Recommending Products When Consumers Learn Their Preference Weights
Consumers often learn the weights they ascribe to product attributes (“preference weights”) as they search. For example, after test driving cars, a consumer might find that he or she undervalued trunk space and overvalued sunroofs. Preference-weight learning makes optimal search complex because each...
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| Format: | Article |
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Institute for Operations Research and the Management Sciences (INFORMS)
2020
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| Online Access: | https://hdl.handle.net/1721.1/124909 |
| _version_ | 1826195900719955968 |
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| author | Dzyabura, Daria Hauser, John R. |
| author2 | Sloan School of Management |
| author_facet | Sloan School of Management Dzyabura, Daria Hauser, John R. |
| author_sort | Dzyabura, Daria |
| collection | MIT |
| description | Consumers often learn the weights they ascribe to product attributes (“preference weights”) as they search. For example, after test driving cars, a consumer might find that he or she undervalued trunk space and overvalued sunroofs. Preference-weight learning makes optimal search complex because each time a product is searched, updated preference weights affect the expected utility of all products and the value of subsequent optimal search. Product recommendations, which take preference-weight learning into account, help consumers search. We motivate a model in which consumers learn (update) their preference weights. When consumers learn preference weights, it may not be optimal to recommend the product with the highest option value, as in most search models, or the product most likely to be chosen, as in traditional recommendation systems. Recommendations are improved if consumers are encouraged to search products with diverse attribute levels, products that are undervalued, or products for which recommendation-system priors differ from consumers’ priors. Synthetic data experiments demonstrate that proposed recommendation systems outperform benchmark recommendation systems, especially when consumers are novices and when recommendation systems have good priors. We demonstrate empirically that consumers learn preference weights during search, that recommendation systems can predict changes, and that a proposed recommendation system encourages learning. |
| first_indexed | 2024-09-23T10:17:37Z |
| format | Article |
| id | mit-1721.1/124909 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T10:17:37Z |
| publishDate | 2020 |
| publisher | Institute for Operations Research and the Management Sciences (INFORMS) |
| record_format | dspace |
| spelling | mit-1721.1/1249092022-09-30T20:10:11Z Recommending Products When Consumers Learn Their Preference Weights Dzyabura, Daria Hauser, John R. Sloan School of Management Consumers often learn the weights they ascribe to product attributes (“preference weights”) as they search. For example, after test driving cars, a consumer might find that he or she undervalued trunk space and overvalued sunroofs. Preference-weight learning makes optimal search complex because each time a product is searched, updated preference weights affect the expected utility of all products and the value of subsequent optimal search. Product recommendations, which take preference-weight learning into account, help consumers search. We motivate a model in which consumers learn (update) their preference weights. When consumers learn preference weights, it may not be optimal to recommend the product with the highest option value, as in most search models, or the product most likely to be chosen, as in traditional recommendation systems. Recommendations are improved if consumers are encouraged to search products with diverse attribute levels, products that are undervalued, or products for which recommendation-system priors differ from consumers’ priors. Synthetic data experiments demonstrate that proposed recommendation systems outperform benchmark recommendation systems, especially when consumers are novices and when recommendation systems have good priors. We demonstrate empirically that consumers learn preference weights during search, that recommendation systems can predict changes, and that a proposed recommendation system encourages learning. 2020-04-28T19:08:51Z 2020-04-28T19:08:51Z 2019-05 2016-03 Article http://purl.org/eprint/type/JournalArticle 0732-2399 1526-548X https://hdl.handle.net/1721.1/124909 Dzyabura, Daria and John R. Hauser. "Recommending Products When Consumers Learn Their Preference Weights." Marketing Science 38, 3 (May 2019): 365-541 © 2019 INFORMS http://dx.doi.org/10.1287/mksc.2018.1144 Marketing 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) Prof. Hauser |
| spellingShingle | Dzyabura, Daria Hauser, John R. Recommending Products When Consumers Learn Their Preference Weights |
| title | Recommending Products When Consumers Learn Their Preference Weights |
| title_full | Recommending Products When Consumers Learn Their Preference Weights |
| title_fullStr | Recommending Products When Consumers Learn Their Preference Weights |
| title_full_unstemmed | Recommending Products When Consumers Learn Their Preference Weights |
| title_short | Recommending Products When Consumers Learn Their Preference Weights |
| title_sort | recommending products when consumers learn their preference weights |
| url | https://hdl.handle.net/1721.1/124909 |
| work_keys_str_mv | AT dzyaburadaria recommendingproductswhenconsumerslearntheirpreferenceweights AT hauserjohnr recommendingproductswhenconsumerslearntheirpreferenceweights |