60,812 research outputs found
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
Recent advancements in deep neural networks for graph-structured data have
led to state-of-the-art performance on recommender system benchmarks. However,
making these methods practical and scalable to web-scale recommendation tasks
with billions of items and hundreds of millions of users remains a challenge.
Here we describe a large-scale deep recommendation engine that we developed and
deployed at Pinterest. We develop a data-efficient Graph Convolutional Network
(GCN) algorithm PinSage, which combines efficient random walks and graph
convolutions to generate embeddings of nodes (i.e., items) that incorporate
both graph structure as well as node feature information. Compared to prior GCN
approaches, we develop a novel method based on highly efficient random walks to
structure the convolutions and design a novel training strategy that relies on
harder-and-harder training examples to improve robustness and convergence of
the model. We also develop an efficient MapReduce model inference algorithm to
generate embeddings using a trained model. We deploy PinSage at Pinterest and
train it on 7.5 billion examples on a graph with 3 billion nodes representing
pins and boards, and 18 billion edges. According to offline metrics, user
studies and A/B tests, PinSage generates higher-quality recommendations than
comparable deep learning and graph-based alternatives. To our knowledge, this
is the largest application of deep graph embeddings to date and paves the way
for a new generation of web-scale recommender systems based on graph
convolutional architectures.Comment: KDD 201
Counterfactual Risk Minimization: Learning from Logged Bandit Feedback
We develop a learning principle and an efficient algorithm for batch learning
from logged bandit feedback. This learning setting is ubiquitous in online
systems (e.g., ad placement, web search, recommendation), where an algorithm
makes a prediction (e.g., ad ranking) for a given input (e.g., query) and
observes bandit feedback (e.g., user clicks on presented ads). We first address
the counterfactual nature of the learning problem through propensity scoring.
Next, we prove generalization error bounds that account for the variance of the
propensity-weighted empirical risk estimator. These constructive bounds give
rise to the Counterfactual Risk Minimization (CRM) principle. We show how CRM
can be used to derive a new learning method -- called Policy Optimizer for
Exponential Models (POEM) -- for learning stochastic linear rules for
structured output prediction. We present a decomposition of the POEM objective
that enables efficient stochastic gradient optimization. POEM is evaluated on
several multi-label classification problems showing substantially improved
robustness and generalization performance compared to the state-of-the-art.Comment: 10 page
SEMANTIC WEB CONTENT MINING FOR CONTENT-BASED RECOMMENDER SYSTEMS
The fast-growing presence of data is crucial to all sectors and domain as it is being harnessed to solve various real-time problems, such as product recommendation. Web
content mining, which is referred to a data mining for web textual content can be used to retrieve, refine and analyze data to solve these problems. It is therefore important that the web content mining process is optimized to improve preprocessing of web textual data for efficient recommendation. Currently, for content-based recommendations, semantic analysis of text from webpages seems to be a major problem. In this research, we present
a semantic web content mining approach for recommender systems. The methodology is based on two major phases. The first phase is the semantic preprocessing of data. This
phase uses both a developed ontology and an existing ontology together with the typical text preprocessing steps such as filtration stemming and so on. The second phase uses the Naïve Bayes algorithm to make the recommendations. The output of the system is evaluated using precision, recall and f-measure. The results from the system showed that the semantic preprocessing improved the recommendation accuracy of the recommender system by 5.2% over the existing approach. Also the developed system is able to provide a platform for content based recommendation which provides an edge over the existing recommender approach because it is able to analyze the textual contents of users
feedback on a product
On hybrid modular recommendation systems for video streaming
The recommendation systems aim to improve the user engagement by recommending
appropriate personalized content to users, exploiting information about their
preferences. We propose the enabler, a hybrid recommendation system which
employs various machine-learning (ML) algorithms for learning an efficient
combination of several recommendation algorithms and selects the best blending
for a given input.Specifically, it integrates three layers, namely, the trainer
which trains the underlying recommenders, the blender which determines the most
efficient combination of the recommenders, and the tester for assessing the
performance of the system. The enabler incorporates a variety of recommendation
algorithms that span from collaborative filtering and content-based techniques
to ones based on neural networks. It uses the nested cross validation for
automatically selecting the best ML algorithm along with its hyper-parameter
values for the given input, according to a specific metric. The enabler can be
easily extended to include other recommenders and blenders. The enabler has
been extensively evaluated in the context of video-streaming. It outperforms
various other algorithms, when tested on the Movielens 1M benchmark
dataset.encouraging results. Moreover For example, it achieves an RMSE of
0.8206, compared to the state-of-the-art performance of the AutoRec and SVD,
0.827 and 0.845, respectively. A pilot web-based recommendation system was
developed and tested in the production environment of a large telecom operator
in Greece. Volunteer customers of the video-streaming service provided by the
telecom operator employed the system in the context of an out-in-the-wild field
study with a post-analysis of the enabler, using the collected ratings of the
pilot, demonstrated that it significantly outperforms several popular
recommendation algorithms
A Hybrid Web Recommendation System based on the Improved Association Rule Mining Algorithm
As the growing interest of web recommendation systems those are applied to
deliver customized data for their users, we started working on this system.
Generally the recommendation systems are divided into two major categories such
as collaborative recommendation system and content based recommendation system.
In case of collaborative recommen-dation systems, these try to seek out users
who share same tastes that of given user as well as recommends the websites
according to the liking given user. Whereas the content based recommendation
systems tries to recommend web sites similar to those web sites the user has
liked. In the recent research we found that the efficient technique based on
asso-ciation rule mining algorithm is proposed in order to solve the problem of
web page recommendation. Major problem of the same is that the web pages are
given equal importance. Here the importance of pages changes according to the
fre-quency of visiting the web page as well as amount of time user spends on
that page. Also recommendation of newly added web pages or the pages those are
not yet visited by users are not included in the recommendation set. To
over-come this problem, we have used the web usage log in the adaptive
association rule based web mining where the asso-ciation rules were applied to
personalization. This algorithm was purely based on the Apriori data mining
algorithm in order to generate the association rules. However this method also
suffers from some unavoidable drawbacks. In this paper we are presenting and
investigating the new approach based on weighted Association Rule Mining
Algorithm and text mining. This is improved algorithm which adds semantic
knowledge to the results, has more efficiency and hence gives better quality
and performances as compared to existing approaches.Comment: 9 pages, 7 figures, 2 table
Performing Hybrid Recommendation in Intermodal Transportation – the FTMarket System’s Recommendation Module
Diverse recommendation techniques have been already proposed and encapsulated into several e-business applications, aiming to perform a more accurate evaluation of the existing information and accordingly augment the assistance provided to the users involved. This paper reports on the development and integration of a recommendation module in an agent-based transportation transactions management system. The module is built according to a novel hybrid recommendation technique, which combines the advantages of collaborative filtering and knowledge-based approaches. The proposed technique and supporting module assist customers in considering in detail alternative transportation transactions that satisfy their requests, as well as in evaluating completed transactions. The related services are invoked through a software agent that constructs the appropriate knowledge rules and performs a synthesis of the recommendation policy
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