Recommender systems: from algorithms to user experience

  title={Recommender systems: from algorithms to user experience},
  author={Joseph A. Konstan and John Riedl},
  journal={User Modeling and User-Adapted Interaction},
  • J. KonstanJ. Riedl
  • Published 1 April 2012
  • Computer Science
  • User Modeling and User-Adapted Interaction
Since their introduction in the early 1990’s, automated recommender systems have revolutionized the marketing and delivery of commerce and content by providing personalized recommendations and predictions over a variety of large and complex product offerings. In this article, we review the key advances in collaborative filtering recommender systems, focusing on the evolution from research concentrated purely on algorithms to research concentrated on the rich set of questions around the user… 

Interacting with Recommenders—Overview and Research Directions

This work provides a comprehensive overview on the existing literature on user interaction aspects in recommender systems, covering existing approaches for preference elicitation and result presentation, as well as proposals that consider recommendation as an interactive process.

User Experience and Recommender Systems

Despite the recent attempts on UX evaluation of RS, this area is still new and needs further investigations, so definition of UX especially in the field of RS is discussed.

Ranking and Context-awareness in Recommender Systems

This thesis focuses on improvement of two critical aspects of CF, namely ranking and context-awareness of the recommendations, and analyzes new developments in the field of collaborative recommendation.

Principles, techniques and evaluation of recommendation systems

The different characteristics and potentials of two different prediction techniques which include Collaborative Filtering and Content-based Filtering in recommendation systems are explored in order to serve as a compass for research and practice in the field of recommendation systems.

Collaborative Filtering beyond the User-Item Matrix

A comprehensive introduction to a large body of research, more than 200 key references, is provided, with the aim of supporting the further development of recommender systems exploiting information beyond the U-I matrix.

Recommending Based on Implicit Feedback

This chapter categorizes different types of implicit feedback and review their use in the context of recommender systems and Social Information Access applications, and extends the categorization scheme to be suitable to recent application domains.

Measuring the Business Value of Recommender Systems

A review of existing publications on field tests of recommender systems and which business-related performance measures were used in such real-world deployments indicates that various open questions remain regarding the realistic quantification of the business effects of recommenders and the performance assessment of recommendation algorithms in academia.

Explaining the user experience of recommender systems

This paper proposes a framework that takes a user-centric approach to recommender system evaluation that links objective system aspects to objective user behavior through a series of perceptual and evaluative constructs (called subjective system aspects and experience, respectively).

On Unexpectedness in Recommender Systems

A method to improve user satisfaction by generating unexpected recommendations based on the utility theory of economics is proposed, which outperforms baseline methods in terms of unexpectedness and other important metrics, such as coverage, aggregate diversity and dispersion, while avoiding any accuracy loss.



Recommender Systems - An Introduction

An overview of approaches to developing state-of-the-art recommender systems, including current algorithmic approaches for generating personalized buying proposals, such as collaborative and content-based filtering, as well as more interactive and knowledge-based approaches.

Evaluating recommender systems from the user’s perspective: survey of the state of the art

This paper surveys the state of the art of user experience research in RS by examining how researchers have evaluated design methods that augment RS’s ability to help users find the information or product that they truly prefer, interact with ease with the system, and form trust with RS through system transparency, control and privacy preserving mechanisms.

Explaining the user experience of recommender systems

This paper proposes a framework that takes a user-centric approach to recommender system evaluation that links objective system aspects to objective user behavior through a series of perceptual and evaluative constructs (called subjective system aspects and experience, respectively).

Hybrid Recommender Systems: Survey and Experiments

  • R. Burke
  • Computer Science
    User Modeling and User-Adapted Interaction
  • 2004
This paper surveys the landscape of actual and possible hybrid recommenders, and introduces a novel hybrid, EntreeC, a system that combines knowledge-based recommendation and collaborative filtering to recommend restaurants, and shows that semantic ratings obtained from the knowledge- based part of the system enhance the effectiveness of collaborative filtering.

Beyond Algorithms: An HCI Perspective on Recommender Systems

From a user’s perspective, an effective recommender system inspires trust in the system; has system logic that is at least somewhat transparent; points users towards new, not-yet-experienced items; provides details about recommended items, including pictures and community ratings; and finally, provides ways to refine recommendations by including or excluding particular genres.

Getting to know you: learning new user preferences in recommender systems

Six techniques that collaborative filtering recommender systems can use to learn about new users are studied, showing that the choice of learning technique significantly affects the user experience, in both the user effort and the accuracy of the resulting predictions.

Item-based collaborative filtering recommendation algorithms

This paper analyzes item-based collaborative ltering techniques and suggests that item- based algorithms provide dramatically better performance than user-based algorithms, while at the same time providing better quality than the best available userbased algorithms.

Shilling recommender systems for fun and profit

Four open questions are explored that may affect the effectiveness of shilling attacks on recommender systems: which recommender algorithm is being used, whether the application is producing recommendations or predictions, how detectable the attacks are by the operator of the system, and what the properties are of the items being attacked.

Critiquing-based recommenders: survey and emerging trends

  • Li ChenP. Pu
  • Computer Science
    User Modeling and User-Adapted Interaction
  • 2011
A hybrid framework is developed to unify the advantages of different methods and overcome their respective limitations, indicating how hybrid critiquing supports could effectively enable end-users to achieve more confident decisions.

Meeting user information needs in recommender systems

This dissertation explores how to tailor recommendation lists not just to a user, but to the user's current information seeking task, and proposes a new set of recommender metrics, which can bridge users and their needs with recommender algorithms to generate more useful recommendation lists.