. Specifically recommended items should contribute in extending and/or deepening the user's taste for the diversification to be effective.

Providing diversity in recommendations has shown to positively influence the user's subjective evaluations such as satisfaction. However, it is often unknown how much diversity a recommendation set needs to consist of. In this work, we explored how music users of Last.fm apply diversity in their listening behavior. We analyzed a dataset with the music listening history of 53,309 Last.fm users capturing their total listening events until August 2014. We complemented this dataset with The Echo Nest features and Hofstede's cultural dimensions to explore how music diversity is applied across countries. Between 47 countries, we found distinct relationships between the cultural dimensions and music diversity variables. These results suggest that different country-based diversity measurements should be considered when applied to a recommendation set in order to maximize the user's subjective evaluations. The country-based relationships also provide opportunities for recommender systems to personalize experiences when user data is limited by being able to rely on the user's demographics.

Se propone una metodología de evaluación de sitios web dirigidos a un público multilingüe y multicultural. Para ello, se han revisado los estándares y recomendaciones de diversas fuentes validadas, se han creado unas pautas generales (heurísticas) y se han desarrollado indicadores detallados para ellas. El resultado es una checklist para la evaluación de sitios web con público internacional, tanto monolingües (con o sin internacionalización) como multilingües (con o sin localización).

The objective of this PhD research is to deepen the understanding of how people listen to music and construct playlists. We believe that further insights into such mechanisms can lead to enhanced music recommendations. We research on the exploitation of user-generated data in the context of on-line music services, since it constitutes a rich and increasing source of information of user behavior. The research carried out so far has centered on the scenario of producing a single artist recommendation. Concretely, in this paper we show how to mitigate the cold-start problem for new artists, elaborating on our findings on the combined effect of users' listening histories and users' tagging activity. As future research, we will investigate how improved techniques to exploit user-generated data can also be applied to the task of producing sequential recommendations, like playlists. We are particulary interested in creating music playlists similarly as users would do, and in finding mechanisms to make such music streams adapt to users' feedback on-line.

Music information retrieval and music recommendation are seeing a paradigm shift towards methods that incorporate user context aspects. However, structured experiments on a standardized music dataset to investigate the effects of doing so are scarce. In this paper, we compare performance of various combinations of collaborative filtering and geospatial as well as cultural user models for the task of music recommendation. To this end, we propose a geospatial model that uses GPS coordinates and a cultural model that uses semantic locations (continent, country, and state of the user). We conduct experiments on a novel standardized music collection, the ``Million Musical Tweets Dataset'' of listening events extracted from microblogs. Overall, we find that modeling listeners' location via Gaussian mixture models and computing similarities from these outperforms both cultural user models and collaborative filtering.