NDLI: Fewer Flops at the Top: Accuracy, Diversity, and Regularization in Two-Class Collaborative Filtering

Please wait, while we are loading the content...

Recommender Systems and the New New Economics of Information

Learning to Rank with Trust and Distrust in Recommender Systems

Educational Question Routing in Online Student Communities

Secure Multi-Party Protocols for Item-Based Collaborative Filtering

Recommending Personalized News in Short User Sessions

Sequential User-based Recurrent Neural Network Recommendations

Expediting Exploration by Attribute-to-Feature Mapping for Cold-Start Recommendations

Fewer Flops at the Top: Accuracy, Diversity, and Regularization in Two-Class Collaborative Filtering

Understanding How People Use Natural Language to Ask for Recommendations

Recommending Product Sizes to Customers

Getting Deep Recommenders Fit: Bloom Embeddings for Sparse Binary Input/Output Networks

A Semantic-Aware Profile Updating Model for Text Recommendation

Rethinking Collaborative Filtering: A Practical Perspective on State-of-the-art Research Based on Real World Insights

Déjà Vu: The Importance of Time and Causality in Recommender Systems

Online Learning to Rank for Recommender Systems

CheckInShop.eu: A Sensor-based Recommender System for micro-location Marketing

The 1st Workshop on Intelligent Recommender Systems by Knowledge Transfer & Learning: (RecSysKTL)

New Paths in Music Recommender Systems Research

Recommending a Sequence of Points of Interest to a Group of Users in a Mobile Context

Improving Higher Education: Learning Analytics & Recommender Systems Research

Metalearning for Context-aware Filtering: Selection of Tensor Factorization Algorithms

The Magic Barrier Revisited: Accessing Natural Limitations of Recommender Assessment

Modeling the Assimilation-Contrast Effects in Online Product Rating Systems: Debiasing and Recommendations

Personalizing Session-based Recommendations with Hierarchical Recurrent Neural Networks

Translation-based Recommendation

Additive Co-Clustering with Social Influence for Recommendation

Geographical Diversification in POI Recommendation: Toward Improved Coverage on Interested Areas

Defining and Supporting Narrative-driven Recommendation

Practical Lessons from Developing a Large-Scale Recommender System at Zalando

TransNets: Learning to Transform for Recommendation

A Multi-criteria Recommender System Exploiting Aspect-based Sentiment Analysis of Users' Reviews

Recommendation Applications and Systems at Electronic Arts

Building Recommender Systems for Fashion: Industry Talk Abstract

Bandit Algorithms for e-Commerce Recommender Systems: Extended Abstract

A Research Tool for User Preferences Elicitation with Facial Expressions

The 1st International Workshop on Temporal Reasoning in Recommender Systems

Privacy for Recommender Systems: Tutorial Abstract

Deep Cross-Domain Fashion Recommendation

Personalization is a Two-Way Street

A Gradient-based Adaptive Learning Framework for Efficient Personal Recommendation

Effective User Interface Designs to Increase Energy-efficient Behavior in a Rasch-based Energy Recommender System

Fairness-Aware Group Recommendation with Pareto-Efficiency

3D Convolutional Networks for Session-based Recommendation with Content Features

MPR: Multi-Objective Pairwise Ranking

Folding: Why Good Models Sometimes Make Spurious Recommendations

Exploiting Socio-Economic Models for Lodging Recommendation in the Sharing Economy

Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction

Exploring the Semantic Gap for Movie Recommendations

Search Ranking And Personalization at Airbnb

Boosting Recommender Systems with Deep Learning

You are what apps you use: Transfer Learning for Personalized Content and Ad Recommendation

Data-Driven Repricing Strategies in Competitive Markets: An Interactive Simulation Platform

DLRS 2017: Second Workshop on Deep Learning for Recommender Systems

Deep Learning for Recommender Systems

Online Recommender System for Personalized Nutrition Advice

Memory Networks for Recommendation

entity2rec: Learning User-Item Relatedness from Knowledge Graphs for Top-N Item Recommendation

Evaluating Decision-Aware Recommender Systems

A Novel Recommender System for Helping Marathoners to Achieve a New Personal-Best

Modeling User Session and Intent with an Attention-based Encoder-Decoder Architecture

An Elementary View on Factorization Machines

Chemical Reactant Recommendation Using a Network of Organic Chemistry

Surveying User Reactions to Recommendations Based on Inferences Made by Face Detection Technology

When Recurrent Neural Networks meet the Neighborhood for Session-Based Recommendation

Dynamic Scholarly Collaborator Recommendation via Competitive Multi-Agent Reinforcement Learning

Bootstrapping a Destination Recommender System

Personalization challenges in e-Learning

Pokedem: an Automatic Social Media Management Application

RecSys Challenge 2017: Offline and Online Evaluation

Product Recommendations Enhanced with Reviews

Improving Similarity Measures Using Ontological Data

On Parallelizing SGD for Pairwise Learning to Rank in Collaborative Filtering Recommender Systems

Using Explainability for Constrained Matrix Factorization

An Insurance Recommendation System Using Bayesian Networks

Recommendation of High Quality Representative Reviews in e-commerce

Personalized Job Recommendation System at LinkedIn: Practical Challenges and Lessons Learned

Citolytics: A Link-based Recommender System for Wikipedia

Second Workshop on Health Recommender Systems: (HealthRecSys 2017)

Tutorial on Open Source Online Learning Recommenders

Improving the Trustworthiness of Recommendations in Collaborative Filtering under the Belief Function Framework

Controlling Popularity Bias in Learning-to-Rank Recommendation

User Preferences for Hybrid Explanations

Visual Analysis of Recommendation Performance

KidRec: Children & Recommender Systems: Workshop Co-located with ACM Conference on Recommender Systems (RecSys 2017)

Unsupervised Context-Driven Recommendations Based On User Reviews

PathRec: Visual Analysis of Travel Route Recommendations

VAMS 2017: Workshop on Value-Aware and Multistakeholder Recommendation

The Exploration-Exploitation Trade-off in Interactive Recommender Systems

Workshop on Recommendation in Complex Scenarios: (ComplexRec 2017)

User Preferences Analysis Using Visual Stimuli

FATREC Workshop on Responsible Recommendation

Constructive Recommendation

RecSys'17 Joint Workshop on Interfaces and Human Decision Making for Recommender Systems

RecTour 2017: Workshop on Recommenders in Tourism

CitRec 2017: International Workshop on Recommender Systems for Citizens

LSRS'17: Workshop on Large-Scale Recommender Systems

Fewer Flops at the Top: Accuracy, Diversity, and Regularization in Two-Class Collaborative Filtering

Content Provider	ACM Digital Library
Author	Paudel, Bibek Haas, Thilo Bernstein, Abraham
Abstract	In most existing recommender systems, implicit or explicit interactions are treated as positive links and all unknown interactions are treated as negative links. The goal is to suggest new links that will be perceived as positive by users. However, as signed social networks and newer content services become common, it is important to distinguish between positive and negative preferences. Even in existing applications, the cost of a negative recommendation could be high when people are looking for new jobs, friends, or places to live. In this work, we develop novel probabilistic latent factor models to recommend positive links and compare them with existing methods on five different openly available datasets. Our models are able to produce better ranking lists and are effective in the task of ranking positive links at the top, with fewer negative links (flops). Moreover, we find that modeling signed social networks and user preferences this way has the advantage of increasing the diversity of recommendations. We also investigate the effect of regularization on the quality of recommendations, a matter that has not received enough attention in the literature. We find that regularization parameter heavily affects the quality of recommendations in terms of both accuracy and diversity.
Starting Page	215
Ending Page	223
Page Count	9
File Format	PDF
ISBN	9781450346528
DOI	10.1145/3109859.3109916
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-08-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Collaborative filtering Positive and negative recommendations Matrix factorization Diverse recommendations
Content Type	Text
Resource Type	Article

Central Library (ISO-9001:2015 Certified)
Indian Institute of Technology Kharagpur
Kharagpur, West Bengal, India | PIN - 721302

See location in the Map
03222 282435
Mail: support@ndl.gov.in

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in