NDLI: Rating: how difficult is it?

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Recommender systems at the long tail

Music recommendation and discovery revisited

Generalizing matrix factorization through flexible regression priors

Personalized PageRank vectors for tag recommendations: inside FolkRank

Multi-criteria service recommendation based on user criteria preferences

Rank and relevance in novelty and diversity metrics for recommender systems

Each to his own: how different users call for different interaction methods in recommender systems

Yahoo! music recommendations: modeling music ratings with temporal dynamics and item taxonomy

Social link recommendation by learning hidden topics

Recommending music for places of interest in a mobile travel guide

LensKit: a modular recommender framework

Using canonical correlation analysis for generalized sentiment analysis, product recommendation and search

3rd workshop on context-aware recommender systems (CARS 2011)

Robustness of recommender systems

Modeling item selection and relevance for accurate recommendations: a bayesian approach

A generalized stochastic block model for recommendation in social rating networks

The effect of context-aware recommendations on customer purchasing behavior and trust

OrdRec: an ordinal model for predicting personalized item rating distributions

Rating: how difficult is it?

CrimeWalker: a recommendation model for suspect investigation

Enhancing collaborative filtering systems with personality information

Adaptive social similarities for recommender systems

myMicSound: an online sound-based microphone recommendation system

Design guidelines for mobile group recommender systems to handle inaccurate or missing location data

WOMRAD: 2nd workshop on music recommendation and discovery

Recommendations as a conversation with the user

Shared collaborative filtering

Product recommendation and rating prediction based on multi-modal social networks

Random walk based entity ranking on graph for multidimensional recommendation

Item popularity and recommendation accuracy

A user-centric evaluation framework for recommender systems

Personalized activity streams: sifting through the "river of news"

A market-based approach to address the new item problem

Content-boosted matrix factorization for recommender systems: experiments with recipe recommendation

Recommenders benchmark framework

Interface and interaction design for group and social recommender systems

3rd workshop on recommender systems and the social web

Wisdom of the better few: cold start recommendation via representative based rating elicitation

Distributed rating prediction in user generated content streams

Group recommendation using feature space representing behavioral tendency and power balance among members

Rethinking the recommender research ecosystem: reproducibility, openness, and LensKit

My head is your tail: applying link analysis on long-tailed music listening behavior for music recommendation

Interactive multi-party critiquing for group recommendation

Intelligent web usage clustering based recommender system

Challenge on context-aware movie recommendation: CAMRa2011

Multi-value probabilistic matrix factorization for IP-TV recommendations

A model for proactivity in mobile, context-aware recommender systems

Predicting performance in recommender systems

Second workshop on information heterogeneity and fusion in recommender systems (HetRec2011)

A probabilistic definition of item similarity

Effective event discovery: using location and social information for scoping event recommendations

Anchoring effects of recommender systems

RecSys'11 workshop on human decision making in recommender systems

The "top N" news recommender: count distortion and manipulation resistance

Collaborative filtering with collective training

Recsys'11 workshop outline PeMA 2011: personalization in mobile applications

Factorization vs. regularization: fusing heterogeneous social relationships in top-n recommendation

Using Wikipedia to boost collaborative filtering techniques

Workshop on novelty and diversity in recommender systems - DiveRS 2011

Semi-SAD: applying semi-supervised learning to shilling attack detection

UCERSTI 2: second workshop on user-centric evaluation of recommender systems and their interfaces

Leveraging the linkedin social network data for extracting content-based user profiles

Applications of the conjugate gradient method for implicit feedback collaborative filtering

Matrix factorization techniques for context aware recommendation

MyMediaLite: a free recommender system library

Towards a more realistic evaluation: testing the ability to predict future tastes of matrix factorization-based recommenders

Collaborative temporal order modeling

LOGO: a long-short user interest integration in personalized news recommendation

A pragmatic procedure to support the user-centric evaluation of recommender systems

Machine learned job recommendation

Utilizing related products for post-purchase recommendation in e-commerce

Precision-oriented evaluation of recommender systems: an algorithmic comparison

Power to the people: exploring neighbourhood formations in social recommender system

Stochastic matching and collaborative filtering to recommend people to people

Recommendations in social media for brand monitoring

Rating: how difficult is it?

Content Provider	ACM Digital Library
Author	Sparling, E. Isaac Sen, Shilad
Abstract	Netflix.com uses star ratings, Digg.com uses up/down votes and Facebook uses a "like" but not a "dislike" button. Despite the popularity and diversity of these rating scales, research offers little guidance for designers choosing between them. This paper compares four different rating scales: unary ("like it"), binary (thumbs up / thumbs down), five-star, and a 100-point slider. Our analysis draws upon 12,847 movie and product review ratings collected from 348 users through an online survey. We a) measure the time and cognitive load required by each scale, b) study how rating time varies with the rating value assigned by a user, and c) survey users' satisfaction with each scale. Overall, users work harder with more granular rating scales, but these effects are moderated by item domain (product reviews or movies). Given a particular scale, users rating times vary significantly for items they like and dislike. Our findings about users' rating effort and satisfaction suggest guidelines for designers choosing between rating scales.
Starting Page	149
Ending Page	156
Page Count	8
File Format	PDF
ISBN	9781450306836
DOI	10.1145/2043932.2043961
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-10-23
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Rating scales User studies Recommender systems
Content Type	Text
Resource Type	Article

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