9,990 research outputs found
KRED: Knowledge-Aware Document Representation for News Recommendations
News articles usually contain knowledge entities such as celebrities or
organizations. Important entities in articles carry key messages and help to
understand the content in a more direct way. An industrial news recommender
system contains various key applications, such as personalized recommendation,
item-to-item recommendation, news category classification, news popularity
prediction and local news detection. We find that incorporating knowledge
entities for better document understanding benefits these applications
consistently. However, existing document understanding models either represent
news articles without considering knowledge entities (e.g., BERT) or rely on a
specific type of text encoding model (e.g., DKN) so that the generalization
ability and efficiency is compromised. In this paper, we propose KRED, which is
a fast and effective model to enhance arbitrary document representation with a
knowledge graph. KRED first enriches entities' embeddings by attentively
aggregating information from their neighborhood in the knowledge graph. Then a
context embedding layer is applied to annotate the dynamic context of different
entities such as frequency, category and position. Finally, an information
distillation layer aggregates the entity embeddings under the guidance of the
original document representation and transforms the document vector into a new
one. We advocate to optimize the model with a multi-task framework, so that
different news recommendation applications can be united and useful information
can be shared across different tasks. Experiments on a real-world Microsoft
News dataset demonstrate that KRED greatly benefits a variety of news
recommendation applications.Comment: RecSys'2
Knowledge Graph semantic enhancement of input data for improving AI
Intelligent systems designed using machine learning algorithms require a
large number of labeled data. Background knowledge provides complementary, real
world factual information that can augment the limited labeled data to train a
machine learning algorithm. The term Knowledge Graph (KG) is in vogue as for
many practical applications, it is convenient and useful to organize this
background knowledge in the form of a graph. Recent academic research and
implemented industrial intelligent systems have shown promising performance for
machine learning algorithms that combine training data with a knowledge graph.
In this article, we discuss the use of relevant KGs to enhance input data for
two applications that use machine learning -- recommendation and community
detection. The KG improves both accuracy and explainability
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
- …