TasteWeights: a visual interactive hybrid recommender system

By Svetlin Bostandjiev, John O'Donovan, Tobias Höllerer. ACM Conference on Recommender Systems, 2012

327 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/8da89a2ec51db1cb11fd8805046d445a1a701a6d

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In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Information Systems

Citations in the corpus

How the Experts Do It: Assessing and Explaining Agent Behaviors in Real-Time Strategy Games (2017, background): “Bostandjiev et al.’s work on a music recommendation system [2] found that explanation led to a remarkable increase in user-satisfaction with their system.”
Artificial intelligence in recommender systems (2020, background): “Interactive and non-interactive strategies are compared in [209], illustrating how a visual interface can improve user satisfaction by providing explanatory notes.”
Visual Interaction with Deep Learning Models through Collaborative Semantic Inference (2019, background): “These explanations have been shown to improve model personalization [11] and explainability [13,49].”
Principles of Explanatory Debugging to Personalize Interactive Machine Learning (2015, background): “Increased transparency has been associated with many beneﬁts, including increased user acceptance of (or satisfaction with) recommendations and predictions [4, 8, 42], improved training of intelligent agents [41], and increased trust in the system’s predictions [10].”
Evaluating Recommender Systems with User Experiments (2015, background): “For example, the original TasteWeights system [14] allows you to inspect connections between liked items, friends, and recommendations, and control the weights of both liked items and friends.”
Explore, exploit, and explain: personalizing explainable recommendations with bandits (2018, unknown): “Further, various visualization techniques for explaining recommendations have been proposed, including interfaces with concentric circles [11] and pathways between columns [2].”
A systematic review and taxonomy of explanations in decision support and recommender systems (2017, unknown): “f our analysis, can be found online at: http://inf.ufrgs.br/prosoft/resources/2017/umuai-sr-explanations. A Systematic Review and Taxonomy of Explanations 9 Table 6 Selected Primary Studies. Approach [166,34,56,206,2,124,54,146,198,192,129,131,86,138,82,136,24,97,4,171,249,84,231,94,101, 143,252,149”
Interactive recommender systems: A survey of the state of the art and future research challenges and opportunities (2016, background): “In addition, the systems help users to learn more about the underlying data such as similar friends (Gretarsson et al., 2010; Bostandjiev et al., 2012) and relations of recommendations (Parra et al.”
Computing Recommendations via a Knowledge Graph-aware Autoencoder (2018, background): “Furthermore, the exploitation of Linked Open Data helps to deal with limited content analysis and cold-start problems: new relevant features can be introduced to improve item representations [4, 38] or to cope with the increasing data sparsity [27, 43].”