5,238 research outputs found
BoostFM: Boosted Factorization Machines for Top-N Feature-based Recommendation
Feature-based matrix factorization techniques such as Factorization Machines (FM) have been proven to achieve impressive accuracy for the rating prediction task. However, most common recommendation scenarios are formulated as a top-N item ranking problem with implicit feedback (e.g., clicks, purchases)rather than explicit ratings. To address this problem, with both implicit feedback and feature information, we propose a feature-based collaborative boosting recommender called BoostFM, which integrates boosting into factorization models during the process of item ranking. Specifically, BoostFM is an adaptive boosting framework that linearly combines multiple homogeneous component recommenders, which are repeatedly constructed on the basis of the individual FM model by a re-weighting scheme. Two ways are proposed to efficiently train the component recommenders from the perspectives of both pairwise and listwise Learning-to-Rank (L2R). The properties of our proposed method are empirically studied on three real-world datasets. The experimental results show that BoostFM outperforms a number of state-of-the-art approaches for top-N recommendation
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
Interacting Attention-gated Recurrent Networks for Recommendation
Capturing the temporal dynamics of user preferences over items is important
for recommendation. Existing methods mainly assume that all time steps in
user-item interaction history are equally relevant to recommendation, which
however does not apply in real-world scenarios where user-item interactions can
often happen accidentally. More importantly, they learn user and item dynamics
separately, thus failing to capture their joint effects on user-item
interactions. To better model user and item dynamics, we present the
Interacting Attention-gated Recurrent Network (IARN) which adopts the attention
model to measure the relevance of each time step. In particular, we propose a
novel attention scheme to learn the attention scores of user and item history
in an interacting way, thus to account for the dependencies between user and
item dynamics in shaping user-item interactions. By doing so, IARN can
selectively memorize different time steps of a user's history when predicting
her preferences over different items. Our model can therefore provide
meaningful interpretations for recommendation results, which could be further
enhanced by auxiliary features. Extensive validation on real-world datasets
shows that IARN consistently outperforms state-of-the-art methods.Comment: Accepted by ACM International Conference on Information and Knowledge
Management (CIKM), 201
Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking
This paper proposes a new neural architecture for collaborative ranking with
implicit feedback. Our model, LRML (\textit{Latent Relational Metric Learning})
is a novel metric learning approach for recommendation. More specifically,
instead of simple push-pull mechanisms between user and item pairs, we propose
to learn latent relations that describe each user item interaction. This helps
to alleviate the potential geometric inflexibility of existing metric learing
approaches. This enables not only better performance but also a greater extent
of modeling capability, allowing our model to scale to a larger number of
interactions. In order to do so, we employ a augmented memory module and learn
to attend over these memory blocks to construct latent relations. The
memory-based attention module is controlled by the user-item interaction,
making the learned relation vector specific to each user-item pair. Hence, this
can be interpreted as learning an exclusive and optimal relational translation
for each user-item interaction. The proposed architecture demonstrates the
state-of-the-art performance across multiple recommendation benchmarks. LRML
outperforms other metric learning models by in terms of Hits@10 and
nDCG@10 on large datasets such as Netflix and MovieLens20M. Moreover,
qualitative studies also demonstrate evidence that our proposed model is able
to infer and encode explicit sentiment, temporal and attribute information
despite being only trained on implicit feedback. As such, this ascertains the
ability of LRML to uncover hidden relational structure within implicit
datasets.Comment: WWW 201
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
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
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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