20,715 research outputs found
Relation Embedding for Personalised POI Recommendation
Point-of-Interest (POI) recommendation is one of the most important
location-based services helping people discover interesting venues or services.
However, the extreme user-POI matrix sparsity and the varying spatio-temporal
context pose challenges for POI systems, which affects the quality of POI
recommendations. To this end, we propose a translation-based relation embedding
for POI recommendation. Our approach encodes the temporal and geographic
information, as well as semantic contents effectively in a low-dimensional
relation space by using Knowledge Graph Embedding techniques. To further
alleviate the issue of user-POI matrix sparsity, a combined matrix
factorization framework is built on a user-POI graph to enhance the inference
of dynamic personal interests by exploiting the side-information. Experiments
on two real-world datasets demonstrate the effectiveness of our proposed model.Comment: 12 pages, 3 figures, Accepted in the 24th Pacific-Asia Conference on
Knowledge Discovery and Data Mining (PAKDD 2020
Network Embedding Learning in Knowledge Graph
University of Technology Sydney. Faculty of Engineering and Information Technology.Knowledge Graph stores a large number of human knowledge facts in form of multi-relational network structure, is widely used as a core technique in real-world applications including search engine, question answering system, and recommender system. Knowledge Graph is used to provide extra info box for user query in Google search engine, the WolframAlpha site provides question answering service relying on Knowledge Graph, and the eBay uses Knowledge Graph as semantic enhance for their recommendation service.
Motivated by several characteristics of Knowledge Graph including incompleteness, structural inferability, and semantical application enhancement, a few efforts have been put into the Knowledge Graph analysis area. Some works contribute to Knowledge Graph construction and maintenance through crowdsourcing. Some previous network embedding learning models show good performance on homogeneous network analysis, while the performance of directly using them on Knowledge Graph is limited because the multiple relationship information of the Knowledge Graph is ignored. Then, the concept of Knowledge Graph embedding learning is given, by learning representation for Knowledge Graph components including entities and relations, the latent semantic information is extracted into embedding representation. And the embedding techniques are also utilized in collaborative learning for Knowledge Graph and external application scenarios, the target is to use Knowledge Graph as a semantic enhancement to improve the performance of external applications.
However, some problems still remain in Knowledge Graph completion, reasoning, and external application. First, a proper model is required for Knowledge Graph self-completion, and a proper integration solution is also required to add extra conceptual taxonomy information into the process of Knowledge Graph completion. Then, a framework to use sub-structure information of Knowledge Graph network into knowledge reasoning is needed. After that, a collaborative learning framework for knowledge graph completion and downstream machine learning tasks is needed to be designed. In this thesis, we take recommender systems as an example of downstream machine learning tasks.
To address the aforementioned research problems, a few approaches are proposed in the works introduced in this thesis.
• A bipartite graph embedding based Knowledge Graph completion approach for Knowledge Graph self-completion, each knowledge fact is represented in the form of bipartite graph structure for more reasonable triple inference.
• An embedding based cross completion approach for completing the factual Knowledge Graph with additive conceptual taxonomy information, the components of factual Knowledge Graph and conceptual taxonomy, entities, relations, types, are jointly represented by embedding representation.
• Two sub-structure based Knowledge Graph transitive relation embedding approaches for knowledge reasoning analysis based on Knowledge Graph sub-structure, the transitive structural information contained in Knowledge Graph network substructure is learned into relation embedding.
• Two hierarchical collaborative embedding approaches for proper collaborative learning on Knowledge Graph and Recommender System through linking Knowledge Graph entities with Recommender items, then entities, relations, items, and users are represented by embedding in collaborative space.
The main contributions of this thesis are proposing a few approaches which can be used in multiple Knowledge Graph related domains, Knowledge Graph completion, reasoning and application. Two approaches achieve more accurate Knowledge Graph completion, other two approaches model knowledge reasoning based on network substructure analysis, and the other approaches apply Knowledge Graph into a recommender system application
Graphing else matters: exploiting aspect opinions and ratings in explainable graph-based recommendations
The success of neural network embeddings has entailed a renewed interest in
using knowledge graphs for a wide variety of machine learning and information
retrieval tasks. In particular, current recommendation methods based on graph
embeddings have shown state-of-the-art performance. These methods commonly
encode latent rating patterns and content features. Different from previous
work, in this paper, we propose to exploit embeddings extracted from graphs
that combine information from ratings and aspect-based opinions expressed in
textual reviews. We then adapt and evaluate state-of-the-art graph embedding
techniques over graphs generated from Amazon and Yelp reviews on six domains,
outperforming baseline recommenders. Our approach has the advantage of
providing explanations which leverage aspect-based opinions given by users
about recommended items. Furthermore, we also provide examples of the
applicability of recommendations utilizing aspect opinions as explanations in a
visualization dashboard, which allows obtaining information about the most and
least liked aspects of similar users obtained from the embeddings of an input
graph
Rating and aspect-based opinion graph embeddings for explainable recommendations
The success of neural network embeddings has entailed a renewed interest in
using knowledge graphs for a wide variety of machine learning and information
retrieval tasks. In particular, recent recommendation methods based on graph
embeddings have shown state-of-the-art performance. In general, these methods
encode latent rating patterns and content features. Differently from previous
work, in this paper, we propose to exploit embeddings extracted from graphs
that combine information from ratings and aspect-based opinions expressed in
textual reviews. We then adapt and evaluate state-of-the-art graph embedding
techniques over graphs generated from Amazon and Yelp reviews on six domains,
outperforming baseline recommenders. Additionally, our method has the advantage
of providing explanations that involve the coverage of aspect-based opinions
given by users about recommended items.Comment: arXiv admin note: substantial text overlap with arXiv:2107.0322
Performance Enhancement of Unified Recommendation and Knowledge Graph Completion Learning by Relation Rotation
Advances in multi-task learning (MTL) models have improved the performance and explainability of recommender systems (RS) by jointly learning the recommendation and knowledge graph completion (KGC) tasks. Recent studies have established that considering the incomplete nature of knowledge graphs (KG) can further enhance the performance of RS. However, most existing MTL models depend on translation-based knowledge graph embedding (KGE) methods for KGC, which cannot capture various relation patterns, including composition relations that are prevalent in real-world KG.
To address this limitation, this thesis proposes a new MTL model, named rotational knowledge-enhanced translation-based user preference (RKTUP). RKTUP enhances the KGC task by incorporating rotational-based KGE techniques (RotatE or HRotatE) to model and infer diverse relation patterns. These relation patterns include symmetry/asymmetry, composition, and inversion. RKTUP is an advanced variant of the knowledge-enhanced translation-based user preference (KTUP) MTL model, which provides interpretations of its recommendations.
The experimental results demonstrate that RKTUP outperforms existing methods and achieves state-of-the-art performance on both recommendation and KGC tasks. Specifically, it shows a 13.7% and 11.6% improvement in F1 score for recommendations on DBbook2014 and MovieLens-1m, respectively, and a 12.8% and 13.6% increase in hit ratio for KGC on the same datasets, respectively.
The use of RotatE improves the two tasks’ performance, while HRotatE enhances the two tasks’ performance and the model’s efficiency
Neural Graph Collaborative Filtering
Learning vector representations (aka. embeddings) of users and items lies at
the core of modern recommender systems. Ranging from early matrix factorization
to recently emerged deep learning based methods, existing efforts typically
obtain a user's (or an item's) embedding by mapping from pre-existing features
that describe the user (or the item), such as ID and attributes. We argue that
an inherent drawback of such methods is that, the collaborative signal, which
is latent in user-item interactions, is not encoded in the embedding process.
As such, the resultant embeddings may not be sufficient to capture the
collaborative filtering effect.
In this work, we propose to integrate the user-item interactions -- more
specifically the bipartite graph structure -- into the embedding process. We
develop a new recommendation framework Neural Graph Collaborative Filtering
(NGCF), which exploits the user-item graph structure by propagating embeddings
on it. This leads to the expressive modeling of high-order connectivity in
user-item graph, effectively injecting the collaborative signal into the
embedding process in an explicit manner. We conduct extensive experiments on
three public benchmarks, demonstrating significant improvements over several
state-of-the-art models like HOP-Rec and Collaborative Memory Network. Further
analysis verifies the importance of embedding propagation for learning better
user and item representations, justifying the rationality and effectiveness of
NGCF. Codes are available at
https://github.com/xiangwang1223/neural_graph_collaborative_filtering.Comment: SIGIR 2019; the latest version of NGCF paper, which is distinct from
the version published in ACM Digital Librar
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
Recent advancements in deep neural networks for graph-structured data have
led to state-of-the-art performance on recommender system benchmarks. However,
making these methods practical and scalable to web-scale recommendation tasks
with billions of items and hundreds of millions of users remains a challenge.
Here we describe a large-scale deep recommendation engine that we developed and
deployed at Pinterest. We develop a data-efficient Graph Convolutional Network
(GCN) algorithm PinSage, which combines efficient random walks and graph
convolutions to generate embeddings of nodes (i.e., items) that incorporate
both graph structure as well as node feature information. Compared to prior GCN
approaches, we develop a novel method based on highly efficient random walks to
structure the convolutions and design a novel training strategy that relies on
harder-and-harder training examples to improve robustness and convergence of
the model. We also develop an efficient MapReduce model inference algorithm to
generate embeddings using a trained model. We deploy PinSage at Pinterest and
train it on 7.5 billion examples on a graph with 3 billion nodes representing
pins and boards, and 18 billion edges. According to offline metrics, user
studies and A/B tests, PinSage generates higher-quality recommendations than
comparable deep learning and graph-based alternatives. To our knowledge, this
is the largest application of deep graph embeddings to date and paves the way
for a new generation of web-scale recommender systems based on graph
convolutional architectures.Comment: KDD 201
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