939 research outputs found
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
CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation
Recent research explores incorporating knowledge graphs (KG) into e-commerce
recommender systems, not only to achieve better recommendation performance, but
more importantly to generate explanations of why particular decisions are made.
This can be achieved by explicit KG reasoning, where a model starts from a user
node, sequentially determines the next step, and walks towards an item node of
potential interest to the user. However, this is challenging due to the huge
search space, unknown destination, and sparse signals over the KG, so
informative and effective guidance is needed to achieve a satisfactory
recommendation quality. To this end, we propose a CoArse-to-FinE neural
symbolic reasoning approach (CAFE). It first generates user profiles as coarse
sketches of user behaviors, which subsequently guide a path-finding process to
derive reasoning paths for recommendations as fine-grained predictions. User
profiles can capture prominent user behaviors from the history, and provide
valuable signals about which kinds of path patterns are more likely to lead to
potential items of interest for the user. To better exploit the user profiles,
an improved path-finding algorithm called Profile-guided Path Reasoning (PPR)
is also developed, which leverages an inventory of neural symbolic reasoning
modules to effectively and efficiently find a batch of paths over a large-scale
KG. We extensively experiment on four real-world benchmarks and observe
substantial gains in the recommendation performance compared with
state-of-the-art methods.Comment: Accepted in CIKM 202
Toward Sustainable Recommendation Systems
Recommendation systems are ubiquitous, acting as an essential component in online platforms to help users discover items of interest. For example, streaming services rely on recommendation systems to serve high-quality informational and entertaining content to their users, and e-commerce platforms recommend interesting items to assist customers in making shopping decisions. Further-more, the algorithms and frameworks driving recommendation systems provide the foundation for new personalized machine learning methods that have wide-ranging impacts.
While successful, many current recommendation systems are fundamentally not sustainable: they focus on short-lived engagement objectives, requiring constant fine-tuning to adapt to the dynamics of evolving systems, or are subject to performance degradation as users and items churn in the system. In this dissertation research, we seek to lay the foundations for a new class of sustainable recommendation systems. By sustainable, we mean a recommendation system should be fundamentally long-lived, while enhancing both current and future potential to connect users with interesting content. By building such sustainable recommendation systems, we can continuously improve the user experience and provide a long-lived foundation for ongoing engagement. Building on a large body of work in recommendation systems, with the advance in graph neural networks, and with recent success in meta-learning for ML-based models, this dissertation focuses on sustainability in recommendation systems from the following three perspectives with corresponding contributions:
• Adaptivity: The first contribution lies in capturing the temporal effects from the instant shifting of users’ preferences to the lifelong evolution of users and items in real-world scenarios, leading to models which are highly adaptive to the temporal dynamics present in online platforms and provide improved item recommendation at different timestamps.
• Resilience: Secondly, we seek to identify the elite users who act as the “backbone” recommendation systems shape the opinions of other users via their public activities. By investigating the correlation between user’s preference on item consumption and their connections to the “backbone”, we enable recommendation models to be resilient to dramatic changes including churn in new items and users, and frequently updated connections between users in online communities.
• Robustness: Finally, we explore the design of a novel framework for “learning-to-adapt” to the imperfect test cases in recommendation systems ranging from cold-start users with few interactions to casual users with low activity levels. Such a model is robust to the imperfection in real-world environments, resulting in reliable recommendation to meet user needs and aspirations
Computational Technologies for Fashion Recommendation: A Survey
Fashion recommendation is a key research field in computational fashion
research and has attracted considerable interest in the computer vision,
multimedia, and information retrieval communities in recent years. Due to the
great demand for applications, various fashion recommendation tasks, such as
personalized fashion product recommendation, complementary (mix-and-match)
recommendation, and outfit recommendation, have been posed and explored in the
literature. The continuing research attention and advances impel us to look
back and in-depth into the field for a better understanding. In this paper, we
comprehensively review recent research efforts on fashion recommendation from a
technological perspective. We first introduce fashion recommendation at a macro
level and analyse its characteristics and differences with general
recommendation tasks. We then clearly categorize different fashion
recommendation efforts into several sub-tasks and focus on each sub-task in
terms of its problem formulation, research focus, state-of-the-art methods, and
limitations. We also summarize the datasets proposed in the literature for use
in fashion recommendation studies to give readers a brief illustration.
Finally, we discuss several promising directions for future research in this
field. Overall, this survey systematically reviews the development of fashion
recommendation research. It also discusses the current limitations and gaps
between academic research and the real needs of the fashion industry. In the
process, we offer a deep insight into how the fashion industry could benefit
from fashion recommendation technologies. the computational technologies of
fashion recommendation
Graph Enhanced Representation Learning for News Recommendation
With the explosion of online news, personalized news recommendation becomes
increasingly important for online news platforms to help their users find
interesting information. Existing news recommendation methods achieve
personalization by building accurate news representations from news content and
user representations from their direct interactions with news (e.g., click),
while ignoring the high-order relatedness between users and news. Here we
propose a news recommendation method which can enhance the representation
learning of users and news by modeling their relatedness in a graph setting. In
our method, users and news are both viewed as nodes in a bipartite graph
constructed from historical user click behaviors. For news representations, a
transformer architecture is first exploited to build news semantic
representations. Then we combine it with the information from neighbor news in
the graph via a graph attention network. For user representations, we not only
represent users from their historically clicked news, but also attentively
incorporate the representations of their neighbor users in the graph. Improved
performances on a large-scale real-world dataset validate the effectiveness of
our proposed method
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Spatio-temporal patterns of human mobility from geo-social networks for urban computing: Analysis, models & applications
The availability of rich information about fine-grained user mobility in urban environments from increasingly geographically-aware social networking services and the rapid development of machine learning applications greatly facilitate the investigation of urban issues. In this setting, urban computing emerges intending to tackle a variety of challenges faced by cities nowadays and to offer promising approaches to improving our living environment. Leveraging massive amounts of data from geo-social networks with unprecedented richness, we show how to devise novel algorithmic techniques to reveal underlying urban mobility patterns for better policy-making and more efficient mobile applications in this dissertation.
Building upon the foundation of existing research efforts in urban computing field and basic machine learning techniques, in this dissertation, we propose a general framework of urban computing with geo-social network data and develop novel algorithms tailored for three urban computing tasks. We begin by exploring how the transition data recording human movements between urban venues from geo-social networks can be aggregated and utilised to detect spatio-temporal changes of local graphs in urban areas. We further explore how this can be used as a proxy to track and predict socio-economic deprivation changes as government financial effort is put in developing areas by supervised machine learning methods. We then study how to extract latent patterns from collective user-venue interactions with the help of a spatio-temporal aware topic modeling approach for the benefit of urban
infrastructure planning. After that, we propose a model to detect the gap between user-side demand and venue-side supply levels for certain types of services in urban environments to suggest further policymaking and investment optimisation. Finally, we address a mobility prediction task, the application aim of which is to recommend new places to explore in the city for mobile users. To this end, we develop a deep learning framework that integrates memory network and topic modeling techniques. Extensive experiments indicate that the proposed architecture can enhance the prediction performance in various recommendation scenarios with high interpretability.
All in all, the insights drawn and the techniques developed in this dissertation make a substantial step in addressing issues in cities and open the door to future possibilities in the promising urban computing area
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