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Improving recommendation lists through topic diversification
TLDR
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. Expand
Being accurate is not enough: how accuracy metrics have hurt recommender systems
TLDR
This paper proposes informal arguments that the recommender community should move beyond the conventional accuracy metrics and their associated experimental methodologies, and proposes new user-centric directions for evaluating recommender systems. Expand
Getting to know you: learning new user preferences in recommender systems
TLDR
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. Expand
On the recommending of citations for research papers
TLDR
This paper investigated six algorithms for selecting citations, evaluating them through offline experiments and an online experiment to gauge user opinion of the effectiveness of the algorithms and of the utility of such recommendations for common research tasks. Expand
Enhancing digital libraries with TechLens
TLDR
This paper presents and experiments with hybrid recommender algorithms that combine collaborative filtering and content-based filtering to recommend research papers to users and shows that users value paper recommendations, that the hybrid algorithms can be successfully combined, and that these results can be applied to develop recommender systems for other types of digital libraries. Expand
Interfaces for Eliciting New User Preferences in Recommender Systems
TLDR
It is found that the two pure interfaces both produced accurate user models, but that directly asking users for items to rate increases user loyalty in the system. Expand
Don't look stupid: avoiding pitfalls when recommending research papers
TLDR
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. Expand
Making recommendations better: an analytic model for human-recommender interaction
TLDR
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. Expand
Meeting user information needs in recommender systems
TLDR
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. Expand
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