Learning Preferences with Side Information
Product and content personalization is now ubiquitous in e-commerce. There are typically not enough available transactional data for this task. As such, companies today seek to use a variety of information on the interactions between a product and a customer to drive personalization decisions. We fo...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Institute for Operations Research and the Management Sciences (INFORMS)
2021
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| Online Access: | https://hdl.handle.net/1721.1/136395 |
| _version_ | 1826205765055021056 |
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| author | Farias, Vivek F Li, Andrew A |
| author2 | Sloan School of Management |
| author_facet | Sloan School of Management Farias, Vivek F Li, Andrew A |
| author_sort | Farias, Vivek F |
| collection | MIT |
| description | Product and content personalization is now ubiquitous in e-commerce. There are typically not enough available transactional data for this task. As such, companies today seek to use a variety of information on the interactions between a product and a customer to drive personalization decisions. We formalize this problem as one of recovering a large-scale matrix with side information in the form of additional matrices of conforming dimension. Viewing the matrix we seek to recover and the side information we have as slices of a tensor, we consider the problem of slice recovery, which is to recover specific slices of “simple” tensors from noisy observations of the entire tensor. We propose a definition of simplicity that on the one hand elegantly generalizes a standard generative model for our motivating problem and on the other hand subsumes low-rank tensors for a variety of existing definitions of tensor rank. We provide an efficient algorithm for slice recovery that is practical for massive data sets and provides a significant performance improvement over state-of-the-art incumbent approaches to tensor recovery. Furthermore, we establish near-optimal recovery guarantees that, in an important regime, represent an order improvement over the best available results for this problem. Experiments on data from a music streaming service demonstrate the performance and scalability of our algorithm. |
| first_indexed | 2024-09-23T13:18:59Z |
| format | Article |
| id | mit-1721.1/136395 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:18:59Z |
| publishDate | 2021 |
| publisher | Institute for Operations Research and the Management Sciences (INFORMS) |
| record_format | dspace |
| spelling | mit-1721.1/1363952023-12-19T21:07:06Z Learning Preferences with Side Information Farias, Vivek F Li, Andrew A Sloan School of Management Product and content personalization is now ubiquitous in e-commerce. There are typically not enough available transactional data for this task. As such, companies today seek to use a variety of information on the interactions between a product and a customer to drive personalization decisions. We formalize this problem as one of recovering a large-scale matrix with side information in the form of additional matrices of conforming dimension. Viewing the matrix we seek to recover and the side information we have as slices of a tensor, we consider the problem of slice recovery, which is to recover specific slices of “simple” tensors from noisy observations of the entire tensor. We propose a definition of simplicity that on the one hand elegantly generalizes a standard generative model for our motivating problem and on the other hand subsumes low-rank tensors for a variety of existing definitions of tensor rank. We provide an efficient algorithm for slice recovery that is practical for massive data sets and provides a significant performance improvement over state-of-the-art incumbent approaches to tensor recovery. Furthermore, we establish near-optimal recovery guarantees that, in an important regime, represent an order improvement over the best available results for this problem. Experiments on data from a music streaming service demonstrate the performance and scalability of our algorithm. 2021-10-27T20:35:10Z 2021-10-27T20:35:10Z 2019 2021-04-15T15:23:36Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/136395 en 10.1287/MNSC.2018.3092 Management Science Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute for Operations Research and the Management Sciences (INFORMS) MIT web domain |
| spellingShingle | Farias, Vivek F Li, Andrew A Learning Preferences with Side Information |
| title | Learning Preferences with Side Information |
| title_full | Learning Preferences with Side Information |
| title_fullStr | Learning Preferences with Side Information |
| title_full_unstemmed | Learning Preferences with Side Information |
| title_short | Learning Preferences with Side Information |
| title_sort | learning preferences with side information |
| url | https://hdl.handle.net/1721.1/136395 |
| work_keys_str_mv | AT fariasvivekf learningpreferenceswithsideinformation AT liandrewa learningpreferenceswithsideinformation |