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Hybrid search: incorporating contextual signals in recommendations at Pinterest

Published: 27 September 2018 Publication History

Abstract

Many modern recommender systems use collaborative filtering or historical engagement data to serve the best recommendations for each item. However, the context of each recommendation instance can be very different. Some users may be casually browsing, while others are searching with high intent. At Pinterest, we realized that building our system solely on aggregated historical data or pin-board collaborative filtering [1] would not be able to capture these differences. Incorporating contextual signals helps us serve better recommendations for every instance.
Pinterest Related Pins is an item-to-item recommender system that accounts for 40 percent of engagement on Pinterest. [2] On Pinterest, Related Pins appears as a feed of content relevant to the Pin a user has clicked on. Users arrive at Related Pins feeds from a variety of surfaces, such as their Home Feed, Search results, or Boards. As expected, these users often have different intents. Users coming from Search have already executed a specific text query and clicked on one of the Pins in the Search results. This context tells us that the user has high intent and is interested in something related to both the Search query as well as the clicked Pin. This context is very different from a user who is casually scrolling through their Home Feed and clicks on a Pin that happens to catch their eye. The Related Pins recommendations for each of these clicked Pins should therefore also differ accordingly.
Related Pins are generally relevant to the clicked Pin. The recommendations for a women's dress shoe Pin will be other shoes of similar style, some of which may be paired with matching outfits. However, if the user searched in particular for "red ballet flats with sequins," the Related Pins may not be specific enough to be useful to the user. In order to address this, we developed a hybrid search that takes both the text search query and the clicked pin image and metadata as inputs, and outputs a set of results tailored to both. We found that this improved user engagement for Related Pins from Search by 20% on top of the previous production recommendation system [2]. Following this exciting launch, we are planning to further incorporate contextual signals by adding them as features in our model.

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References

[1]
C. Eksombatchai et. al. Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time. In Proceedings of WWW, 2018.
[2]
D. C. Liu et. al. Related Pins at Pinterest: The Evolution of a Real-World Recommender System. In Proceedings of WWW, 2017.

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cover image ACM Conferences
RecSys '18: Proceedings of the 12th ACM Conference on Recommender Systems
September 2018
600 pages
ISBN:9781450359016
DOI:10.1145/3240323
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 September 2018

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Author Tags

  1. context
  2. pinterest
  3. recommender systems

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  • Invited-talk

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RecSys '18
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RecSys '18: Twelfth ACM Conference on Recommender Systems
October 2, 2018
British Columbia, Vancouver, Canada

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RecSys '18 Paper Acceptance Rate 32 of 181 submissions, 18%;
Overall Acceptance Rate 254 of 1,295 submissions, 20%

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