9 research outputs found
KGAT: Knowledge Graph Attention Network for Recommendation
To provide more accurate, diverse, and explainable recommendation, it is
compulsory to go beyond modeling user-item interactions and take side
information into account. Traditional methods like factorization machine (FM)
cast it as a supervised learning problem, which assumes each interaction as an
independent instance with side information encoded. Due to the overlook of the
relations among instances or items (e.g., the director of a movie is also an
actor of another movie), these methods are insufficient to distill the
collaborative signal from the collective behaviors of users. In this work, we
investigate the utility of knowledge graph (KG), which breaks down the
independent interaction assumption by linking items with their attributes. We
argue that in such a hybrid structure of KG and user-item graph, high-order
relations --- which connect two items with one or multiple linked attributes
--- are an essential factor for successful recommendation. We propose a new
method named Knowledge Graph Attention Network (KGAT) which explicitly models
the high-order connectivities in KG in an end-to-end fashion. It recursively
propagates the embeddings from a node's neighbors (which can be users, items,
or attributes) to refine the node's embedding, and employs an attention
mechanism to discriminate the importance of the neighbors. Our KGAT is
conceptually advantageous to existing KG-based recommendation methods, which
either exploit high-order relations by extracting paths or implicitly modeling
them with regularization. Empirical results on three public benchmarks show
that KGAT significantly outperforms state-of-the-art methods like Neural FM and
RippleNet. Further studies verify the efficacy of embedding propagation for
high-order relation modeling and the interpretability benefits brought by the
attention mechanism.Comment: KDD 2019 research trac
Scientific and Technological News Recommendation Based on Knowledge Graph with User Perception
Existing research usually utilizes side information such as social network or
item attributes to improve the performance of collaborative filtering-based
recommender systems. In this paper, the knowledge graph with user perception is
used to acquire the source of side information. We proposed KGUPN to address
the limitations of existing embedding-based and path-based knowledge
graph-aware recommendation methods, an end-to-end framework that integrates
knowledge graph and user awareness into scientific and technological news
recommendation systems. KGUPN contains three main layers, which are the
propagation representation layer, the contextual information layer and
collaborative relation layer. The propagation representation layer improves the
representation of an entity by recursively propagating embeddings from its
neighbors (which can be users, news, or relationships) in the knowledge graph.
The contextual information layer improves the representation of entities by
encoding the behavioral information of entities appearing in the news. The
collaborative relation layer complements the relationship between entities in
the news knowledge graph. Experimental results on real-world datasets show that
KGUPN significantly outperforms state-of-the-art baselines in scientific and
technological news recommendation
Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph
Graph neural networks (GNN) have recently been applied to exploit knowledge
graph (KG) for recommendation. Existing GNN-based methods explicitly model the
dependency between an entity and its local graph context in KG (i.e., the set
of its first-order neighbors), but may not be effective in capturing its
non-local graph context (i.e., the set of most related high-order neighbors).
In this paper, we propose a novel recommendation framework, named
Contextualized Graph Attention Network (CGAT), which can explicitly exploit
both local and non-local graph context information of an entity in KG.
Specifically, CGAT captures the local context information by a user-specific
graph attention mechanism, considering a user's personalized preferences on
entities. Moreover, CGAT employs a biased random walk sampling process to
extract the non-local context of an entity, and utilizes a Recurrent Neural
Network (RNN) to model the dependency between the entity and its non-local
contextual entities. To capture the user's personalized preferences on items,
an item-specific attention mechanism is also developed to model the dependency
between a target item and the contextual items extracted from the user's
historical behaviors. Experimental results on real datasets demonstrate the
effectiveness of CGAT, compared with state-of-the-art KG-based recommendation
methods
NOTION OF EXPLAINABLE ARTIFICIAL INTELLIGENCE - AN EMPIRICAL INVESTIGATION FROM A USER\u27S PERSPECTIVE
The growing attention on artificial intelligence-based decision-making has led to research interest in the explainability and interpretability of machine learning models, algorithmic transparency, and comprehensibility. This renewed attention on XAI advocates the need to investigate end user-centric explainable AI, due to the universal adoption of AI-based systems at the root level. Therefore, this paper investigates user-centric explainable AI from a recommendation systems context. We conducted focus group interviews to collect qualitative data on the recommendation system. We asked participants about the end users\u27 comprehension of a recommended item, its probable explanation and their opinion of making a recommendation explainable. Our finding reveals end users want a non-technical and tailor-made explanation with on-demand supplementary information. Moreover, we also observed users would like to have an explanation about personal data usage, detailed user feedback, authentic and reliable explanations. Finally, we proposed a synthesized framework that will include end users in the XAI development process
Neuro-Symbolic Recommendation Model based on Logic Query
A recommendation system assists users in finding items that are relevant to
them. Existing recommendation models are primarily based on predicting
relationships between users and items and use complex matching models or
incorporate extensive external information to capture association patterns in
data. However, recommendation is not only a problem of inductive statistics
using data; it is also a cognitive task of reasoning decisions based on
knowledge extracted from information. Hence, a logic system could naturally be
incorporated for the reasoning in a recommendation task. However, although
hard-rule approaches based on logic systems can provide powerful reasoning
ability, they struggle to cope with inconsistent and incomplete knowledge in
real-world tasks, especially for complex tasks such as recommendation.
Therefore, in this paper, we propose a neuro-symbolic recommendation model,
which transforms the user history interactions into a logic expression and then
transforms the recommendation prediction into a query task based on this logic
expression. The logic expressions are then computed based on the modular logic
operations of the neural network. We also construct an implicit logic encoder
to reasonably reduce the complexity of the logic computation. Finally, a user's
interest items can be queried in the vector space based on the computation
results. Experiments on three well-known datasets verified that our method
performs better compared to state of the art shallow, deep, session, and
reasoning models.Comment: 17 pages, 6 figure