Explaining collaborative filtering recommendations

Herlocker, Jonathan L.; Konstan, Joseph A.; Riedl, John

doi:10.1145/358916.358995

Cited by 1,311 publications

(837 citation statements)

References 11 publications

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“…Another weakness is that RSs are mainly conceived and perceived as black boxes [6]: the user receives the recommendations but doesn't know how they were generated and has no control in the recommendation process. For example, in [7], the authors conducted a survey with real users and found that users want to see how recommendations are generated, how their neighbours are computed and how their neighbours rate items. Swearingen and Sinha [6] analyzed RSs from a Human Computer Interaction perspective and found that RSs are effective if, among other things, "the system logic is at least somewhat transparent".…”

Section: Motivationsmentioning

confidence: 99%

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Trust Metrics in Recommender Systems

Massa

Avesani²

2009

Human–Computer Interaction Series

110

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Section: Motivationsmentioning

confidence: 99%

“…We reported in Section 2 how traditional RS are often seen by users as black boxes [7,6] and thought hard to understand and control [8,13]. RSs are considered more effective by users if, among other things, "the system logic is at least somewhat transparent" [6].…”

Section: How Trust Alleviates Rs Weaknessesmentioning

confidence: 99%

Trust Metrics in Recommender Systems

Massa

Avesani²

2009

Human–Computer Interaction Series

110

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“…ReMashed takes the preferences into account to offer tailored recommendation to the learner. ReMashed uses collaborative filtering [11] to generate recommendations. It works by matching together users with similar opinions about learning resources.…”

Section: The Remashed Systemmentioning

confidence: 99%

“…ReMashed identifies the cold-start situation of the recommender system [11] and recommends resources based on tags of the Web2.0 sources of the current learner. It computes the similarity between the tag cloud of the current learner with other learners and learning resources.…”

Section: The Remashed Systemmentioning

confidence: 99%

ReMashed – Recommendations for Mash-Up Personal Learning Environments

Drachsler

Pecceu

Arts

et al. 2009

Lecture Notes in Computer Science

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Abstract.The following article presents a Mash-Up Personal Learning Environment called ReMashed that recommends learning resources from emerging information of a Learning Network. In ReMashed learners can specify certain Web2.0 services and combine them in a Mash-Up Personal Learning Environment. Learners can rate information from an emerging amount of Web2.0 information of a Learning Network and train a recommender system for their particular needs. ReMashed therefore has three main objectives: 1. to provide a recommender system for Mash-up Personal Learning Environments to learners, 2. to offer an environment for testing new recommendation approaches and methods for researchers, and 3. to create informal user-generated content data sets that are needed to evaluate new recommendation algorithms for learners in informal Learning Networks.

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“…CBF approaches and CF algorithms have both been used fairly successfully to build recommendation systems in various domains [11][12][13]16]. However, as described above, they suffer from the cold-start problem in one form or another.…”

Section: Related Workmentioning

confidence: 99%

A Content-Boosted Collaborative Filtering Algorithm for Personalized Training in Interpretation of Radiological Imaging

2014

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Devising a method that can select cases based on the performance levels of trainees and the characteristics of cases is essential for developing a personalized training program in radiology education. In this paper, we propose a novel hybrid prediction algorithm called content-boosted collaborative filtering (CBCF) to predict the difficulty level of each case for each trainee. The CBCF utilizes a content-based filtering (CBF) method to enhance existing trainee-case ratings data and then provides final predictions through a collaborative filtering (CF) algorithm. The CBCF algorithm incorporates the advantages of both CBF and CF, while not inheriting the disadvantages of either. The CBCF method is compared with the pure CBF and pure CF approaches using three datasets. The experimental data are then evaluated in terms of the MAE metric. Our experimental results show that the CBCF outperforms the pure CBF and CF methods by 13.33 and 12.17 %, respectively, in terms of prediction precision. This also suggests that the CBCF can be used in the development of personalized training systems in radiology education.

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Explaining collaborative filtering recommendations

Cited by 1,311 publications

References 11 publications

Trust Metrics in Recommender Systems

Trust Metrics in Recommender Systems

ReMashed – Recommendations for Mash-Up Personal Learning Environments

A Content-Boosted Collaborative Filtering Algorithm for Personalized Training in Interpretation of Radiological Imaging

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