Automatic text processing
A critical investigation of recall and precision as measures of retrieval system performance
ACM Transactions on Information Systems (TOIS)
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Fab: content-based, collaborative recommendation
Communications of the ACM
Enabling custom enhancements in digital sports broadcasts
Proceedings of the sixth international conference on 3D Web technology
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Information Retrieval
Novelty and redundancy detection in adaptive filtering
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
User response to two algorithms as a test of collaborative filtering
CHI '01 Extended Abstracts on Human Factors in Computing Systems
Persuasive Technology: Using Computers to Change What We Think and Do
Persuasive Technology: Using Computers to Change What We Think and Do
Improving User Modelling with Content-Based Techniques
UM '01 Proceedings of the 8th International Conference on User Modeling 2001
Implicit feedback for inferring user preference: a bibliography
ACM SIGIR Forum
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Ontological user profiling in recommender systems
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Supply Chain Management on Demand
Supply Chain Management on Demand
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
Trust building with explanation interfaces
Proceedings of the 11th international conference on Intelligent user interfaces
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Interaction, usability and aesthetics: what influences users' preferences?
DIS '06 Proceedings of the 6th conference on Designing Interactive systems
The Long Tail: Why the Future of Business Is Selling Less of More
The Long Tail: Why the Future of Business Is Selling Less of More
A hybrid approach for improving predictive accuracy of collaborative filtering algorithms
User Modeling and User-Adapted Interaction
User Modeling and User-Adapted Interaction
Persuasion in Recommender Systems
International Journal of Electronic Commerce
Explanations of recommendations
Proceedings of the 2007 ACM conference on Recommender systems
Wikify!: linking documents to encyclopedic knowledge
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Improving Recommendation Novelty Based on Topic Taxonomy
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Framing the user experience: information biases on website quality judgement
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Evaluating product search and recommender systems for E-commerce environments
Electronic Commerce Research
Factorization meets the neighborhood: a multifaceted collaborative filtering model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Persuasion in Knowledge-Based Recommendation
PERSUASIVE '08 Proceedings of the 3rd international conference on Persuasive Technology
Interactive Television - A Brief Media History
EUROITV '08 Proceedings of the 6th European conference on Changing Television Environments
Interactive Narratives: Exploring the Links between Empathy, Interactivity and Structure
EUROITV '08 Proceedings of the 6th European conference on Changing Television Environments
Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
A cross-cultural user evaluation of product recommender interfaces
Proceedings of the 2008 ACM conference on Recommender systems
A new approach to evaluating novel recommendations
Proceedings of the 2008 ACM conference on Recommender systems
The effects of transparency on trust in and acceptance of a content-based art recommender
User Modeling and User-Adapted Interaction
An Evaluation Methodology for Collaborative Recommender Systems
AXMEDIS '08 Proceedings of the 2008 International Conference on Automated solutions for Cross Media Content and Multi-channel Distribution
Learning preferences of new users in recommender systems: an information theoretic approach
ACM SIGKDD Explorations Newsletter
Collaborative filtering with temporal dynamics
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Scalable Collaborative Filtering Approaches for Large Recommender Systems
The Journal of Machine Learning Research
Acceptance issues of personality-based recommender systems
Proceedings of the third ACM conference on Recommender systems
Critiquing recommenders for public taste products
Proceedings of the third ACM conference on Recommender systems
Enhancing diversity in Top-N recommendation
Proceedings of the third ACM conference on Recommender systems
Factor in the neighbors: Scalable and accurate collaborative filtering
ACM Transactions on Knowledge Discovery from Data (TKDD)
Evaluating the Diversity of Top-N Recommendations
ICTAI '09 Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence
From hits to niches?: or how popular artists can bias music recommendation and discovery
Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition
Interfaces for eliciting new user preferences in recommender systems
UM'03 Proceedings of the 9th international conference on User modeling
Persuasive recommendation: serial position effects in knowledge-based recommender systems
PERSUASIVE'07 Proceedings of the 2nd international conference on Persuasive technology
Metrics for evaluating the serendipity of recommendation lists
JSAI'07 Proceedings of the 2007 conference on New frontiers in artificial intelligence
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Understanding choice overload in recommender systems
Proceedings of the fourth ACM conference on Recommender systems
Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space
On bootstrapping recommender systems
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Adaptive bootstrapping of recommender systems using decision trees
Proceedings of the fourth ACM international conference on Web search and data mining
Recommender Systems Handbook
Comparative evaluation of recommender system quality
CHI '11 Extended Abstracts on Human Factors in Computing Systems
Looking for "good" recommendations: a comparative evaluation of recommender systems
INTERACT'11 Proceedings of the 13th IFIP TC 13 international conference on Human-computer interaction - Volume Part III
Precision-oriented evaluation of recommender systems: an algorithmic comparison
Proceedings of the fifth ACM conference on Recommender systems
A personalized TV guide system compliant with MHP
IEEE Transactions on Consumer Electronics
User profiling vs. accuracy in recommender system user experience
Proceedings of the International Working Conference on Advanced Visual Interfaces
User effort vs. accuracy in rating-based elicitation
Proceedings of the sixth ACM conference on Recommender systems
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Recommender Systems (RSs) help users search large amounts of digital contents and services by allowing them to identify the items that are likely to be more attractive or useful. RSs play an important persuasion role, as they can potentially augment the users’ trust towards in an application and orient their decisions or actions towards specific directions. This article explores the persuasiveness of RSs, presenting two vast empirical studies that address a number of research questions. First, we investigate if a design property of RSs, defined by the statistically measured quality of algorithms, is a reliable predictor of their potential for persuasion. This factor is measured in terms of perceived quality, defined by the overall satisfaction, as well as by how users judge the accuracy and novelty of recommendations. For our purposes, we designed an empirical study involving 210 subjects and implemented seven full-sized versions of a commercial RS, each one using the same interface and dataset (a subset of Netflix), but each with a different recommender algorithm. In each experimental configuration we computed the statistical quality (recall and F-measures) and collected data regarding the quality perceived by 30 users. The results show us that algorithmic attributes are less crucial than we might expect in determining the user’s perception of an RS’s quality, and suggest that the user’s judgment and attitude towards a recommender are likely to be more affected by factors related to the user experience. Second, we explore the persuasiveness of RSs in the context of large interactive TV services. We report a study aimed at assessing whether measurable persuasion effects (e.g., changes of shopping behavior) can be achieved through the introduction of a recommender. Our data, collected for more than one year, allow us to conclude that, (1) the adoption of an RS can affect both the lift factor and the conversion rate, determining an increased volume of sales and influencing the user’s decision to actually buy one of the recommended products, (2) the introduction of an RS tends to diversify purchases and orient users towards less obvious choices (the long tail), and (3) the perceived novelty of recommendations is likely to be more influential than their perceived accuracy. Overall, the results of these studies improve our understanding of the persuasion phenomena induced by RSs, and have implications that can be of interest to academic scholars, designers, and adopters of this class of systems.