36,615 research outputs found
Current Challenges and Visions in Music Recommender Systems Research
Music recommender systems (MRS) have experienced a boom in recent years,
thanks to the emergence and success of online streaming services, which
nowadays make available almost all music in the world at the user's fingertip.
While today's MRS considerably help users to find interesting music in these
huge catalogs, MRS research is still facing substantial challenges. In
particular when it comes to build, incorporate, and evaluate recommendation
strategies that integrate information beyond simple user--item interactions or
content-based descriptors, but dig deep into the very essence of listener
needs, preferences, and intentions, MRS research becomes a big endeavor and
related publications quite sparse.
The purpose of this trends and survey article is twofold. We first identify
and shed light on what we believe are the most pressing challenges MRS research
is facing, from both academic and industry perspectives. We review the state of
the art towards solving these challenges and discuss its limitations. Second,
we detail possible future directions and visions we contemplate for the further
evolution of the field. The article should therefore serve two purposes: giving
the interested reader an overview of current challenges in MRS research and
providing guidance for young researchers by identifying interesting, yet
under-researched, directions in the field
Sequence-based context-aware music recommendation
© 2017, Springer Science+Business Media, LLC. Contextual factors greatly affect usersâ preferences for music, so they can benefit music recommendation and music retrieval. However, how to acquire and utilize the contextual information is still facing challenges. This paper proposes a novel approach for context-aware music recommendation, which infers usersâ preferences for music, and then recommends music pieces that fit their real-time requirements. Specifically, the proposed approach first learns the low dimensional representations of music pieces from usersâ music listening sequences using neural network models. Based on the learned representations, it then infers and models usersâ general and contextual preferences for music from usersâ historical listening records. Finally, music pieces in accordance with userâs preferences are recommended to the target user. Extensive experiments are conducted on real world datasets to compare the proposed method with other state-of-the-art recommendation methods. The results demonstrate that the proposed method significantly outperforms those baselines, especially on sparse data
Graph-RAT: Combining data sources in music recommendation systems
The complexity of music recommendation systems has increased rapidly in recent years, drawing upon different sources of information: content analysis, web-mining, social tagging, etc. Unfortunately, the tools to scientifically evaluate such integrated systems are not readily available; nor are the base algorithms available. This article describes Graph-RAT (Graph-based Relational Analysis Toolkit), an open source toolkit that provides a framework for developing and evaluating novel hybrid systems. While this toolkit is designed for music recommendation, it has applications outside its discipline as well. An experimentâindicative of the sort of procedure that can be configured using the toolkitâis provided to illustrate its usefulness
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Music, movies and meaning: communication in film-markers' search for pre-existing music, and the implications for music information retrieval
While the use of music to accompany moving images is widespread, the information behaviour, communicative practice and decision making by creative professionals within this area of the music industry is an under-researched area. This investigation discusses the use of music in films and advertising focusing on communication and meaning of the music and introduces a reflexive communication model. The model is discussed in relation to interviews with a sample of music professionals who search for and use music for their work. Key factors in this process include stakeholders, briefs, product knowledge and relevance. Searching by both content and context is important, although the final decision when matching music to picture is partly intuitive and determined by a range of stakeholders
Social Collaborative Retrieval
Socially-based recommendation systems have recently attracted significant
interest, and a number of studies have shown that social information can
dramatically improve a system's predictions of user interests. Meanwhile, there
are now many potential applications that involve aspects of both recommendation
and information retrieval, and the task of collaborative retrieval---a
combination of these two traditional problems---has recently been introduced.
Successful collaborative retrieval requires overcoming severe data sparsity,
making additional sources of information, such as social graphs, particularly
valuable. In this paper we propose a new model for collaborative retrieval, and
show that our algorithm outperforms current state-of-the-art approaches by
incorporating information from social networks. We also provide empirical
analyses of the ways in which cultural interests propagate along a social graph
using a real-world music dataset.Comment: 10 page
Context-aware, ontology-based, service discovery
Service discovery is a process of locating, or discovering, one or more documents, that describe a particular service. Most of the current service discovery approaches perform syntactic matching, that is, they retrieve services descriptions that contain particular keywords from the userâs query. This often leads to poor discovery results, because the keywords in the query can be semantically similar but syntactically different, or syntactically similar but semantically different from the terms in a service description. Another drawback of the existing service discovery mechanisms is that the query-service matching score is calculated taking into account only the keywords from the userâs query and the terms in the service descriptions. Thus, regardless of the context of the service user and the context of the services providers, the same list of results is returned in response to a particular query. This paper presents a novel approach for service discovery that uses ontologies to capture the semantics of the userâs query, of the services and of the contextual information that is considered relevant in the matching process
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