525 research outputs found
Context-aware Deep Model for Entity Recommendation in Search Engine at Alibaba
Entity recommendation, providing search users with an improved experience via
assisting them in finding related entities for a given query, has become an
indispensable feature of today's search engines. Existing studies typically
only consider the queries with explicit entities. They usually fail to handle
complex queries that without entities, such as "what food is good for cold
weather", because their models could not infer the underlying meaning of the
input text. In this work, we believe that contexts convey valuable evidence
that could facilitate the semantic modeling of queries, and take them into
consideration for entity recommendation. In order to better model the semantics
of queries and entities, we learn the representation of queries and entities
jointly with attentive deep neural networks. We evaluate our approach using
large-scale, real-world search logs from a widely used commercial Chinese
search engine. Our system has been deployed in ShenMa Search Engine and you can
fetch it in UC Browser of Alibaba. Results from online A/B test suggest that
the impression efficiency of click-through rate increased by 5.1% and page view
increased by 5.5%.Comment: CIKM2019 International Workshop on Entity Retrieval. arXiv admin
note: text overlap with arXiv:1511.08996 by other author
The Short Text Matching Model Enhanced with Knowledge via Contrastive Learning
In recent years, short Text Matching tasks have been widely applied in the
fields ofadvertising search and recommendation. The difficulty lies in the lack
of semantic information and word ambiguity caused by the short length of the
text. Previous works have introduced complement sentences or knowledge bases to
provide additional feature information. However, these methods have not fully
interacted between the original sentence and the complement sentence, and have
not considered the noise issue that may arise from the introduction of external
knowledge bases. Therefore, this paper proposes a short Text Matching model
that combines contrastive learning and external knowledge. The model uses a
generative model to generate corresponding complement sentences and uses the
contrastive learning method to guide the model to obtain more semantically
meaningful encoding of the original sentence. In addition, to avoid noise, we
use keywords as the main semantics of the original sentence to retrieve
corresponding knowledge words in the knowledge base, and construct a knowledge
graph. The graph encoding model is used to integrate the knowledge base
information into the model. Our designed model achieves state-of-the-art
performance on two publicly available Chinese Text Matching datasets,
demonstrating the effectiveness of our model.Comment: 11 pages,2 figure
AliMe KG: Domain Knowledge Graph Construction and Application in E-commerce
Pre-sales customer service is of importance to E-commerce platforms as it
contributes to optimizing customers' buying process. To better serve users, we
propose AliMe KG, a domain knowledge graph in E-commerce that captures user
problems, points of interests (POI), item information and relations thereof. It
helps to understand user needs, answer pre-sales questions and generate
explanation texts. We applied AliMe KG to several online business scenarios
such as shopping guide, question answering over properties and recommendation
reason generation, and gained positive results. In the paper, we systematically
introduce how we construct domain knowledge graph from free text, and
demonstrate its business value with several applications. Our experience shows
that mining structured knowledge from free text in vertical domain is
practicable, and can be of substantial value in industrial settings
Normal vs. Adversarial: Salience-based Analysis of Adversarial Samples for Relation Extraction
Recent neural-based relation extraction approaches, though achieving
promising improvement on benchmark datasets, have reported their vulnerability
towards adversarial attacks. Thus far, efforts mostly focused on generating
adversarial samples or defending adversarial attacks, but little is known about
the difference between normal and adversarial samples. In this work, we take
the first step to leverage the salience-based method to analyze those
adversarial samples. We observe that salience tokens have a direct correlation
with adversarial perturbations. We further find the adversarial perturbations
are either those tokens not existing in the training set or superficial cues
associated with relation labels. To some extent, our approach unveils the
characters against adversarial samples. We release an open-source testbed,
"DiagnoseAdv" in https://github.com/zjunlp/DiagnoseAdv.Comment: IJCKG 202
ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendation
Recommender system (RS) devotes to predicting user preference to a given item
and has been widely deployed in most web-scale applications. Recently,
knowledge graph (KG) attracts much attention in RS due to its abundant
connective information. Existing methods either explore independent meta-paths
for user-item pairs over KG, or employ graph neural network (GNN) on whole KG
to produce representations for users and items separately. Despite
effectiveness, the former type of methods fails to fully capture structural
information implied in KG, while the latter ignores the mutual effect between
target user and item during the embedding propagation. In this work, we propose
a new framework named Adaptive Target-Behavior Relational Graph network (ATBRG
for short) to effectively capture structural relations of target user-item
pairs over KG. Specifically, to associate the given target item with user
behaviors over KG, we propose the graph connect and graph prune techniques to
construct adaptive target-behavior relational graph. To fully distill
structural information from the sub-graph connected by rich relations in an
end-to-end fashion, we elaborate on the model design of ATBRG, equipped with
relation-aware extractor layer and representation activation layer. We perform
extensive experiments on both industrial and benchmark datasets. Empirical
results show that ATBRG consistently and significantly outperforms
state-of-the-art methods. Moreover, ATBRG has also achieved a performance
improvement of 5.1% on CTR metric after successful deployment in one popular
recommendation scenario of Taobao APP.Comment: Accepted by SIGIR 2020, full paper with 10 pages and 5 figure
MTBRN: Multiplex Target-Behavior Relation Enhanced Network for Click-Through Rate Prediction
Click-through rate (CTR) prediction is a critical task for many industrial
systems, such as display advertising and recommender systems. Recently,
modeling user behavior sequences attracts much attention and shows great
improvements in the CTR field. Existing works mainly exploit attention
mechanism based on embedding product when considering relations between user
behaviors and target item. However, this methodology lacks of concrete
semantics and overlooks the underlying reasons driving a user to click on a
target item. In this paper, we propose a new framework named Multiplex
Target-Behavior Relation enhanced Network (MTBRN) to leverage multiplex
relations between user behaviors and target item to enhance CTR prediction.
Multiplex relations consist of meaningful semantics, which can bring a better
understanding on users' interests from different perspectives. To explore and
model multiplex relations, we propose to incorporate various graphs (e.g.,
knowledge graph and item-item similarity graph) to construct multiple
relational paths between user behaviors and target item. Then Bi-LSTM is
applied to encode each path in the path extractor layer. A path fusion network
and a path activation network are devised to adaptively aggregate and finally
learn the representation of all paths for CTR prediction. Extensive offline and
online experiments clearly verify the effectiveness of our framework.Comment: Accepted by CIKM202
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