GENERATING NEGATIVE SAMPLES FOR SEQUENTIAL RECOMMENDATION
Embodiments described herein provide methods and systems for training a sequential recommendation model. A system receives a plurality of user behavior sequences, and encodes those sequences into a plurality of user interest representations. The system predicts a next item using a sequential recommendation model, producing a probability distribution over a set of items. The next interacted item in a sequence is selected as a positive sample, and a negative sample is selected based on the generated probability distribution. The positive and negative samples are used to compute a contrastive loss and update the sequential recommendation model.
1 . A method for training a sequential recommendation model via contrastive learning, comprising:
receiving, via a communication interface, a training dataset of user behavior sequences;
encoding, by an encoder of the sequential recommendation model, a first sequence of user behaviors up to a first time instance into a first user interest representation;
generating, by a decoder based on the first user interest representation, a first plurality of probabilities corresponding to a plurality of items being sequentially recommended as a next item following the first sequence of user behavior;
sampling a negative sample from the plurality of items according to the first plurality of probabilities;
selecting a positive sample corresponding to a next interacted item at a next time instance following the first time instance from the training dataset of user behavior sequences;
inputting the sampled negative sample, and the selected positive sample to the sequential recommendation model;
computing a contrastive loss in response to the inputting; and
updating the sequential recommendation model based on the contrastive loss.
2 . The method of claim 1 , wherein the generating comprises:
computing a distance in a feature space between the user interest representation and representations of the plurality of items.
3 . The method of claim 1 , wherein the computing the contrastive loss comprises:
computing a first distance in a feature space between a representation of the sampled negative sample and the first user interest representation;
computing a second distance in feature space between a representation of the selected positive sample and the first user interest representation; and
computing the contrastive loss based at least in part on the first distance and the second distance.
4 . The method of claim 1 , wherein a first particular item of the plurality of items associated with a higher probability of the first plurality of probabilities than a second particular item of the plurality of items, has a higher probability of being sampled.
5 . The method of claim 1 , wherein the sampling the negative sample is constrained from sampling the next interacted item from the first sequence of user behavior.
6 . The method of claim 1 , wherein the updating the sequential recommendation model comprises updating the encoder based on the contrastive loss.
7 . The method of claim 1 , wherein the sampling the negative sample comprises:
scaling the first plurality of probabilities based on a scaling parameter; and
sampling the negative sample according to scaled probabilities.
8 . The method of claim 1 , wherein the sampling the negative sample further comprises:
controlling a quantity of items in a subset of the plurality of items according to an adjustable parameter; and
sampling the negative sample from the subset of the plurality of items,
wherein the adjustable parameter is a pre-defined constant throughout a training stage of the sequential recommendation model, or
gradually increased throughout the training stage of the sequential recommendation model.
9 . The method of claim 8 , further comprising:
sampling a plurality of negative samples per one next item prediction at one training time step.
10 . The method of claim 1 , further comprising:
after updating the sequential recommendation model based on the contrastive loss:
re-using the first sequence of user behaviors for training the updated sequential recommendation model at a next training timestep.
11 . The method of claim 1 , further comprising:
after updating the sequential recommendation model based on the contrastive loss:
including the next interacted item into the first sequence of user behaviors resulting in a second sequence of user behaviors;
encoding the second sequence of user behaviors into a second user interest representation;
generating a second plurality of probabilities corresponding to the plurality of items being sequentially recommended as a next item following the second sequence of user behavior;
sampling another negative sample from the plurality of items according to the second plurality of probabilities; and
using the other negative sample for contrastive learning with the updated sequential recommendation model.
12 . A system for sequential recommendation, the system comprising:
a memory that stores a sequential recommendation model;
a communication interface that receives a plurality of user behavior sequences; and
one or more hardware processors that:
receives, via a communication interface, a training dataset of user behavior sequences;
encodes, by an encoder of the sequential recommendation model, a first sequence of user behaviors up to a first time instance into a first user interest representation;
generates, by a decoder based on the first user interest representation, a first plurality of probabilities corresponding to a plurality of items being sequentially recommended as a next item following the first sequence of user behavior;
samples a negative sample from the plurality of items according to the first plurality of probabilities;
selects a positive sample corresponding to a next interacted item at a next time instance following the first time instance from the training dataset of user behavior sequences;
inputs the sampled negative sample, and the selected positive sample to the sequential recommendation model;
computes a contrastive loss in response to the inputting; and
updates the sequential recommendation model based on the contrastive loss.
13 . The system of claim 12 , wherein the generating comprises:
computing a distance in a feature space between the user interest representation and representations of the plurality of items.
14 . The system of claim 12 , wherein the computing the contrastive loss comprises:
computing a first distance in a feature space between a representation of the sampled negative sample and the first user interest representation;
computing a second distance in feature space between a representation of the selected positive sample and the first user interest representation; and
computing the contrastive loss based at least in part on the first distance and the second distance.
15 . The system of claim 12 , wherein a first particular item of the plurality of items associated with a higher probability of the first plurality of probabilities than a second particular item of the plurality of items, has a higher probability of being sampled.
16 . The system of claim 12 , wherein the sampling the negative sample is constrained from sampling the next interacted item from the first sequence of user behavior.
17 . The system of claim 12 , wherein the updating the sequential recommendation model comprises updating the encoder based on the contrastive loss.
18 . A processor-readable non-transitory storage medium storing a plurality of processor-executable instructions for a sequential recommendation model, the instructions being executed by a processor to perform operations comprising:
receiving, via a communication interface, a training dataset of user behavior sequences;
encoding, by an encoder of the sequential recommendation model, a first sequence of user behaviors up to a first time instance into a first user interest representation;
generating, by a decoder based on the first user interest representation, a first plurality of probabilities corresponding to a plurality of items being sequentially recommended as a next item following the first sequence of user behavior;
sampling a negative sample from the plurality of items according to the first plurality of probabilities;
selecting a positive sample corresponding to a next interacted item at a next time instance following the first time instance from the training dataset of user behavior sequences;
inputting the sampled negative sample, and the selected positive sample to the sequential recommendation model;
computing a contrastive loss in response to the inputting; and
updating the sequential recommendation model based on the contrastive loss.
19 . The processor-readable non-transitory storage medium of claim 18 , wherein the generating comprises:
computing a distance in a feature space between the user interest representation and representations of the plurality of items.
20 . The processor-readable non-transitory storage medium of claim 18 , wherein the computing the contrastive loss comprises:
computing a first distance in a feature space between a representation of the sampled negative sample and the first user interest representation;
computing a second distance in feature space between a representation of the selected positive sample and the first user interest representation; and
computing the contrastive loss based at least in part on the first distance and the second distance.