437 research outputs found
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
Making Neural Networks Interpretable with Attribution: Application to Implicit Signals Prediction
Explaining recommendations enables users to understand whether recommended
items are relevant to their needs and has been shown to increase their trust in
the system. More generally, if designing explainable machine learning models is
key to check the sanity and robustness of a decision process and improve their
efficiency, it however remains a challenge for complex architectures,
especially deep neural networks that are often deemed "black-box". In this
paper, we propose a novel formulation of interpretable deep neural networks for
the attribution task. Differently to popular post-hoc methods, our approach is
interpretable by design. Using masked weights, hidden features can be deeply
attributed, split into several input-restricted sub-networks and trained as a
boosted mixture of experts. Experimental results on synthetic data and
real-world recommendation tasks demonstrate that our method enables to build
models achieving close predictive performances to their non-interpretable
counterparts, while providing informative attribution interpretations.Comment: 14th ACM Conference on Recommender Systems (RecSys '20
Neural Collaborative Filtering Classification Model to Obtain Prediction Reliabilities
Neural collaborative filtering is the state of art field in the recommender systems area; it provides some models that obtain accurate predictions and recommendations. These models are regression-based, and they just
return rating predictions. This paper proposes the use of a classification-based approach, returning both rating predictions and their reliabilities. The extra information (prediction reliabilities) can be used in a variety of
relevant collaborative filtering areas such as detection of shilling attacks, recommendations explanation or navigational tools to show users and items dependences. Additionally, recommendation reliabilities can be
gracefully provided to users: âprobably you will like this filmâ, âalmost certainly you will like this songâ, etc. This paper provides the proposed neural architecture; it also tests that the quality of its recommendation results is as good as the state of art baselines. Remarkably, individual rating predictions are improved by using the proposed architecture compared to baselines. Experiments have been performed making use of four popular public datasets, showing generalizable quality results. Overall, the proposed architecture improves individual rating predictions quality, maintains recommendation results and opens the doors to a set of relevant collaborative filtering fields
Exploring Unconventional Sources in Big Data: A Data Lifecycle Approach for Social and Economic Analysis with Machine Learning
This study delves into the realm of leveraging unconventional sources within the domain of Big Data for conducting insightful social and economic analyses. Employing a Data Lifecycle Approach, the research focuses on harnessing the potential of linear regression, random forest, and XGBoost techniques to extract meaningful insights from unconventional data sources. The study encompasses a structured methodology involving data collection, preprocessing, feature engineering, model selection, and iterative refinement. By applying these techniques to diverse datasets, encompassing sources like social media content, sensor data, and satellite imagery, the study aims to provide a comprehensive understanding of social and economic trends. The results obtained through these methods contribute to an enhanced comprehension of the intricate relationships within societal and economic systems, further highlighting the importance of unconventional data sources in driving valuable insights for decision-makers and researchers alike
CASPR: Customer Activity Sequence-based Prediction and Representation
Tasks critical to enterprise profitability, such as customer churn
prediction, fraudulent account detection or customer lifetime value estimation,
are often tackled by models trained on features engineered from customer data
in tabular format. Application-specific feature engineering adds development,
operationalization and maintenance costs over time. Recent advances in
representation learning present an opportunity to simplify and generalize
feature engineering across applications. When applying these advancements to
tabular data researchers deal with data heterogeneity, variations in customer
engagement history or the sheer volume of enterprise datasets. In this paper,
we propose a novel approach to encode tabular data containing customer
transactions, purchase history and other interactions into a generic
representation of a customer's association with the business. We then evaluate
these embeddings as features to train multiple models spanning a variety of
applications. CASPR, Customer Activity Sequence-based Prediction and
Representation, applies Transformer architecture to encode activity sequences
to improve model performance and avoid bespoke feature engineering across
applications. Our experiments at scale validate CASPR for both small and large
enterprise applications.Comment: Presented at the Table Representation Learning Workshop, NeurIPS
2022, New Orleans. Authors listed in random orde
Review-based collaborative recommender system using deep learning methods
Recommender systems have been widely adopted to assist users in purchasing and increasing sales. Collaborative filtering techniques have been identified to be the most popular methods used for the recommendation system. One major drawback of these approaches is the data sparsity problem, which generally leads to low performances of the recommender systems. Recent development has shown that user review texts can be exploited to tackle the issue of data sparsity thereby improving the accuracy of the recommender systems. However, the problem with existing methods for the review-based recommender system is the use of handcrafted features which makes the system less accurate. Thus, to address the above issue, this study proposed collaborative recommender system models that utilize user textual reviews based on deep learning methods for improving predictive performances of recommender systems. To extract the product aspects to mine usersâ opinion, an aspect extraction method was first developed using a Multi-Channel Convolutional Neural Network. An aspect-based recommender system was then designed by integrating the opinions of users based on the product aspects into the collaborative filtering method for the recommendation process. To further improve the predictive performance, the fine-grained user-item interaction based on the aspect-based collaborative method was studied and a sentiment-aware recommender system was also designed using a deep learning method. Extensive series of experiments were conducted on real-world datasets from the Semeval-014, Amazon, and Yelp reviews to evaluate the performances of the proposed models from both the aspect extraction and rating prediction. Experimental results showed that the proposed aspect extraction model performed better than compared methods such as rule-based and the neural network-based approaches, with average gains of 5.2%, 12.0%, and 7.5% in terms of Precision, Recall, and F1 score, respectively. Meanwhile, the proposed aspect-based collaborative methods demonstrated better performances compared to benchmark approaches such as topic modelling techniques with an average improvement of 6.5% and 8.0% in terms of the Root Means Squared Error (RMSE) and Mean Absolute Error (MAE), respectively. Statistical T-test was conducted and the results showed that all the performance improvements were significant at P<0.05. This result indicates the effectiveness of utilizing the multi-channel convolutional neural network for better extraction accuracy. The findings also demonstrate the advantage of utilizing user textual reviews and the deep learning methods for improving the predictive accuracy in recommendation systems
A Review of Text Corpus-Based Tourism Big Data Mining
With the massive growth of the Internet, text data has become one of the main formats of tourism big data. As an effective expression means of touristsâ opinions, text mining of such data has big potential to inspire innovations for tourism practitioners. In the past decade, a variety of text mining techniques have been proposed and applied to tourism analysis to develop tourism value analysis models, build tourism recommendation systems, create tourist profiles, and make policies for supervising tourism markets. The successes of these techniques have been further boosted by the progress of natural language processing (NLP), machine learning, and deep learning. With the understanding of the complexity due to this diverse set of techniques and tourism text data sources, this work attempts to provide a detailed and up-to-date review of text mining techniques that have been, or have the potential to be, applied to modern tourism big data analysis. We summarize and discuss different text representation strategies, text-based NLP techniques for topic extraction, text classification, sentiment analysis, and text clustering in the context of tourism text mining, and their applications in tourist profiling, destination image analysis, market demand, etc. Our work also provides guidelines for constructing new tourism big data applications and outlines promising research areas in this field for incoming years
Salience and Market-aware Skill Extraction for Job Targeting
At LinkedIn, we want to create economic opportunity for everyone in the
global workforce. To make this happen, LinkedIn offers a reactive Job Search
system, and a proactive Jobs You May Be Interested In (JYMBII) system to match
the best candidates with their dream jobs. One of the most challenging tasks
for developing these systems is to properly extract important skill entities
from job postings and then target members with matched attributes. In this
work, we show that the commonly used text-based \emph{salience and
market-agnostic} skill extraction approach is sub-optimal because it only
considers skill mention and ignores the salient level of a skill and its market
dynamics, i.e., the market supply and demand influence on the importance of
skills. To address the above drawbacks, we present \model, our deployed
\emph{salience and market-aware} skill extraction system. The proposed \model
~shows promising results in improving the online performance of job
recommendation (JYMBII) ( job apply) and skill suggestions for job
posters ( suggestion rejection rate). Lastly, we present case studies to
show interesting insights that contrast traditional skill recognition method
and the proposed \model~from occupation, industry, country, and individual
skill levels. Based on the above promising results, we deployed the \model
~online to extract job targeting skills for all M job postings served at
LinkedIn.Comment: 9 pages, to appear in KDD202
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