Despite advances in deep learning methods for song recommendation, most existing methods do not take advantage of the sequential nature of song content. In addition, there is a lack of methods that can explain their predictions using the content of recommended songs and only a few approaches can handle the item cold start problem. In this work, we propose a hybrid deep learning model that uses collaborative filtering (CF) and deep learning sequence models on the Musical Instrument Digital Interface (MIDI) content of songs to provide accurate recommendations, while also being able to generate a relevant, personalized explanation for each recommended song. Compared to state-of-the-art methods, our validation experiments showed that in addition to generating explainable recommendations, our model stood out among the top performers in terms of recommendation accuracy and the ability to handle the item cold start problem. Moreover, validation shows that our personalized explanations capture properties that are in accordance with the user’s preferences.
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Adaptive Navigational Support and Explainable Recommendations in a Personalized Programming Practice System
We present the results of a study where we provided students with textual explanations for learning content recommendations along with adaptive navigational support, in the context of a personalized system for practicing Java programming. We evaluated how varying the modality of access (no access vs. on-mouseover vs. on-click) can influence how students interact with the learning platform and work with both recommended and non-recommended content. We found that the persistence of students when solving recommended coding problems is correlated with their learning gain and that specific student-engagement metrics can be supported by the design of adequate navigational support and access to recommendations' explanations.
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- NSF-PAR ID:
- 10482915
- Publisher / Repository:
- ACM
- Date Published:
- Journal Name:
- Proceedings of the 34th ACM Conference on Hypertext and Social Media
- ISBN:
- 9798400702327
- Page Range / eLocation ID:
- 1 to 9
- Subject(s) / Keyword(s):
- explanations, navigation support, e-learning, recommender system, open learner model
- Format(s):
- Medium: X
- Location:
- Rome Italy
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3603163.3609054
