User perception of differences in recommender algorithms

@inproceedings{Ekstrand2014UserPO,
  title={User perception of differences in recommender algorithms},
  author={Michael D. Ekstrand and F. Maxwell Harper and Martijn C. Willemsen and Joseph A. Konstan},
  booktitle={ACM Conference on Recommender Systems},
  year={2014}
}
Recent developments in user evaluation of recommender systems have brought forth powerful new tools for understanding what makes recommendations effective and useful. We apply these methods to understand how users evaluate recommendation lists for the purpose of selecting an algorithm for finding movies. This paper reports on an experiment in which we asked users to compare lists produced by three common collaborative filtering algorithms on the dimensions of novelty, diversity, accuracy… 

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References

SHOWING 1-10 OF 30 REFERENCES

Evaluating Recommendation Systems

This paper discusses how to compare recommenders based on a set of properties that are relevant for the application, and focuses on comparative studies, where a few algorithms are compared using some evaluation metric, rather than absolute benchmarking of algorithms.

A user-centric evaluation framework for recommender systems

A unifying evaluation framework, called ResQue (Recommender systems' Quality of user experience), which aimed at measuring the qualities of the recommended items, the system's usability, usefulness, interface and interaction qualities, users' satisfaction with the systems, and the influence of these qualities on users' behavioral intentions.

Understanding choice overload in recommender systems

Investigation of the effect of recommendation set size and set quality on perceived variety, recommendation set attractiveness, choice difficulty and satisfaction with the chosen item shows that larger sets containing only good items do not necessarily result in higher choice satisfaction compared to smaller sets.

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.

Improving recommendation lists through topic diversification

This work presents topic diversification, a novel method designed to balance and diversify personalized recommendation lists in order to reflect the user's complete spectrum of interests, and introduces the intra-list similarity metric to assess the topical diversity of recommendation lists.

Solving the apparent diversity-accuracy dilemma of recommender systems

This paper introduces a new algorithm specifically to address the challenge of diversity and shows how it can be used to resolve this apparent dilemma when combined in an elegant hybrid with an accuracy-focused algorithm.

An Empirical Analysis of Design Choices in Neighborhood-Based Collaborative Filtering Algorithms

An analysis framework is applied that divides the neighborhood-based prediction approach into three components and then examines variants of the key parameters in each component, and identifies the three components identified are similarity computation, neighbor selection, and rating combination.

Don't look stupid: avoiding pitfalls when recommending research papers

This work performs a detailed user study with over 130 users to understand differences between recommender algorithms through an online survey of paper recommendations from the ACM Digital Library, and succinctly summarizes the most striking results as "Don't Look Stupid" in front of users.

Avoiding monotony: improving the diversity of recommendation lists

This work model the competing goals of maximizing the diversity of the retrieved list while maintaining adequate similarity to the user query as a binary optimization problem, leading to a parameterized eigenvalue problem whose solution is finally quantized to the required binary solution.

Making recommendations better: an analytic model for human-recommender interaction

It is argued that recommenders need a deeper understanding of users and their information seeking tasks and recommender algorithms using a common language and an analytic process model.