20,485 research outputs found
Discovering both explicit and implicit similarities for cross-domain recommendation
© 2017, Springer International Publishing AG. Recommender System has become one of the most important techniques for businesses today. Improving its performance requires a thorough understanding of latent similarities among users and items. This issue is addressable given recent abundance of datasets across domains. However, the question of how to utilize this cross-domain rich information to improve recommendation performance is still an open problem. In this paper, we propose a cross-domain recommender as the first algorithm utilizing both explicit and implicit similarities between datasets across sources for performance improvement. Validated on real-world datasets, our proposed idea outperforms the current cross-domain recommendation methods by more than 2 times. Yet, the more interesting observation is that both explicit and implicit similarities between datasets help to better suggest unknown information from cross-domain sources
Scalable factorization model to discover implicit and explicit similarities across domains
University of Technology Sydney. Faculty of Engineering and Information Technology.E-commerce businesses increasingly depend on recommendation systems to introduce personalized services and products to their target customers. Achieving accurate recommendations requires a sufficient understanding of user preferences and item characteristics. Given the current innovations on the Web, coupled datasets are abundantly available across domains. An analysis of these datasets can provide a broader knowledge to understand the underlying relationship between users and items. This thorough understanding results in more collaborative filtering power and leads to a higher recommendation accuracy.
However, how to effectively use this knowledge for recommendation is still a challenging problem. In this research, we propose to exploit both explicit and implicit similarities extracted from latent factors across domains with matrix tri-factorization. On the coupled dimensions, common parts of the coupled factors across domains are shared among them. At the same time, their domain-specific parts are preserved. We show that such a configuration of both common and domain-specific parts benefits cross-domain recommendations significantly. Moreover, on the non-coupled dimensions, the middle factor of the tri-factorization is proposed to use to match the closely related clusters across datasets and align the matched ones to transfer cross-domain implicit similarities, further improving the recommendation.
Furthermore, when dealing with data coupled from different sources, the scalability of the analytical method is another significant concern. We design a distributed factorization model that can scale up as the observed data across domains increases. Our data parallelism, based on Apache Spark, enables the model to have the smallest communication cost. Also, the model is equipped with an optimized solver that converges faster. We demonstrate that these key features stabilize our model’s performance when the data grows.
Validated on real-world datasets, our developed model outperforms the existing algorithms regarding recommendation accuracy and scalability. These empirical results illustrate the potential of our research in exploiting both explicit and implicit similarities across domains for improving recommendation performance
Transfer Learning via Contextual Invariants for One-to-Many Cross-Domain Recommendation
The rapid proliferation of new users and items on the social web has
aggravated the gray-sheep user/long-tail item challenge in recommender systems.
Historically, cross-domain co-clustering methods have successfully leveraged
shared users and items across dense and sparse domains to improve inference
quality. However, they rely on shared rating data and cannot scale to multiple
sparse target domains (i.e., the one-to-many transfer setting). This, combined
with the increasing adoption of neural recommender architectures, motivates us
to develop scalable neural layer-transfer approaches for cross-domain learning.
Our key intuition is to guide neural collaborative filtering with
domain-invariant components shared across the dense and sparse domains,
improving the user and item representations learned in the sparse domains. We
leverage contextual invariances across domains to develop these shared modules,
and demonstrate that with user-item interaction context, we can learn-to-learn
informative representation spaces even with sparse interaction data. We show
the effectiveness and scalability of our approach on two public datasets and a
massive transaction dataset from Visa, a global payments technology company
(19% Item Recall, 3x faster vs. training separate models for each domain). Our
approach is applicable to both implicit and explicit feedback settings.Comment: SIGIR 202
Deep Item-based Collaborative Filtering for Top-N Recommendation
Item-based Collaborative Filtering(short for ICF) has been widely adopted in
recommender systems in industry, owing to its strength in user interest
modeling and ease in online personalization. By constructing a user's profile
with the items that the user has consumed, ICF recommends items that are
similar to the user's profile. With the prevalence of machine learning in
recent years, significant processes have been made for ICF by learning item
similarity (or representation) from data. Nevertheless, we argue that most
existing works have only considered linear and shallow relationship between
items, which are insufficient to capture the complicated decision-making
process of users.
In this work, we propose a more expressive ICF solution by accounting for the
nonlinear and higher-order relationship among items. Going beyond modeling only
the second-order interaction (e.g. similarity) between two items, we
additionally consider the interaction among all interacted item pairs by using
nonlinear neural networks. Through this way, we can effectively model the
higher-order relationship among items, capturing more complicated effects in
user decision-making. For example, it can differentiate which historical
itemsets in a user's profile are more important in affecting the user to make a
purchase decision on an item. We treat this solution as a deep variant of ICF,
thus term it as DeepICF. To justify our proposal, we perform empirical studies
on two public datasets from MovieLens and Pinterest. Extensive experiments
verify the highly positive effect of higher-order item interaction modeling
with nonlinear neural networks. Moreover, we demonstrate that by more
fine-grained second-order interaction modeling with attention network, the
performance of our DeepICF method can be further improved.Comment: 25 pages, submitted to TOI
Joint Topic-Semantic-aware Social Recommendation for Online Voting
Online voting is an emerging feature in social networks, in which users can
express their attitudes toward various issues and show their unique interest.
Online voting imposes new challenges on recommendation, because the propagation
of votings heavily depends on the structure of social networks as well as the
content of votings. In this paper, we investigate how to utilize these two
factors in a comprehensive manner when doing voting recommendation. First, due
to the fact that existing text mining methods such as topic model and semantic
model cannot well process the content of votings that is typically short and
ambiguous, we propose a novel Topic-Enhanced Word Embedding (TEWE) method to
learn word and document representation by jointly considering their topics and
semantics. Then we propose our Joint Topic-Semantic-aware social Matrix
Factorization (JTS-MF) model for voting recommendation. JTS-MF model calculates
similarity among users and votings by combining their TEWE representation and
structural information of social networks, and preserves this
topic-semantic-social similarity during matrix factorization. To evaluate the
performance of TEWE representation and JTS-MF model, we conduct extensive
experiments on real online voting dataset. The results prove the efficacy of
our approach against several state-of-the-art baselines.Comment: The 26th ACM International Conference on Information and Knowledge
Management (CIKM 2017
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
Spectral Collaborative Filtering
Despite the popularity of Collaborative Filtering (CF), CF-based methods are
haunted by the \textit{cold-start} problem, which has a significantly negative
impact on users' experiences with Recommender Systems (RS). In this paper, to
overcome the aforementioned drawback, we first formulate the relationships
between users and items as a bipartite graph. Then, we propose a new spectral
convolution operation directly performing in the \textit{spectral domain},
where not only the proximity information of a graph but also the connectivity
information hidden in the graph are revealed. With the proposed spectral
convolution operation, we build a deep recommendation model called Spectral
Collaborative Filtering (SpectralCF). Benefiting from the rich information of
connectivity existing in the \textit{spectral domain}, SpectralCF is capable of
discovering deep connections between users and items and therefore, alleviates
the \textit{cold-start} problem for CF. To the best of our knowledge,
SpectralCF is the first CF-based method directly learning from the
\textit{spectral domains} of user-item bipartite graphs. We apply our method on
several standard datasets. It is shown that SpectralCF significantly
outperforms state-of-the-art models. Code and data are available at
\url{https://github.com/lzheng21/SpectralCF}.Comment: RecSys201
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