April 23, 2024

Bella Huang | Software program Engineer, Residence Candidate Technology; Raymond Hsu | Engineer Supervisor, Residence Candidate Technology; Dylan Wang | Engineer Supervisor, Residence Relevance

Graphic: Reward the new engagement to its query in offline workflow to Query Pins (repins, clicks, closeups) to Homefeed Recommendations to User (New Recommendations are generated from queries) to Future engagements (future repins, clicks, closeups) with Feedback Loop arrow in the center of the flow map.

In Homefeed, ~30% of really useful pins come from pin to pin-based retrieval. Which means that through the retrieval stage, we use a batch of question pins to name our retrieval system to generate pin suggestions. We sometimes use a consumer’s beforehand engaged pins, and a consumer could have tons of (or hundreds!) of engaged pins, so a key drawback for us is: how can we choose the suitable question pins from the consumer’s profile?

At Pinterest, we use PinnerSAGE as the principle supply of a consumer’s pin profile. PinnerSAGE generates clusters of the consumer’s engaged pins primarily based on the pin embedding by grouping close by pins collectively. Every cluster represents a sure use case of the consumer and permits for range by deciding on question pins from totally different clusters. We pattern the PinnerSAGE clusters because the supply of the queries.

Beforehand, we sampled the clusters primarily based on uncooked counts of actions within the cluster. Nonetheless, there are a number of drawbacks for this fundamental sampling method:

  • The question choice is comparatively static if no new engagements occur. The principle purpose is that we solely take into account the motion quantity after we pattern the clusters. Until the consumer takes a big variety of new actions, the sampling distribution stays roughly the identical.
  • No suggestions is used for the longer term question choice. Throughout every cluster sampling, we don’t take into account the downstream engagements from the final request’s sampling outcomes. A consumer could have had optimistic or unfavorable engagement on the earlier request, however don’t take that into consideration for his or her subsequent request.
  • It can’t differentiate between the identical motion sorts except for their timestamp. For instance, if the actions inside the identical cluster all occurred across the identical time, the burden of every motion would be the identical.
Graphic: Events arrow to Cluster Sampling (three clusters) arrow to Query Selection.
Determine 1. Earlier question choice movement
Events arrow to Cluster Sampling. Arrow above from Query Reward to Cluster Sampling (three clusters). Arrow from Cluster Sampling to Query Selection.
Determine 2. Present question choice movement with question reward

To deal with the shortcomings of the earlier method, we added a brand new element to the Question Choice layer known as Question Reward. Question Reward consists of a workflow that computes the engagement price of every question, which we retailer and retrieve to be used in future question choice. Subsequently, we will construct a suggestions loop to reward the queries with downstream engagement.

Right here’s an instance of how Question Reward works. Suppose a consumer has two PinnerSAGE clusters: one massive cluster associated to Recipes, and one small cluster associated to Furnishings. We initially present the consumer loads of recipe pins, however the consumer doesn’t have interaction with them. Question Reward can seize that the Recipes cluster has many impressions however no future engagement. Subsequently, the longer term reward, which is calculated by the engagement price of the cluster, will regularly drop and we may have a better probability to pick the small Furnishings cluster. If we present the consumer a number of Furnishings pins they usually have interaction with them, Question Reward will improve the probability that we choose the Furnishings cluster sooner or later. Subsequently, with the assistance of Question Reward, we’re capable of construct a suggestions loop primarily based on customers’ engagement charges and higher choose the question for candidate era.

Some clusters could not have any engagement (e.g. an empty Question Reward). This could possibly be as a result of:

  • The cluster was engaged a very long time in the past so it didn’t have an opportunity to be chosen lately
  • The cluster is a brand new use case for customers, so we don’t have a lot report within the reward

When clusters don’t have any engagement, we’ll give them a mean weight in order that there’ll nonetheless be an opportunity for them to be uncovered to the customers. After the subsequent run of the Question Reward workflow, we’ll get extra details about the unexposed clusters and determine whether or not we’ll choose them subsequent time.

Graphic: Reward the new engagement to its query in offline workflow to Query Pins (repins, clicks, closeups) to Homefeed Recommendations to User (New Recommendations are generated from queries) to Future engagements (future repins, clicks, closeups) with Feedback Loop arrow in the center of the flow map.
Determine 3. Constructing a suggestions loop primarily based on Question Reward
  • Pinterest, as a platform to convey inspirations, wish to give Pinners personalised suggestions as a lot as we will. Taking customers’ downstream suggestions like each optimistic and unfavorable engagements is what we wish to prioritize. Sooner or later iterations, we’ll take into account extra engagement sorts relatively than repin to construct a consumer profile.
  • As a way to maximize the Pinterest utilization effectivity, as a substitute of constructing the offline Question Reward, we wish to transfer to a realtime model to complement the sign for profiling amongst on-line requests. This might enable the suggestions loop to be extra responsive and immediate, doubtlessly responding to a consumer in the identical Homefeed session as they browse.
  • Apart from the pin primarily based retrieval, we will simply undertake the same methodology on any token-based retrieval methodology.

Due to our collaborators who contributed by way of discussions, evaluations, and options: Bowen Deng, Xinyuan Gui, Yitong Zhou, Neng Gu, Minzhe Zhou, Dafang He, Zhaohui Wu, Zhongxian Chen

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