NDLI: Human-Recommender Systems: From Benchmark Data to Benchmark Cognitive Models

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Automated Machine Learning in the Wild

Recommendations with a Purpose

Field-aware Factorization Machines for CTR Prediction

Joint User Modeling across Aligned Heterogeneous Sites

HCI for Recommender Systems: the Past, the Present and the Future

Mechanism Design for Personalized Recommender Systems

Domain-Aware Grade Prediction and Top-n Course Recommendation

Recommender Systems with Personality

Meta-Prod2Vec: Product Embeddings Using Side-Information for Recommendation

The Contextual Turn: from Context-Aware to Context-Driven Recommender Systems

Recommending New Items to Ephemeral Groups Using Contextual User Influence

Personalized Recommendations using Knowledge Graphs: A Probabilistic Logic Programming Approach

Mendeley: Recommendations for Researchers

Recommending for the World

Leveraging a Graph-Powered, Real-Time Recommendation Engine to Create Rapid Business Value

A Recommender System to tackle Enterprise Collaboration

4th Workshop on Emotions and Personality in Personalized Systems (EMPIRE)

Group Recommender Systems

Context-Based IDE Command Recommender System

Opening Remarks

Personalization for Google Now: User Understanding and Application to Information Recommendation and Exploration

Recommender Systems for Self-Actualization

Learning Hierarchical Feature Influence for Recommendation by Recursive Regularization

Fifty Shades of Ratings: How to Benefit from a Negative Feedback in Top-N Recommendations Tasks

Human-Recommender Systems: From Benchmark Data to Benchmark Cognitive Models

Mood-Sensitive Truth Discovery For Reliable Recommendation Systems in Social Sensing

Deep Neural Networks for YouTube Recommendations

Past, Present, and Future of Recommender Systems: An Industry Perspective

Convolutional Matrix Factorization for Document Context-Aware Recommendation

Discovering What You're Known For: A Contextual Poisson Factorization Approach

Guided Walk: A Scalable Recommendation Algorithm for Complex Heterogeneous Social Networks

Efficient Bayesian Methods for Graph-based Recommendation

When Recommendation Systems Go Bad

The Exploit-Explore Dilemma in Music Recommendation

Hypothesis Testing: How to Eliminate Ideas as Soon as Possible

Conversational Recommendation System with Unsupervised Learning

Engendering Health with Recommender Systems

Matrix and Tensor Decomposition in Recommender Systems

Generating Pseudotransactions for Improving Sparse Matrix Factorization

A Coverage-Based Approach to Recommendation Diversity On Similarity Graph

Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence

Latent Factor Representations for Cold-Start Video Recommendation

Gaze Prediction for Recommender Systems

Crowd-Based Personalized Natural Language Explanations for Recommendations

Optimizing Similar Item Recommendations in a Semi-structured Marketplace to Maximize Conversion

Algorithms Aside: Recommendation As The Lens Of Life

Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations

TAPER: A Contextual Tensor-Based Approach for Personalized Expert Recommendation

STAR: Semiring Trust Inference for Trust-Aware Social Recommenders

Using Navigation to Improve Recommendations in Real-Time

News Recommendations at scale at Bloomberg Media: Challenges and Approaches

Feature Selection For Human Recommenders

Recommending the World's Knowledge: Application of Recommender Systems at Quora

Powering Content Discovery through Scalable, Realtime Profiling of Users' Content Preferences

RecProfile '16: Workshop on Profiling User Preferences for Dynamic, Online, and Real-Time recommendations

People Recommendation Tutorial

Gray Sheep, Influential Users, User Modeling and Recommender System Adoption by Startups

A Scalable Approach for Periodical Personalized Recommendations

Local Item-Item Models For Top-N Recommendation

Ask the GRU: Multi-task Learning for Deep Text Recommendations

Exploring the Value of Personality in Predicting Rating Behaviors: A Study of Category Preferences on MovieLens

A Package Recommendation Framework for Trip Planning Activities

Behaviorism is Not Enough: Better Recommendations through Listening to Users

Are You Influenced by Others When Rating?: Improve Rating Prediction by Conformity Modeling

Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation

Bayesian Low-Rank Determinantal Point Processes

Marsbot: Building a Personal Assistant

Considering Supplier Relations and Monetization in Designing Recommendation Systems

Multi-corpus Personalized Recommendations on Google Play

RecExp: A Semantic Recommender System with Explanation Based on Heterogeneous Information Network

RecSys'16 Joint Workshop on Interfaces and Human Decision Making for Recommender Systems

Tutorial: Lessons Learned from Building Real-life Recommender Systems

Increasing the Trustworthiness of Recommendations by Exploiting Social Media Sources

Multi-Word Generative Query Recommendation Using Topic Modeling

Asynchronous Distributed Matrix Factorization with Similar User and Item Based Regularization

Addressing Cold Start for Next-song Recommendation

Pairwise Preferences Based Matrix Factorization and Nearest Neighbor Recommendation Techniques

Modelling Contextual Information in Session-Aware Recommender Systems with Neural Networks

Representation Learning for Homophilic Preferences

Recommending Repeat Purchases using Product Segment Statistics

Music Personalization at Spotify

A Cross-Industry Machine Learning Framework with Explicit Representations

Item-to-item Recommendations at Pinterest

Topical Semantic Recommendations for Auteur Films

RecSys'16 Workshop on Deep Learning for Recommender Systems (DLRS)

Mining Information for the Cold-Item Problem

Contrasting Offline and Online Results when Evaluating Recommendation Algorithms

Query-based Music Recommendations via Preference Embedding

Accuracy and Diversity in Cross-domain Recommendations for Cold-start Users with Positive-only Feedback

Observing Group Decision Making Processes

Getting the Timing Right: Leveraging Category Inter-purchase Times to Improve Recommender Systems

Bayesian Personalized Ranking with Multi-Channel User Feedback

T-RecS: A Framework for a Temporal Semantic Analysis of the ACM Recommender Systems Conference

RecTour 2016: Workshop on Recommenders in Tourism

Personalized Support for Healthy Nutrition Decisions

Adaptive, Personalized Diversity for Visual Discovery

ExpLOD: A Framework for Explaining Recommendations based on the Linked Open Data Cloud

MAPS: A Multi Aspect Personalized POI Recommender System

Third Workshop on New Trends in Content-based Recommender Systems (CBRecSys 2016)

Proactive Recommendation Delivery

Intent-Aware Diversification Using a Constrained PLSA

The Value of Online Customer Reviews

LSRS'16: Workshop on Large-Scale Recommender Systems

Recommender Systems from an Industrial and Ethical Perspective

3rd Workshop on Recommendation Systems for Television and Online Video (RecSysTV 2016)

RecSys Challenge 2016: Job Recommendations

Human-Recommender Systems: From Benchmark Data to Benchmark Cognitive Models

Content Provider	ACM Digital Library
Author	Shafto, Patrick Nasraoui, Olfa
Abstract	We bring to the fore of the recommender system research community, an inconvenient truth about the current state of understanding how recommender system algorithms and humans influence one another, both computationally and cognitively. Unlike the great variety of supervised machine learning algorithms which traditionally rely on expert input labels and are typically used for decision making by an expert, recommender systems specifically rely on data input from non-expert or casual users and are meant to be used directly by these same non-expert users on an every day basis. Furthermore, the advances in online machine learning, data generation, and predictive model learning have become increasingly interdependent, such that each one feeds on the other in an iterative cycle. Research in psychology suggests that people's choices are (1) contextually dependent, and (2) dependent on interaction history. Thus, while standard methods of training and assessing performance of recommender systems rely on benchmark datasets, we suggest that a critical step in the evolution of recommender systems is the development of benchmark models of human behavior that capture contextual and dynamic aspects of human behavior. It is important to emphasize that even extensive real life user-tests may not be sufficient to make up for this gap in benchmarking validity because user tests are typically done with either a focus on user satisfaction or engagement (clicks, sales, likes, etc) with whatever the recommender algorithm suggests to the user, and thus ignore the human cognitive aspect. We conclude by highlighting the interdisciplinary implications of this endeavor.
Starting Page	127
Ending Page	130
Page Count	4
File Format	PDF MP4
ISBN	9781450340359
DOI	10.1145/2959100.2959188
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2016-09-07
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Machine learning Recommender systems Human learning
Content Type	Audio Text
Resource Type	Article

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