We present the Height Optimized Trie (HOT), a fast and space-efficient in-memory index structure. The core algorithmic idea of HOT is to dynamically vary the number of bits considered at each node, which enables a consistently high fanout and thereby good cache efficiency. For a fixed maximum node fanout, the overall tree height is minimal and its structure is deterministically defined. Multiple carefully engineered node implementations using SIMD instructions or lightweight compression schemes provide compactness and fast search and optimize HOT structures for different usage scenarios. Our experiments, which use a wide variety of workloads and data sets, show that HOT outperforms other state-of-the-art index structures for string keys both in terms of search performance and memory footprint, while being competitive for integer keys.

Abstract In the last decade, music consumption has changed dramatically as humans have increasingly started to use music streaming platforms. While such platforms provide access to millions of songs, the sheer volume of choices available renders it hard for users to find songs they like. Consequently, the task of finding music the user likes is often mitigated by music recommender systems, which aim to provide recommendations that match the user’s current context. Particularly in the field of music recommendation, adapting recommendations to the user’s current context is critical as, throughout the day, users listen to different music in numerous different contexts and situations. Therefore, we propose a multi-context-aware user model and track recommender system that jointly exploit information about the current situation and musical preferences of users. Our proposed system clusters users based on their situational context features and similarly, clusters music tracks based on their content features. By conducting a series of offline experiments, we show that by relying on Factorization Machines for the computation of recommendations, the proposed multi-context-aware user model successfully leverages interaction effects between user listening histories, situational, and track content information, substantially outperforming a set of baseline recommender systems.

In recent years, music aficionados have increasingly been consuming music via public music streaming platforms. Due to the size of the collections provided, music recommender systems have become a vital component as these aim to provide recommendations that match the user's current context as, throughout the day, users listen to music in numerous different contexts and situations. In this paper, we propose a multi-context-aware track recommender system that jointly exploits information about the current situation and musical preferences of users. To jointly model users by their situational and musical preferences, we cluster users based on their situational features and similarly, cluster music tracks based on their content features. Our experiments show that by relying on Factorization Machines for the computation of recommendations, the proposed approach allows to successfully leverage interaction effects between listening histories, situational and track content information, substantially outperforming a set of baseline recommenders.

We present the Height Optimized Trie (HOT), a fast and space-efficient in-memory index structure. The core algorithmic idea of HOT is to dynamically vary the number of bits considered at each node, which enables a consistently high fanout and thereby good cache efficiency. The layout of each node is carefully engineered for compactness and fast search using SIMD instructions. Our experimental results, which use a wide variety of workloads and data sets, show that HOT outperforms other state-of-the-art index structures for string keys both in terms of search performance and memory footprint, while being competitive for integer keys. We believe that these properties make HOT highly useful as a general-purpose index structure for main-memory databases.

Integrating information about the listener's cultural background when building music recommender systems has recently been identified as a means to improve recommendation quality. In this paper, we therefore propose a novel approach to jointly model users by the user's musical preferences and his/her cultural background. We describe the musical preferences of users by relying on the acoustic features of the songs the users have been listening to and characterize the cultural background of users by cultural and socio-economic features that we infer from the user's country. To evaluate the impact of the proposed user model on recommendation quality, we integrate the model into a culture-aware music recommender system. We show that incorporating both acoustic information of the tracks a user has listened to as well as the cultural background of users in the form of a music-cultural user model contributes to improved recommendation performance.

Incorporating user characteristics and contextual information has shown to be essential when it comes to personalized music retrieval and recommendation. To this end, the current location of a user is often exploited. However, relying solely on GPS coordinates neglects the cultural background of users, which does not necessarily coincide with political borders. In this paper, we analyze culture-specific music listening behavior based on a dataset of 2,724 Spotify users, 62,104 distinct tracks and 104,390 listening events by modeling users jointly via their musical preferences and cultural characteristics. By applying a density-based spatial clustering algorithm, we identify nine clusters which reflect similar users regarding both their musical preference and cultural background. Our findings show that cultural aspects cannot be approximated by GPS coordinates and that incorporating cultural characteristics allows for more precise user characterization. Also, we observe that listening patterns occur on two different levels: we observe country-specific listening patterns as well as cross-country listening patterns that span across several countries.

Over the last years, music consumption has changed fundamentally: people switch from private, mostly limited music collections to huge public music collections provided by music streaming platforms. Thus, the amount of available music has increased dramatically and music streaming platforms heavily rely on recommender systems to assist users in discovering music they like. Incorporating the context of users has been shown to improve the quality of recommendations. Previous approaches based on pre-filtering suffered from a split dataset. In this work, we present a context-aware recommender system based on factorization machines that extracts information about the user's context from the names of the user's playlists. Based on a dataset comprising 15,000 users and 1.8 million tracks we show that our proposed approach outperforms the pre-filtering approach substantially in terms of accuracy of the computed recommendations.

Music streaming platforms enable people to access millions of tracks using computers and mobile devices. The latter allow users consume different music during different activities. Both, the sheer amount of music and the mobile access to music makes music organization an interesting topic for multimedia researchers. Assisting users to organize their music and make the music they like easily available in the right moment, contributes to increased usability of music streaming platforms. To get a deeper understanding of how users organize music nowadays, we analyze user-created playlists crawled from the music streaming platform Spotify. Using this new data set we find an explanation of differences in the playlists using audio features and based on this compute playlist clusters. We find that 91% of all users create at least one playlist in the “feel good music”-cluster and classical music or rap music can be considered as niche music with respect to the number of playlists, however not as niche music when considering the number of users. To foster research in this field, we make our analysis tool publicly available.

The Wikidata platform is a crowdsourced, structured knowledgebase aiming to provide integrated, free and language-agnostic facts which are---amongst others---used by Wikipedias. Users who actively enter, review and revise data on Wikidata are assisted by a property suggesting system which provides users with properties that might also be applicable to a given item. We argue that evaluating and subsequently improving this recommendation mechanism and hence, assisting users, can directly contribute to an even more integrated, consistent and extensive knowledge base serving a huge variety of applications. However, the quality and usefulness of such recommendations has not been evaluated yet. In this work, we provide the first evaluation of different approaches aiming to provide users with property recommendations in the process of curating information on Wikidata. We compare the approach currently facilitated on Wikidata with two state-of-the-art recommendation approaches stemming from the field of RDF recommender systems and collaborative information systems. Further, we also evaluate hybrid recommender systems combining these approaches. Our evaluations show that the current recommendation algorithm works well in regards to recall and precision, reaching a [email protected] of 79.71% and a [email protected] of 27.97%. We also find that generally, incorporating contextual as well as classifying information into the computation of property recommendations can further improve its performance significantly.