Sebastian Michel

Angjela Davitkova, Damjan Gjurovski, Sebastian Michel

Bloom filters are widely used data structures that compactly represent sets of elements. Querying a Bloom filter reveals if an element is not included in the underlying set or is included with a certain error rate. This membership testing can be modeled as a binary classification problem and solved through deep learning models, leading to what is called learned Bloom filters. We have identified that the benefits of learned Bloom filters are apparent only when considering a vast amount of data, and even then, there is a possibility to further reduce their memory consumption. For that reason, we introduce a lossless input compression technique that improves the memory consumption of the learned model while preserving a comparable model accuracy. We evaluate our approach and show significant memory consumption improvements over learned Bloom filters.

Aleksandar Stupar, Sebastian Michel

In this work, a benchmark to evaluate the retrieval performance of soundtrack recommendation systems is proposed. Such systems aim at finding songs that are played as background music for a given set of images. The proposed benchmark is based on preference judgments, where relevance is considered a continuous ordinal variable and judgments are collected for pairs of songs with respect to a query (i.e., set of images). To capture a wide variety of songs and images, we use a large space of possible music genres, different emotions expressed through music, and various query-image themes. The benchmark consists of two types of relevance assessments: (i) judgments obtained from a user study, that serve as a "gold standard" for (ii) relevance judgments gathered through Amazon's Mechanical Turk. We report on an analysis of relevance judgments based on different levels of user agreement and investigate the performance of two state-of-the-art soundtrack recommendation systems using the proposed benchmark.