Top-N Recommendation with Missing Implicit Feedback

By Daryl Lim, Julian McAuley, Gert R. G. Lanckriet. 2015

51 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Tobias Schnabel
2. Thorsten Joachims
3. Adith Swaminathan
4. Himanshu Jain
5. Yashoteja Prabhu
6. Manik Varma
7. Ashudeep Singh
8. Xiaoran Xu

In Information Systems

1. Thorsten Joachims
2. Adith Swaminathan
3. Tobias Schnabel
4. Manik Varma
5. Yashoteja Prabhu
6. Himanshu Jain
7. Xiaoran Xu
8. Hanning Zhou

In Management Science and Operations Research

1. Thorsten Joachims
2. Tobias Schnabel
3. Adith Swaminathan
4. Manik Varma
5. Himanshu Jain
6. Hanning Zhou
7. Ashudeep Singh
8. Navin Chandak

Citations in the corpus

• Recommendations as Treatments: Debiasing Learning and Evaluation (2016, methodology): “Alternative approaches to learning with MNAR data (Steck, 2010; Lim et al., 2015) aim to avoid the problem by considering performance measures less affected by selection bias.”
• Distilling Structured Knowledge into Embeddings for Explainable and Accurate Recommendation (2019, methodology): “From a more technical perspective, our proposed approach can also be seen as a label propagation method to address missing data problem in recommender systems [19, 21].”
• Extreme Multi-label Loss Functions for Recommendation, Tagging, Ranking & Other Missing Label Applications (2016, background): “Previous attempts at developing unbiased loss functions in these areas have been limited to square loss [16], recall [37] and average discounted gain [25] under the restrictive assumption that ground truth labels go missing uniformly at random.”