67 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
Neighborhood Matching Network for Entity Alignment
Structural heterogeneity between knowledge graphs is an outstanding challenge
for entity alignment. This paper presents Neighborhood Matching Network (NMN),
a novel entity alignment framework for tackling the structural heterogeneity
challenge. NMN estimates the similarities between entities to capture both the
topological structure and the neighborhood difference. It provides two
innovative components for better learning representations for entity alignment.
It first uses a novel graph sampling method to distill a discriminative
neighborhood for each entity. It then adopts a cross-graph neighborhood
matching module to jointly encode the neighborhood difference for a given
entity pair. Such strategies allow NMN to effectively construct
matching-oriented entity representations while ignoring noisy neighbors that
have a negative impact on the alignment task. Extensive experiments performed
on three entity alignment datasets show that NMN can well estimate the
neighborhood similarity in more tough cases and significantly outperforms 12
previous state-of-the-art methods.Comment: 11 pages, accepted by ACL 202
Leveraging Negative Signals with Self-Attention for Sequential Music Recommendation
Music streaming services heavily rely on their recommendation engines to
continuously provide content to their consumers. Sequential recommendation
consequently has seen considerable attention in current literature, where state
of the art approaches focus on self-attentive models leveraging contextual
information such as long and short-term user history and item features;
however, most of these studies focus on long-form content domains (retail,
movie, etc.) rather than short-form, such as music. Additionally, many do not
explore incorporating negative session-level feedback during training. In this
study, we investigate the use of transformer-based self-attentive architectures
to learn implicit session-level information for sequential music
recommendation. We additionally propose a contrastive learning task to
incorporate negative feedback (e.g skipped tracks) to promote positive hits and
penalize negative hits. This task is formulated as a simple loss term that can
be incorporated into a variety of deep learning architectures for sequential
recommendation. Our experiments show that this results in consistent
performance gains over the baseline architectures ignoring negative user
feedback.Comment: Accepted to the 1st Workshop on Music Recommender Systems, co-located
with the 17th ACM Conference on Recommender Systems (MuRS @ RecSys 2023
Measuring and Analysing the Chain of Implicit Trust: AStudy of Third-party Resources Loading
The web is a tangled mass of interconnected services, whereby websites import a range of external resources from various third-party domains. The latter can also load further resources hosted on other domains. For each website, this creates a dependency chain underpinned by a form of implicit trust between the first-party and transitively connected third parties. The chain can only be loosely controlled as first-party websites often have little, if any, visibility on where these resources are loaded from. This article performs a large-scale study of dependency chains in the web to find that around 50% of first-party websites render content that they do not directly load. Although the majority (84.91%) of websites have short dependency chains (below three levels), we find websites with dependency chains exceeding 30. Using VirusTotal, we show that 1.2% of these third parties are classified as suspicious—although seemingly small, this limited set of suspicious third parties have remarkable reach into the wider ecosystem. We find that 73% of websites under-study load resources from suspicious third parties, and 24.8% of first-party webpages contain at least three third parties classified as suspicious in their dependency chain. By running sandboxed experiments, we observe a range of activities with the majority of suspicious JavaScript codes downloading malware
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