4,093 research outputs found
Ensemble Methods for Personalized E-Commerce Search Challenge at CIKM Cup 2016
Personalized search has been a hot research topic for many years and has been
widely used in e-commerce. This paper describes our solution to tackle the
challenge of personalized e-commerce search at CIKM Cup 2016. The goal of this
competition is to predict search relevance and re-rank the result items in SERP
according to the personalized search, browsing and purchasing preferences.
Based on a detailed analysis of the provided data, we extract three different
types of features, i.e., statistic features, query-item features and session
features. Different models are used on these features, including logistic
regression, gradient boosted decision trees, rank svm and a novel deep match
model. With the blending of multiple models, a stacking ensemble model is built
to integrate the output of individual models and produce a more accurate
prediction result. Based on these efforts, our solution won the champion of the
competition on all the evaluation metrics.Comment: First Place Solution at CIKM Cup 2016 Track
Behavior Sequence Transformer for E-commerce Recommendation in Alibaba
Deep learning based methods have been widely used in industrial
recommendation systems (RSs). Previous works adopt an Embedding&MLP paradigm:
raw features are embedded into low-dimensional vectors, which are then fed on
to MLP for final recommendations. However, most of these works just concatenate
different features, ignoring the sequential nature of users' behaviors. In this
paper, we propose to use the powerful Transformer model to capture the
sequential signals underlying users' behavior sequences for recommendation in
Alibaba. Experimental results demonstrate the superiority of the proposed
model, which is then deployed online at Taobao and obtain significant
improvements in online Click-Through-Rate (CTR) comparing to two baselines.Comment: 4 pages, 1 figur
Contextual Hybrid Session-based News Recommendation with Recurrent Neural Networks
Recommender systems help users deal with information overload by providing
tailored item suggestions to them. The recommendation of news is often
considered to be challenging, since the relevance of an article for a user can
depend on a variety of factors, including the user's short-term reading
interests, the reader's context, or the recency or popularity of an article.
Previous work has shown that the use of Recurrent Neural Networks is promising
for the next-in-session prediction task, but has certain limitations when only
recorded item click sequences are used as input. In this work, we present a
contextual hybrid, deep learning based approach for session-based news
recommendation that is able to leverage a variety of information types. We
evaluated our approach on two public datasets, using a temporal evaluation
protocol that simulates the dynamics of a news portal in a realistic way. Our
results confirm the benefits of considering additional types of information,
including article popularity and recency, in the proposed way, resulting in
significantly higher recommendation accuracy and catalog coverage than other
session-based algorithms. Additional experiments show that the proposed
parameterizable loss function used in our method also allows us to balance two
usually conflicting quality factors, accuracy and novelty.
Keywords: Artificial Neural Networks, Context-Aware Recommender Systems,
Hybrid Recommender Systems, News Recommender Systems, Session-based
RecommendationComment: 20 pgs. Published at IEEE Access, Volume 7, 2019.
https://ieeexplore.ieee.org/document/890868
Image Matters: Visually modeling user behaviors using Advanced Model Server
In Taobao, the largest e-commerce platform in China, billions of items are
provided and typically displayed with their images. For better user experience
and business effectiveness, Click Through Rate (CTR) prediction in online
advertising system exploits abundant user historical behaviors to identify
whether a user is interested in a candidate ad. Enhancing behavior
representations with user behavior images will help understand user's visual
preference and improve the accuracy of CTR prediction greatly. So we propose to
model user preference jointly with user behavior ID features and behavior
images. However, training with user behavior images brings tens to hundreds of
images in one sample, giving rise to a great challenge in both communication
and computation. To handle these challenges, we propose a novel and efficient
distributed machine learning paradigm called Advanced Model Server (AMS). With
the well known Parameter Server (PS) framework, each server node handles a
separate part of parameters and updates them independently. AMS goes beyond
this and is designed to be capable of learning a unified image descriptor model
shared by all server nodes which embeds large images into low dimensional high
level features before transmitting images to worker nodes. AMS thus
dramatically reduces the communication load and enables the arduous joint
training process. Based on AMS, the methods of effectively combining the images
and ID features are carefully studied, and then we propose a Deep Image CTR
Model. Our approach is shown to achieve significant improvements in both online
and offline evaluations, and has been deployed in Taobao display advertising
system serving the main traffic.Comment: CIKM 201
Deeply Supervised Semantic Model for Click-Through Rate Prediction in Sponsored Search
In sponsored search it is critical to match ads that are relevant to a query
and to accurately predict their likelihood of being clicked. Commercial search
engines typically use machine learning models for both query-ad relevance
matching and click-through-rate (CTR) prediction. However, matching models are
based on the similarity between a query and an ad, ignoring the fact that a
retrieved ad may not attract clicks, while click models rely on click history,
being of limited use for new queries and ads. We propose a deeply supervised
architecture that jointly learns the semantic embeddings of a query and an ad
as well as their corresponding CTR.We also propose a novel cohort negative
sampling technique for learning implicit negative signals. We trained the
proposed architecture using one billion query-ad pairs from a major commercial
web search engine. This architecture improves the best-performing baseline deep
neural architectures by 2\% of AUC for CTR prediction and by statistically
significant 0.5\% of NDCG for query-ad matching.Comment: The first and second authors listed are co-first autho
BoostJet: Towards Combining Statistical Aggregates with Neural Embeddings for Recommendations
Recommenders have become widely popular in recent years because of their
broader applicability in many e-commerce applications. These applications rely
on recommenders for generating advertisements for various offers or providing
content recommendations. However, the quality of the generated recommendations
depends on user features (like demography, temporality), offer features (like
popularity, price), and user-offer features (like implicit or explicit
feedback). Current state-of-the-art recommenders do not explore such diverse
features concurrently while generating the recommendations.
In this paper, we first introduce the notion of Trackers which enables us to
capture the above-mentioned features and thus incorporate users' online
behaviour through statistical aggregates of different features (demography,
temporality, popularity, price). We also show how to capture offer-to-offer
relations, based on their consumption sequence, leveraging neural embeddings
for offers in our Offer2Vec algorithm. We then introduce BoostJet, a novel
recommender which integrates the Trackers along with the neural embeddings
using MatrixNet, an efficient distributed implementation of gradient boosted
decision tree, to improve the recommendation quality significantly. We provide
an in-depth evaluation of BoostJet on Yandex's dataset, collecting online
behaviour from tens of millions of online users, to demonstrate the
practicality of BoostJet in terms of recommendation quality as well as
scalability.Comment: 9 pages, 9 figure
Large-scale Collaborative Filtering with Product Embeddings
The application of machine learning techniques to large-scale personalized
recommendation problems is a challenging task. Such systems must make sense of
enormous amounts of implicit feedback in order to understand user preferences
across numerous product categories. This paper presents a deep learning based
solution to this problem within the collaborative filtering with implicit
feedback framework. Our approach combines neural attention mechanisms, which
allow for context dependent weighting of past behavioral signals, with
representation learning techniques to produce models which obtain extremely
high coverage, can easily incorporate new information as it becomes available,
and are computationally efficient. Offline experiments demonstrate significant
performance improvements when compared to several alternative methods from the
literature. Results from an online setting show that the approach compares
favorably with current production techniques used to produce personalized
product recommendations.Comment: 15 pages, 5 figure
A Social Search Model for Large Scale Social Networks
With the rise of social networks, information on the internet is no longer
solely organized by web pages. Rather, content is generated and shared among
users and organized around their social relations on social networks. This
presents new challenges to information retrieval systems. On a social network
search system, the generation of result sets not only needs to consider keyword
matches, like a traditional web search engine does, but it also needs to take
into account the searcher's social connections and the content's visibility
settings. Besides, search ranking should be able to handle both textual
relevance and the rich social interaction signals from the social network. In
this paper, we present our solution to these two challenges by first
introducing a social retrieval mechanism, and then investigate novel deep
neural networks for the ranking problem. The retrieval system treats social
connections as indexing terms, and generates meaningful results sets by biasing
towards close social connections in a constrained optimization fashion. The
result set is then ranked by a deep neural network that handles textual and
social relevance in a two-tower approach, in which personalization and textual
relevance are addressed jointly. The retrieval mechanism is deployed on
Facebook and is helping billions of users finding postings from their
connections efficiently. Based on the postings being retrieved, we evaluate our
two-tower neutral network, and examine the importance of personalization and
textual signals in the ranking problem.Comment: 8 pages, 8 figure
Deep density networks and uncertainty in recommender systems
Building robust online content recommendation systems requires learning
complex interactions between user preferences and content features. The field
has evolved rapidly in recent years from traditional multi-arm bandit and
collaborative filtering techniques, with new methods employing Deep Learning
models to capture non-linearities. Despite progress, the dynamic nature of
online recommendations still poses great challenges, such as finding the
delicate balance between exploration and exploitation. In this paper we show
how uncertainty estimations can be incorporated by employing them in an
optimistic exploitation/exploration strategy for more efficient exploration of
new recommendations. We provide a novel hybrid deep neural network model, Deep
Density Networks (DDN), which integrates content-based deep learning models
with a collaborative scheme that is able to robustly model and estimate
uncertainty. Finally, we present online and offline results after incorporating
DNN into a real world content recommendation system that serves billions of
recommendations per day, and show the benefit of using DDN in practice
Visually-Aware Fashion Recommendation and Design with Generative Image Models
Building effective recommender systems for domains like fashion is
challenging due to the high level of subjectivity and the semantic complexity
of the features involved (i.e., fashion styles). Recent work has shown that
approaches to `visual' recommendation (e.g.~clothing, art, etc.) can be made
more accurate by incorporating visual signals directly into the recommendation
objective, using `off-the-shelf' feature representations derived from deep
networks. Here, we seek to extend this contribution by showing that
recommendation performance can be significantly improved by learning `fashion
aware' image representations directly, i.e., by training the image
representation (from the pixel level) and the recommender system jointly; this
contribution is related to recent work using Siamese CNNs, though we are able
to show improvements over state-of-the-art recommendation techniques such as
BPR and variants that make use of pre-trained visual features. Furthermore, we
show that our model can be used \emph{generatively}, i.e., given a user and a
product category, we can generate new images (i.e., clothing items) that are
most consistent with their personal taste. This represents a first step towards
building systems that go beyond recommending existing items from a product
corpus, but which can be used to suggest styles and aid the design of new
products.Comment: 10 pages, 6 figures. Accepted by ICDM'17 as a long pape
- …