525 research outputs found
Large-Scale User Modeling with Recurrent Neural Networks for Music Discovery on Multiple Time Scales
The amount of content on online music streaming platforms is immense, and
most users only access a tiny fraction of this content. Recommender systems are
the application of choice to open up the collection to these users.
Collaborative filtering has the disadvantage that it relies on explicit
ratings, which are often unavailable, and generally disregards the temporal
nature of music consumption. On the other hand, item co-occurrence algorithms,
such as the recently introduced word2vec-based recommenders, are typically left
without an effective user representation. In this paper, we present a new
approach to model users through recurrent neural networks by sequentially
processing consumed items, represented by any type of embeddings and other
context features. This way we obtain semantically rich user representations,
which capture a user's musical taste over time. Our experimental analysis on
large-scale user data shows that our model can be used to predict future songs
a user will likely listen to, both in the short and long term.Comment: Author pre-print version, 20 pages, 6 figures, 4 table
Automatic estimation of harmonic tension by distributed representation of chords
The buildup and release of a sense of tension is one of the most essential
aspects of the process of listening to music. A veridical computational model
of perceived musical tension would be an important ingredient for many music
informatics applications. The present paper presents a new approach to
modelling harmonic tension based on a distributed representation of chords. The
starting hypothesis is that harmonic tension as perceived by human listeners is
related, among other things, to the expectedness of harmonic units (chords) in
their local harmonic context. We train a word2vec-type neural network to learn
a vector space that captures contextual similarity and expectedness, and define
a quantitative measure of harmonic tension on top of this. To assess the
veridicality of the model, we compare its outputs on a number of well-defined
chord classes and cadential contexts to results from pertinent empirical
studies in music psychology. Statistical analysis shows that the model's
predictions conform very well with empirical evidence obtained from human
listeners.Comment: 12 pages, 4 figures. To appear in Proceedings of the 13th
International Symposium on Computer Music Multidisciplinary Research (CMMR),
Porto, Portuga
Target Type Identification for Entity-Bearing Queries
Identifying the target types of entity-bearing queries can help improve
retrieval performance as well as the overall search experience. In this work,
we address the problem of automatically detecting the target types of a query
with respect to a type taxonomy. We propose a supervised learning approach with
a rich variety of features. Using a purpose-built test collection, we show that
our approach outperforms existing methods by a remarkable margin. This is an
extended version of the article published with the same title in the
Proceedings of SIGIR'17.Comment: Extended version of SIGIR'17 short paper, 5 page
Temporal word embeddings for dynamic user profiling in Twitter
The research described in this paper focused on exploring
the domain of user profiling, a nascent and contentious technology which
has been steadily attracting increased interest from the research community as its potential for providing personalised digital services is realised.
An extensive review of related literature revealed that limited research
has been conducted into how temporal aspects of users can be captured
using user profiling techniques. This, coupled with the notable lack of
research into the use of word embedding techniques to capture temporal
variances in language, revealed an opportunity to extend the Random Indexing word embedding technique such that the interests of users could
be modelled based on their use of language. To achieve this, this work
concerned itself with extending an existing implementation of Temporal
Random Indexing to model Twitter users across multiple granularities of
time based on their use of language. The product of this is a novel technique for temporal user profiling, where a set of vectors is used to describe
the evolution of a Twitter user’s interests over time through their use of
language. The vectors produced were evaluated against a temporal implementation of another state-of-the-art word embedding technique, the
Word2Vec Dynamic Independent Skip-gram model, where it was found
that Temporal Random Indexing outperformed Word2Vec in the generation of temporal user profiles
Graph-Embedding Empowered Entity Retrieval
In this research, we improve upon the current state of the art in entity
retrieval by re-ranking the result list using graph embeddings. The paper shows
that graph embeddings are useful for entity-oriented search tasks. We
demonstrate empirically that encoding information from the knowledge graph into
(graph) embeddings contributes to a higher increase in effectiveness of entity
retrieval results than using plain word embeddings. We analyze the impact of
the accuracy of the entity linker on the overall retrieval effectiveness. Our
analysis further deploys the cluster hypothesis to explain the observed
advantages of graph embeddings over the more widely used word embeddings, for
user tasks involving ranking entities
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