5,586 research outputs found
Disentangled Graph Social Recommendation
Social recommender systems have drawn a lot of attention in many online web
services, because of the incorporation of social information between users in
improving recommendation results. Despite the significant progress made by
existing solutions, we argue that current methods fall short in two
limitations: (1) Existing social-aware recommendation models only consider
collaborative similarity between items, how to incorporate item-wise semantic
relatedness is less explored in current recommendation paradigms. (2) Current
social recommender systems neglect the entanglement of the latent factors over
heterogeneous relations (e.g., social connections, user-item interactions).
Learning the disentangled representations with relation heterogeneity poses
great challenge for social recommendation. In this work, we design a
Disentangled Graph Neural Network (DGNN) with the integration of latent memory
units, which empowers DGNN to maintain factorized representations for
heterogeneous types of user and item connections. Additionally, we devise new
memory-augmented message propagation and aggregation schemes under the graph
neural architecture, allowing us to recursively distill semantic relatedness
into the representations of users and items in a fully automatic manner.
Extensive experiments on three benchmark datasets verify the effectiveness of
our model by achieving great improvement over state-of-the-art recommendation
techniques. The source code is publicly available at:
https://github.com/HKUDS/DGNN.Comment: Accepted by IEEE ICDE 202
Recent Developments in Recommender Systems: A Survey
In this technical survey, we comprehensively summarize the latest
advancements in the field of recommender systems. The objective of this study
is to provide an overview of the current state-of-the-art in the field and
highlight the latest trends in the development of recommender systems. The
study starts with a comprehensive summary of the main taxonomy of recommender
systems, including personalized and group recommender systems, and then delves
into the category of knowledge-based recommender systems. In addition, the
survey analyzes the robustness, data bias, and fairness issues in recommender
systems, summarizing the evaluation metrics used to assess the performance of
these systems. Finally, the study provides insights into the latest trends in
the development of recommender systems and highlights the new directions for
future research in the field
Off-policy Learning over Heterogeneous Information for Recommendation
Reinforcement learning has recently become an active topic in recommender system research, where the logged data that records interactions between items and users feedback is used to discover the policy. Much off-policy learning, referring to the procedure of policy optimization with access only to logged feedback data, has been a popular research topic in reinforcement learning. However, the log entries are biased in that the logs over-represent actions favored by the recommender system, as the user feedback contains only partial information limited to the particular items exposed to the user. As a result, the policy learned from such off-line logged data tends to be biased from the true behaviour policy. In this paper, we are the first to propose a novel off-policy learning augmented by meta-paths for the recommendation. We argue that the Heterogeneous information network (HIN), which provides rich contextual information of items and user aspects, could scale the logged data contribution for unbiased target policy learning. Towards this end, we develop a new HIN augmented target policy model (HINpolicy), which explicitly leverages contextual information to scale the generated reward for target policy. In addition, being equipped with the HINpolicy model, our solution adaptively receives HIN-augmented corrections for counterfactual risk minimization, and ultimately yields an effective policy to maximize the long run rewards for the recommendation. Finally, we extensively evaluate our method through a series of simulations and large-scale real-world datasets, obtaining favorable results compared with state-of-the-art methods
Recommending on graphs: a comprehensive review from a data perspective
Recent advances in graph-based learning approaches have demonstrated their
effectiveness in modelling users' preferences and items' characteristics for
Recommender Systems (RSS). Most of the data in RSS can be organized into graphs
where various objects (e.g., users, items, and attributes) are explicitly or
implicitly connected and influence each other via various relations. Such a
graph-based organization brings benefits to exploiting potential properties in
graph learning (e.g., random walk and network embedding) techniques to enrich
the representations of the user and item nodes, which is an essential factor
for successful recommendations. In this paper, we provide a comprehensive
survey of Graph Learning-based Recommender Systems (GLRSs). Specifically, we
start from a data-driven perspective to systematically categorize various
graphs in GLRSs and analyze their characteristics. Then, we discuss the
state-of-the-art frameworks with a focus on the graph learning module and how
they address practical recommendation challenges such as scalability, fairness,
diversity, explainability and so on. Finally, we share some potential research
directions in this rapidly growing area.Comment: Accepted by UMUA
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Learning from Sequential User Data: Models and Sample-efficient Algorithms
Recent advances in deep learning have made learning representation from ever-growing datasets possible in the domain of vision, natural language processing (NLP), and robotics, among others. However, deep networks are notoriously data-hungry; for example, training language models with attention mechanisms sometimes requires trillions of parameters and tokens. In contrast, we can often access a limited number of samples in many tasks. It is crucial to learn models from these `limited\u27 datasets. Learning with limited datasets can take several forms. In this thesis, we study how to select data samples sequentially such that downstream task performance is maximized. Moreover, we study how to introduce prior knowledge in the deep networks to maximize prediction performance. We focus on four sequential tasks: computerized adaptive testing in psychometrics, sketching in recommender systems, knowledge tracing in computer-assisted education, and career path modeling in the labor market.
In the first two tasks, we devise novel sample-efficient algorithms to query a minimal number of sequential samples to improve future predictions. We propose a Bilevel Optimization-Based framework for computerized adaptive testing to learn a data-driven question selection algorithm that improves existing data selection policies. We also tackle the sketching problem in the recommender system, with the task of recommending the next item using a stored subset of prior data samples. In this setting, we develop a data-driven sequential selection algorithm that tackles evolving downstream task distribution. In the last two tasks, we devise novel neural models to introduce prior knowledge exploiting limited data samples. For knowledge tracing, we propose a novel neural architecture, inspired by cognitive and psychometric models, to improve the prediction of students\u27 future performance and utilize the labeled data samples efficiently. For career path modeling, we propose a novel and interpretable monotonic nonlinear state-space model to analyze online user professional profiles and provide actionable feedback and recommendations to users on how they can reach their career goals.
The data-driven differentiable data selection algorithms for the first two tasks open up future directions to query (a non-differentiable operation) a minimal number of samples optimally to maximize prediction performance. The structures, introduced in the neural architecture for the models in the last two tasks using prior knowledge, open up future directions to learn deep models augmented with prior knowledge using limited data samples
Hierarchical Transformer with Spatio-Temporal Context Aggregation for Next Point-of-Interest Recommendation
Next point-of-interest (POI) recommendation is a critical task in
location-based social networks, yet remains challenging due to a high degree of
variation and personalization exhibited in user movements. In this work, we
explore the latent hierarchical structure composed of multi-granularity
short-term structural patterns in user check-in sequences. We propose a
Spatio-Temporal context AggRegated Hierarchical Transformer (STAR-HiT) for next
POI recommendation, which employs stacked hierarchical encoders to recursively
encode the spatio-temporal context and explicitly locate subsequences of
different granularities. More specifically, in each encoder, the global
attention layer captures the spatio-temporal context of the sequence, while the
local attention layer performed within each subsequence enhances subsequence
modeling using the local context. The sequence partition layer infers positions
and lengths of subsequences from the global context adaptively, such that
semantics in subsequences can be well preserved. Finally, the subsequence
aggregation layer fuses representations within each subsequence to form the
corresponding subsequence representation, thereby generating a new sequence of
higher-level granularity. The stacking of encoders captures the latent
hierarchical structure of the check-in sequence, which is used to predict the
next visiting POI. Extensive experiments on three public datasets demonstrate
that the proposed model achieves superior performance whilst providing
explanations for recommendations. Codes are available at
https://github.com/JennyXieJiayi/STAR-HiT
A Survey on Knowledge Graphs: Representation, Acquisition and Applications
Human knowledge provides a formal understanding of the world. Knowledge
graphs that represent structural relations between entities have become an
increasingly popular research direction towards cognition and human-level
intelligence. In this survey, we provide a comprehensive review of knowledge
graph covering overall research topics about 1) knowledge graph representation
learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph,
and 4) knowledge-aware applications, and summarize recent breakthroughs and
perspective directions to facilitate future research. We propose a full-view
categorization and new taxonomies on these topics. Knowledge graph embedding is
organized from four aspects of representation space, scoring function, encoding
models, and auxiliary information. For knowledge acquisition, especially
knowledge graph completion, embedding methods, path inference, and logical rule
reasoning, are reviewed. We further explore several emerging topics, including
meta relational learning, commonsense reasoning, and temporal knowledge graphs.
To facilitate future research on knowledge graphs, we also provide a curated
collection of datasets and open-source libraries on different tasks. In the
end, we have a thorough outlook on several promising research directions
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