pwc-archive/papers-with-abstracts huggingface dataset:
Causal relationship between eWOM topics and profit of rural tourism at Japanese Roadside Stations "MICHINOEKI"
Authors: Elisa Claire Alemán Carreón, Tetsuro Ito, Hirofumi Nonaka, Minoru Kumano, Toru Hiraoka, Masaharu Hirota
Date: 2019-04-25 00:00:00

Affected by urbanization, centralization and the decrease of overall
population, Japan has been making efforts to revitalize the rural areas across
the country. One particular effort is to increase tourism to these rural areas
via regional branding, using local farm products as tourist attractions across
Japan. Particularly, a program subsidized by the government called Michinoeki,
which stands for 'roadside station', was created 20 years ago and it strives to
provide a safe and comfortable space for cultural interaction between road
travelers and the local community, as well as offering refreshment, and
relevant information to travelers. However, despite its importance in the
revitalization of the Japanese economy, studies with newer technologies and
methodologies are lacking. Using sales data from establishments in the Kyushu
area of Japan, we used Support Vector to classify content from Twitter into
relevant topics and studied their causal relationship to the sales for each
establishment using LiNGAM, a linear non-gaussian acyclic model built for
causal structure analysis, to perform an improved market analysis considering
more than just correlation. Under the hypotheses stated by the LiNGAM model, we
discovered a positive causal relationship between the number of tweets
mentioning those establishments, specially mentioning deserts, a need for
better access and traf^ic options, and a potentially untapped customer base in
motorcycle biker groups.
Community Detection and Growth Potential Prediction from Patent Citation Networks
Authors: Asahi Hentona, Takeshi Sakumoto, Hugo Alberto Mendoza España, Hirofumi Nonaka, Shotaro Kataoka, Toru Hiraoka, Kensei Nakai, Elisa Claire Alemán Carreón, Masaharu Hirota
Date: 2019-04-23 00:00:00
Tasks: Clustering, Community Detection, Management, Prediction, Time Series, Time Series Analysis

The scoring of patents is useful for technology management analysis.
Therefore, a necessity of developing citation network clustering and prediction
of future citations for practical patent scoring arises. In this paper, we
propose a community detection method using the Node2vec. And in order to
analyze growth potential we compare three ''time series analysis methods'', the
Long Short-Term Memory (LSTM), ARIMA model, and Hawkes Process. The results of
our experiments, we could find common technical points from those clusters by
Node2vec. Furthermore, we found that the prediction accuracy of the ARIMA model
was higher than that of other models.
Knowledge-aware Complementary Product Representation Learning
Authors: Da Xu, Chuanwei Ruan, Jason Cho, Evren Korpeoglu, Sushant Kumar, Kannan Achan
Date: 2019-03-16 00:00:00
Tasks: Multi-Task Learning, Recommendation Systems, Representation Learning

Learning product representations that reflect complementary relationship plays a central role in e-commerce recommender system. In the absence of the product relationships graph, which existing methods rely on, there is a need to detect the complementary relationships directly from noisy and sparse customer purchase activities. Furthermore, unlike simple relationships such as similarity, complementariness is asymmetric and non-transitive. Standard usage of representation learning emphasizes on only one set of embedding, which is problematic for modelling such properties of complementariness. We propose using knowledge-aware learning with dual product embedding to solve the above challenges. We encode contextual knowledge into product representation by multi-task learning, to alleviate the sparsity issue. By explicitly modelling with user bias terms, we separate the noise of customer-specific preferences from the complementariness. Furthermore, we adopt the dual embedding framework to capture the intrinsic properties of complementariness and provide geometric interpretation motivated by the classic separating hyperplane theory. Finally, we propose a Bayesian network structure that unifies all the components, which also concludes several popular models as special cases. The proposed method compares favourably to state-of-art methods, in downstream classification and recommendation tasks. We also develop an implementation that scales efficiently to a dataset with millions of items and customers.
Knowledge Graph Convolutional Networks for Recommender Systems
Authors: Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, Minyi Guo
Date: 2019-03-18 00:00:00
Tasks: Click-Through Rate Prediction, Collaborative Filtering, Link Prediction, Music Recommendation, Recommendation Systems

To alleviate sparsity and cold start problem of collaborative filtering based
recommender systems, researchers and engineers usually collect attributes of
users and items, and design delicate algorithms to exploit these additional
information. In general, the attributes are not isolated but connected with
each other, which forms a knowledge graph (KG). In this paper, we propose
Knowledge Graph Convolutional Networks (KGCN), an end-to-end framework that
captures inter-item relatedness effectively by mining their associated
attributes on the KG. To automatically discover both high-order structure
information and semantic information of the KG, we sample from the neighbors
for each entity in the KG as their receptive field, then combine neighborhood
information with bias when calculating the representation of a given entity.
The receptive field can be extended to multiple hops away to model high-order
proximity information and capture users' potential long-distance interests.
Moreover, we implement the proposed KGCN in a minibatch fashion, which enables
our model to operate on large datasets and KGs. We apply the proposed model to
three datasets about movie, book, and music recommendation, and experiment
results demonstrate that our approach outperforms strong recommender baselines.
Link Stream Graph for Temporal Recommendations
Authors: Armel Jacques Nzekon Nzeko'o, Maurice Tchuente, Matthieu Latapy
Date: 2019-03-27 00:00:00
Tasks: Recommendation Systems

Several researches on recommender systems are based on explicit rating data,
but in many real world e-commerce platforms, ratings are not always available,
and in those situations, recommender systems have to deal with implicit data
such as users' purchase history, browsing history and streaming history. In
this context, classical bipartite user-item graphs (BIP) are widely used to
compute top-N recommendations. However, these graphs have some limitations,
particularly in terms of taking temporal dynamic into account. This is not good
because users' preference change over time. To overcome this limit, the
Session-based Temporal Graph (STG) was proposed by Xiang et al. to combine
long- and short-term preferences in a graph-based recommender system. But in
the STG, time is divided into slices and therefore considered discontinuously.
This approach loses details of the real temporal dynamics of user actions. To
address this challenge, we propose the Link Stream Graph (LSG) which is an
extension of link stream representation proposed by Latapy et al. and which
allows to model interactions between users and items by considering time
continuously. Experiments conducted on four real world implicit datasets for
temporal recommendation, with 3 evaluation metrics, show that LSG is the best
in 9 out of 12 cases compared to BIP and STG which are the most used
state-of-the-art recommender graphs.
Table understanding in structured documents
Authors: Martin Holeček, Antonín Hoskovec, Petr Baudiš, Pavel Klinger
Date: 2019-03-22 00:00:00
Tasks: Table Detection

Abstract--- Table detection and extraction has been studied in the context of documents like reports, where tables are clearly outlined and stand out from the document structure visually. We study this topic in a rather more challenging domain of layout-heavy business documents, particularly invoices. Invoices present the novel challenges of tables being often without outlines - either in the form of borders or surrounding text flow - with ragged columns and widely varying data content. We will also show, that we can extract specific information from structurally different tables or table-like structures with one model. We present a comprehensive representation of a page using graph over word boxes, positional embeddings, trainable textual features and rephrase the table detection as a text box labeling problem. We will work on our newly presented dataset of pro forma invoices, invoices and debit note documents using this representation and propose multiple baselines to solve this labeling problem. We then propose a novel neural network model that achieves strong, practical results on the presented dataset and analyze the model performance and effects of graph convolutions and self-attention in detail.
HAXMLNet: Hierarchical Attention Network for Extreme Multi-Label Text Classification
Authors: Ronghui You, Zihan Zhang, Suyang Dai, Shanfeng Zhu
Date: 2019-03-24 00:00:00
Tasks: Classification, General Classification, Multi Label Text Classification, Multi-Label Text Classification, text-classification, Text Classification

Extreme multi-label text classification (XMTC) addresses the problem of
tagging each text with the most relevant labels from an extreme-scale label
set. Traditional methods use bag-of-words (BOW) representations without context
information as their features. The state-ot-the-art deep learning-based method,
AttentionXML, which uses a recurrent neural network (RNN) and the multi-label
attention, can hardly deal with extreme-scale (hundreds of thousands labels)
problem. To address this, we propose our HAXMLNet, which uses an efficient and
effective hierarchical structure with the multi-label attention. Experimental
results show that HAXMLNet reaches a competitive performance with other
state-of-the-art methods.
Operation-aware Neural Networks for User Response Prediction
Authors: Yi Yang, Baile Xu, Furao Shen, Jian Zhao
Date: 2019-04-02 00:00:00
Tasks: Prediction

User response prediction makes a crucial contribution to the rapid
development of online advertising system and recommendation system. The
importance of learning feature interactions has been emphasized by many works.
Many deep models are proposed to automatically learn high-order feature
interactions. Since most features in advertising system and recommendation
system are high-dimensional sparse features, deep models usually learn a
low-dimensional distributed representation for each feature in the bottom
layer. Besides traditional fully-connected architectures, some new operations,
such as convolutional operations and product operations, are proposed to learn
feature interactions better. In these models, the representation is shared
among different operations. However, the best representation for different
operations may be different. In this paper, we propose a new neural model named
Operation-aware Neural Networks (ONN) which learns different representations
for different operations. Our experimental results on two large-scale
real-world ad click/conversion datasets demonstrate that ONN consistently
outperforms the state-of-the-art models in both offline-training environment
and online-training environment.
Text Classification Components for Detecting Descriptions and Names of CAD models
Authors: Thomas Köllmer, Jens Hasselbach, Patrick Aichroth
Date: 2019-04-04 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification, Word Embeddings

We apply text analysis approaches for a specialized search engine for 3D CAD
models and associated products. The main goals are to distinguish between
actual product descriptions and other text on a website, as well as to decide
whether a given text is or contains a product name.
  For this we use paragraph vectors for text classification, a character-level
long short-term memory network (LSTM) for a single word classification and an
LSTM tagger based on word embeddings for detecting product names within
sentences. Despite the need to collect bigger datasets in our specific problem
domain, the first results are promising and partially fit for production use.
Pre-training of Context-aware Item Representation for Next Basket Recommendation
Authors: Jingxuan Yang, Jun Xu, Jianzhuo Tong, Sheng Gao, Jun Guo, Ji-Rong Wen
Date: 2019-04-14 00:00:00
Tasks: Next-basket recommendation, Recommendation Systems

Next basket recommendation, which aims to predict the next a few items that a
user most probably purchases given his historical transactions, plays a vital
role in market basket analysis. From the viewpoint of item, an item could be
purchased by different users together with different items, for different
reasons. Therefore, an ideal recommender system should represent an item
considering its transaction contexts. Existing state-of-the-art deep learning
methods usually adopt the static item representations, which are invariant
among all of the transactions and thus cannot achieve the full potentials of
deep learning. Inspired by the pre-trained representations of BERT in natural
language processing, we propose to conduct context-aware item representation
for next basket recommendation, called Item Encoder Representations from
Transformers (IERT). In the offline phase, IERT pre-trains deep item
representations conditioning on their transaction contexts. In the online
recommendation phase, the pre-trained model is further fine-tuned with an
additional output layer. The output contextualized item embeddings are used to
capture users' sequential behaviors and general tastes to conduct
recommendation. Experimental results on the Ta-Feng data set show that IERT
outperforms the state-of-the-art baseline methods, which demonstrated the
effectiveness of IERT in next basket representation.
N2VSCDNNR: A Local Recommender System Based on Node2vec and Rich Information Network
Authors: Jinyin Chen, Yangyang Wu, Lu Fan, Xiang Lin, Haibin Zheng, Shanqing Yu, Qi Xuan
Date: 2019-04-12 00:00:00
Tasks: Clustering, Recommendation Systems

Recommender systems are becoming more and more important in our daily lives.
However, traditional recommendation methods are challenged by data sparsity and
efficiency, as the numbers of users, items, and interactions between the two in
many real-world applications increase fast. In this work, we propose a novel
clustering recommender system based on node2vec technology and rich information
network, namely N2VSCDNNR, to solve these challenges. In particular, we use a
bipartite network to construct the user-item network, and represent the
interactions among users (or items) by the corresponding one-mode projection
network. In order to alleviate the data sparsity problem, we enrich the network
structure according to user and item categories, and construct the one-mode
projection category network. Then, considering the data sparsity problem in the
network, we employ node2vec to capture the complex latent relationships among
users (or items) from the corresponding one-mode projection category network.
Moreover, considering the dependency on parameter settings and information loss
problem in clustering methods, we use a novel spectral clustering method, which
is based on dynamic nearest-neighbors (DNN) and a novel automatically
determining cluster number (ADCN) method that determines the cluster centers
based on the normal distribution method, to cluster the users and items
separately. After clustering, we propose the two-phase personalized
recommendation to realize the personalized recommendation of items for each
user. A series of experiments validate the outstanding performance of our
N2VSCDNNR over several advanced embedding and side information based
recommendation algorithms. Meanwhile, N2VSCDNNR seems to have lower time
complexity than the baseline methods in online recommendations, indicating its
potential to be widely applied in large-scale systems.
OpenKI: Integrating Open Information Extraction and Knowledge Bases with Relation Inference
Authors: Dongxu Zhang, Subhabrata Mukherjee, Colin Lockard, Xin Luna Dong, Andrew McCallum
Date: 2019-04-12 00:00:00
Proceeding: NAACL 2019 6
Tasks: Open Information Extraction, Relation

In this paper, we consider advancing web-scale knowledge extraction and
alignment by integrating OpenIE extractions in the form of (subject, predicate,
object) triples with Knowledge Bases (KB). Traditional techniques from
universal schema and from schema mapping fall in two extremes: either they
perform instance-level inference relying on embedding for (subject, object)
pairs, thus cannot handle pairs absent in any existing triples; or they perform
predicate-level mapping and completely ignore background evidence from
individual entities, thus cannot achieve satisfying quality. We propose OpenKI
to handle sparsity of OpenIE extractions by performing instance-level
inference: for each entity, we encode the rich information in its neighborhood
in both KB and OpenIE extractions, and leverage this information in relation
inference by exploring different methods of aggregation and attention. In order
to handle unseen entities, our model is designed without creating
entity-specific parameters. Extensive experiments show that this method not
only significantly improves state-of-the-art for conventional OpenIE
extractions like ReVerb, but also boosts the performance on OpenIE from
semi-structured data, where new entity pairs are abundant and data are fairly
sparse.
Towards Understanding and Detecting Fake Reviews in App Stores
Authors: Daniel Martens, Walid Maalej
Date: 2019-04-11 00:00:00

App stores include an increasing amount of user feedback in form of app
ratings and reviews. Research and recently also tool vendors have proposed
analytics and data mining solutions to leverage this feedback to developers and
analysts, e.g., for supporting release decisions. Research also showed that
positive feedback improves apps' downloads and sales figures and thus their
success. As a side effect, a market for fake, incentivized app reviews emerged
with yet unclear consequences for developers, app users, and app store
operators. This paper studies fake reviews, their providers, characteristics,
and how well they can be automatically detected. We conducted disguised
questionnaires with 43 fake review providers and studied their review policies
to understand their strategies and offers. By comparing 60,000 fake reviews
with 62 million reviews from the Apple App Store we found significant
differences, e.g., between the corresponding apps, reviewers, rating
distribution, and frequency. This inspired the development of a simple
classifier to automatically detect fake reviews in app stores. On a labelled
and imbalanced dataset including one-tenth of fake reviews, as reported in
other domains, our classifier achieved a recall of 91% and an AUC/ROC value of
98%. We discuss our findings and their impact on software engineering, app
users, and app store operators.
Using Machine Learning and Natural Language Processing to Review and Classify the Medical Literature on Cancer Susceptibility Genes
Authors: Yujia Bao, Zhengyi Deng, Yan Wang, Heeyoon Kim, Victor Diego Armengol, Francisco Acevedo, Nofal Ouardaoui, Cathy Wang, Giovanni Parmigiani, Regina Barzilay, Danielle Braun, Kevin S. Hughes
Date: 2019-04-24 00:00:00
Tasks: Classification, General Classification

PURPOSE: The medical literature relevant to germline genetics is growing
exponentially. Clinicians need tools monitoring and prioritizing the literature
to understand the clinical implications of the pathogenic genetic variants. We
developed and evaluated two machine learning models to classify abstracts as
relevant to the penetrance (risk of cancer for germline mutation carriers) or
prevalence of germline genetic mutations. METHODS: We conducted literature
searches in PubMed and retrieved paper titles and abstracts to create an
annotated dataset for training and evaluating the two machine learning
classification models. Our first model is a support vector machine (SVM) which
learns a linear decision rule based on the bag-of-ngrams representation of each
title and abstract. Our second model is a convolutional neural network (CNN)
which learns a complex nonlinear decision rule based on the raw title and
abstract. We evaluated the performance of the two models on the classification
of papers as relevant to penetrance or prevalence. RESULTS: For penetrance
classification, we annotated 3740 paper titles and abstracts and used 60% for
training the model, 20% for tuning the model, and 20% for evaluating the model.
The SVM model achieves 89.53% accuracy (percentage of papers that were
correctly classified) while the CNN model achieves 88.95 % accuracy. For
prevalence classification, we annotated 3753 paper titles and abstracts. The
SVM model achieves 89.14% accuracy while the CNN model achieves 89.13 %
accuracy. CONCLUSION: Our models achieve high accuracy in classifying abstracts
as relevant to penetrance or prevalence. By facilitating literature review,
this tool could help clinicians and researchers keep abreast of the burgeoning
knowledge of gene-cancer associations and keep the knowledge bases for clinical
decision support tools up to date.
Exploring the Daschle Collection using Text Mining
Authors: Damon Bayer, Semhar Michael
Date: 2019-04-23 00:00:00

A U.S. Senator from South Dakota donated documents that were accumulated
during his service as a house representative and senator to be housed at the
Bridges library at South Dakota State University. This project investigated the
utility of quantitative statistical methods to explore some portions of this
vast document collection. The available scanned documents and emails from
constituents are analyzed using natural language processing methods including
the Latent Dirichlet Allocation (LDA) model. This model identified major topics
being discussed in a given collection of documents. Important events and
popular issues from the Senator Daschles career are reflected in the changing
topics from the model. These quantitative statistical methods provide a summary
of the massive amount of text without requiring significant human effort or
time and can be applied to similar collections.