33,429 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
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Knowledge-aware Complementary Product Representation Learning
Learning product representations that reflect complementary relationship
plays a central role in e-commerce recommender system. In the absence of the
product relationships graph, which existing methods rely on, there is a need to
detect the complementary relationships directly from noisy and sparse customer
purchase activities. Furthermore, unlike simple relationships such as
similarity, complementariness is asymmetric and non-transitive. Standard usage
of representation learning emphasizes on only one set of embedding, which is
problematic for modelling such properties of complementariness. We propose
using knowledge-aware learning with dual product embedding to solve the above
challenges. We encode contextual knowledge into product representation by
multi-task learning, to alleviate the sparsity issue. By explicitly modelling
with user bias terms, we separate the noise of customer-specific preferences
from the complementariness. Furthermore, we adopt the dual embedding framework
to capture the intrinsic properties of complementariness and provide geometric
interpretation motivated by the classic separating hyperplane theory. Finally,
we propose a Bayesian network structure that unifies all the components, which
also concludes several popular models as special cases. The proposed method
compares favourably to state-of-art methods, in downstream classification and
recommendation tasks. We also develop an implementation that scales efficiently
to a dataset with millions of items and customers
Musical recommendations and personalization in a social network
This paper presents a set of algorithms used for music recommendations and
personalization in a general purpose social network www.ok.ru, the second
largest social network in the CIS visited by more then 40 millions users per
day. In addition to classical recommendation features like "recommend a
sequence" and "find similar items" the paper describes novel algorithms for
construction of context aware recommendations, personalization of the service,
handling of the cold-start problem, and more. All algorithms described in the
paper are working on-line and are able to detect and address changes in the
user's behavior and needs in the real time.
The core component of the algorithms is a taste graph containing information
about different entities (users, tracks, artists, etc.) and relations between
them (for example, user A likes song B with certainty X, track B created by
artist C, artist C is similar to artist D with certainty Y and so on). Using
the graph it is possible to select tracks a user would most probably like, to
arrange them in a way that they match each other well, to estimate which items
from a fixed list are most relevant for the user, and more.
In addition, the paper describes the approach used to estimate algorithms
efficiency and analyze the impact of different recommendation related features
on the users' behavior and overall activity at the service.Comment: This is a full version of a 4 pages article published at ACM RecSys
201
Contextual Attention Recurrent Architecture for Context-aware Venue Recommendation
Venue recommendation systems aim to effectively rank a list of interesting venues users should visit based on their historical feedback (e.g. checkins). Such systems are increasingly deployed by Location-based Social Networks (LBSNs) such as Foursquare and Yelp to enhance their usefulness to users. Recently, various RNN architectures have been proposed to incorporate contextual information associated with the users' sequence of checkins (e.g. time of the day, location of venues) to effectively capture the users' dynamic preferences. However, these architectures assume that different types of contexts have an identical impact on the users' preferences, which may not hold in practice. For example, an ordinary context such as the time of the day reflects the user's current contextual preferences, whereas a transition context - such as a time interval from their last visited venue - indicates a transition effect from past behaviour to future behaviour. To address these challenges, we propose a novel Contextual Attention Recurrent Architecture (CARA) that leverages both sequences of feedback and contextual information associated with the sequences to capture the users' dynamic preferences. Our proposed recurrent architecture consists of two types of gating mechanisms, namely 1) a contextual attention gate that controls the influence of the ordinary context on the users' contextual preferences and 2) a time- and geo-based gate that controls the influence of the hidden state from the previous checkin based on the transition context. Thorough experiments on three large checkin and rating datasets from commercial LBSNs demonstrate the effectiveness of our proposed CARA architecture by significantly outperforming many state-of-the-art RNN architectures and factorisation approaches
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