NDLI: Discrete Content-aware Matrix Factorization

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Foreword to the Applied Data Science: Invited Talks Track at KDD-2017

Benchmarks and Process Management in Data Science: Will We Ever Get Over the Mess?

Learning Certifiably Optimal Rule Lists

Effective Evaluation Using Logged Bandit Feedback from Multiple Loggers

Using Convolutional Networks and Satellite Imagery to Identify Patterns in Urban Environments at a Large Scale

A Practical Algorithm for Solving the Incoherence Problem of Topic Models In Industrial Applications

The Future of Data Integration

More than the Sum of its Parts: Building Domino Data Lab

The Future of Artificially Intelligent Assistants

Improved Degree Bounds and Full Spectrum Power Laws in Preferential Attachment Networks

Tripoles: A New Class of Relationships in Time Series Data

Luck is Hard to Beat: The Difficulty of Sports Prediction

Machine Learning for Encrypted Malware Traffic Classification: Accounting for Noisy Labels and Non-Stationarity

Three Principles of Data Science: Predictability, Stability and Computability

Mining Big Data in NeuroGenetics to Understand Muscular Dystrophy

Unsupervised Network Discovery for Brain Imaging Data

Post Processing Recommender Systems for Diversity

Planning Bike Lanes based on Sharing-Bikes' Trajectories

Extremely Fast Decision Tree Mining for Evolving Data Streams

Industrial Machine Learning

Patient Subtyping via Time-Aware LSTM Networks

Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews

TFX: A TensorFlow-Based Production-Scale Machine Learning Platform

Real-Time Optimization of Web Publisher RTB Revenues

Behavior Informatics to Discover Behavior Insight for Active and Tailored Client Management

Robust Top-k Multiclass SVM for Visual Category Recognition

Bolt: Accelerated Data Mining with Fast Vector Compression

LiJAR: A System for Job Application Redistribution towards Efficient Career Marketplace

Customer Lifetime Value Prediction Using Embeddings

It Takes More than Math and Engineering to Hit the Bullseye with Data

KATE: K-Competitive Autoencoder for Text

Robust Spectral Clustering for Noisy Data: Modeling Sparse Corruptions Improves Latent Embeddings

A Data Science Approach to Understanding Residential Water Contamination in Flint

TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks

Planning and Learning under Uncertainty: Theory and Practice

A Minimal Variance Estimator for the Cardinality of Big Data Set Intersection

DeepMood: Modeling Mobile Phone Typing Dynamics for Mood Detection

Estimation of Recent Ancestral Origins of Individuals on a Large Scale

Learning Tree-Structured Detection Cascades for Heterogeneous Networks of Embedded Devices

Big Data in Climate: Opportunities and Challenges for Machine Learning

HyperLogLog Hyperextended: Sketches for Concave Sublinear Frequency Statistics

Fast Newton Hard Thresholding Pursuit for Sparsity Constrained Nonconvex Optimization

A Dirty Dozen: Twelve Common Metric Interpretation Pitfalls in Online Controlled Experiments

AESOP: Automatic Policy Learning for Predicting and Mitigating Network Service Impairments

Addressing Challenges with Big Data for Media Measurement

Fast Enumeration of Large k-Plexes

On Sampling Strategies for Neural Network-based Collaborative Filtering

A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations

Automated Categorization of Onion Sites for Analyzing the Darkweb Ecosystem

Machine Learning Software in Practice: Quo Vadis?

Matrix Profile V: A Generic Technique to Incorporate Domain Knowledge into Motif Discovery

Unsupervised Feature Selection in Signed Social Networks

FIRST: Fast Interactive Attributed Subgraph Matching

Toward Automated Fact-Checking: Detecting Check-worthy Factual Claims by ClaimBuster

Designing AI at Scale to Power Everyday Life

metapath2vec: Scalable Representation Learning for Heterogeneous Networks

GRAM: Graph-based Attention Model for Healthcare Representation Learning

Prognosis and Diagnosis of Parkinson's Disease Using Multi-Task Learning

An Efficient Bandit Algorithm for Realtime Multivariate Optimization

Spaceborne Data Enters the Mainstream

Ego-Splitting Framework: from Non-Overlapping to Overlapping Clusters

Algorithmic Decision Making and the Cost of Fairness

A Data Mining Framework for Valuing Large Portfolios of Variable Annuities

Large Scale Sentiment Learning with Limited Labels

Contextual Motifs: Increasing the Utility of Motifs using Contextual Data

Structural Diversity and Homophily: A Study Across More Than One Hundred Big Networks

GELL: Automatic Extraction of Epidemiological Line Lists from Open Sources

Optimization Beyond Prediction: Prescriptive Price Optimization

Unsupervised P2P Rental Recommendations via Integer Programming

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Unsupervised Discovery of Drug Side-Effects from Heterogeneous Data Sources

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Discrete Content-aware Matrix Factorization

Content Provider	ACM Digital Library
Author	Lian, Defu Ge, Yong Xie, Xing Liu, Rui Cao, Longbing Zheng, Kai
Abstract	Precisely recommending relevant items from massive candidates to a large number of users is an indispensable yet computationally expensive task in many online platforms (e.g., Amazon.com and Netflix.com). A promising way is to project users and items into a Hamming space and then recommend items via Hamming distance. However, previous studies didn't address the cold-start challenges and couldn't make the best use of preference data like implicit feedback. To fill this gap, we propose a Discrete Content-aware Matrix Factorization (DCMF) model, 1) to derive compact yet informative binary codes at the presence of user/item content information; 2) to support the classification task based on a local upper bound of logit loss; 3) to introduce an interaction regularization for dealing with the sparsity issue. We further develop an efficient discrete optimization algorithm for parameter learning. Based on extensive experiments on three real-world datasets, we show that DCFM outperforms the state-of-the-arts on both regression and classification tasks.
Starting Page	325
Ending Page	334
Page Count	10
File Format	PDF
ISBN	9781450348874
DOI	10.1145/3097983.3098008
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-08-13
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Collaborative filtering Content-based filtering Discrete hashing Recommendation
Content Type	Text
Resource Type	Article

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