43 research outputs found
Deep recommender engine based on efficient product embeddings neural pipeline
Predictive analytics systems are currently one of the most important areas of
research and development within the Artificial Intelligence domain and
particularly in Machine Learning. One of the "holy grails" of predictive
analytics is the research and development of the "perfect" recommendation
system. In our paper, we propose an advanced pipeline model for the multi-task
objective of determining product complementarity, similarity and sales
prediction using deep neural models applied to big-data sequential transaction
systems. Our highly parallelized hybrid model pipeline consists of both
unsupervised and supervised models, used for the objectives of generating
semantic product embeddings and predicting sales, respectively. Our
experimentation and benchmarking processes have been done using pharma industry
retail real-life transactional Big-Data streams.Comment: 2018 17th RoEduNet Conference: Networking in Education and Research
(RoEduNet
Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks
Recommendations can greatly benefit from good representations of the user
state at recommendation time. Recent approaches that leverage Recurrent Neural
Networks (RNNs) for session-based recommendations have shown that Deep Learning
models can provide useful user representations for recommendation. However,
current RNN modeling approaches summarize the user state by only taking into
account the sequence of items that the user has interacted with in the past,
without taking into account other essential types of context information such
as the associated types of user-item interactions, the time gaps between events
and the time of day for each interaction. To address this, we propose a new
class of Contextual Recurrent Neural Networks for Recommendation (CRNNs) that
can take into account the contextual information both in the input and output
layers and modifying the behavior of the RNN by combining the context embedding
with the item embedding and more explicitly, in the model dynamics, by
parametrizing the hidden unit transitions as a function of context information.
We compare our CRNNs approach with RNNs and non-sequential baselines and show
good improvements on the next event prediction task
LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee's Advertisement Recommendation
Graph Neural Network (GNN) is the trending solution for item retrieval in
recommendation problems. Most recent reports, however, focus heavily on new
model architectures. This may bring some gaps when applying GNN in the
industrial setup, where, besides the model, constructing the graph and handling
data sparsity also play critical roles in the overall success of the project.
In this work, we report how GNN is applied for large-scale e-commerce item
retrieval at Shopee. We introduce our simple yet novel and impactful techniques
in graph construction, modeling, and handling data skewness. Specifically, we
construct high-quality item graphs by combining strong-signal user behaviors
with high-precision collaborative filtering (CF) algorithm. We then develop a
new GNN architecture named LightSAGE to produce high-quality items' embeddings
for vector search. Finally, we design multiple strategies to handle cold-start
and long-tail items, which are critical in an advertisement (ads) system. Our
models bring improvement in offline evaluations, online A/B tests, and are
deployed to the main traffic of Shopee's Recommendation Advertisement system
Diversify and Conquer: Bandits and Diversity for an Enhanced E-commerce Homepage Experience
In the realm of e-commerce, popular platforms utilize widgets to recommend
advertisements and products to their users. However, the prevalence of mobile
device usage on these platforms introduces a unique challenge due to the
limited screen real estate available. Consequently, the positioning of relevant
widgets becomes pivotal in capturing and maintaining customer engagement. Given
the restricted screen size of mobile devices, widgets placed at the top of the
interface are more prominently displayed and thus attract greater user
attention. Conversely, widgets positioned further down the page require users
to scroll, resulting in reduced visibility and subsequent lower impression
rates. Therefore it becomes imperative to place relevant widgets on top.
However, selecting relevant widgets to display is a challenging task as the
widgets can be heterogeneous, widgets can be introduced or removed at any given
time from the platform. In this work, we model the vertical widget reordering
as a contextual multi-arm bandit problem with delayed batch feedback. The
objective is to rank the vertical widgets in a personalized manner. We present
a two-stage ranking framework that combines contextual bandits with a diversity
layer to improve the overall ranking. We demonstrate its effectiveness through
offline and online A/B results, conducted on proprietary data from Myntra, a
major fashion e-commerce platform in India.Comment: Accepted in Proceedings of Fashionxrecys Workshop, 17th ACM
Conference on Recommender Systems, 202