7,036 research outputs found
Personalized Purchase Prediction of Market Baskets with Wasserstein-Based Sequence Matching
Personalization in marketing aims at improving the shopping experience of
customers by tailoring services to individuals. In order to achieve this,
businesses must be able to make personalized predictions regarding the next
purchase. That is, one must forecast the exact list of items that will comprise
the next purchase, i.e., the so-called market basket. Despite its relevance to
firm operations, this problem has received surprisingly little attention in
prior research, largely due to its inherent complexity. In fact,
state-of-the-art approaches are limited to intuitive decision rules for pattern
extraction. However, the simplicity of the pre-coded rules impedes performance,
since decision rules operate in an autoregressive fashion: the rules can only
make inferences from past purchases of a single customer without taking into
account the knowledge transfer that takes place between customers. In contrast,
our research overcomes the limitations of pre-set rules by contributing a novel
predictor of market baskets from sequential purchase histories: our predictions
are based on similarity matching in order to identify similar purchase habits
among the complete shopping histories of all customers. Our contributions are
as follows: (1) We propose similarity matching based on subsequential dynamic
time warping (SDTW) as a novel predictor of market baskets. Thereby, we can
effectively identify cross-customer patterns. (2) We leverage the Wasserstein
distance for measuring the similarity among embedded purchase histories. (3) We
develop a fast approximation algorithm for computing a lower bound of the
Wasserstein distance in our setting. An extensive series of computational
experiments demonstrates the effectiveness of our approach. The accuracy of
identifying the exact market baskets based on state-of-the-art decision rules
from the literature is outperformed by a factor of 4.0.Comment: Accepted for oral presentation at 25th ACM SIGKDD Conference on
Knowledge Discovery and Data Mining (KDD 2019
Prediction Techniques in Internet of Things (IoT) Environment: A Comparative Study
Socialization and Personalization in Internet of Things (IOT) environment are the current trends in computing research. Most of the research work stresses the importance of predicting the service & providing socialized and personalized services. This paper presents a survey report on different techniques used for predicting user intention in wide variety of IOT based applications like smart mobile, smart television, web mining, weather forecasting, health-care/medical, robotics, road-traffic, educational data mining, natural calamities, retail banking, e-commerce, wireless networks & social networking. As per the survey made the prediction techniques are used for: predicting the application that can be accessed by the mobile user, predicting the next page to be accessed by web user, predicting the users favorite TV program, predicting user navigational patterns and usage needs on websites & also to extract the users browsing behavior, predicting future climate conditions, predicting whether a patient is suffering from a disease, predicting user intention to make implicit and human-like interactions possible by accepting implicit commands, predicting the amount of traffic occurring at a particular location, predicting student performance in schools & colleges, predicting & estimating the frequency of natural calamities occurrences like floods, earthquakes over a long period of time & also to take precautionary measures, predicting & detecting false user trying to make transaction in the name of genuine user, predicting the actions performed by the user to improve the business, predicting & detecting the intruder acting in the network, predicting the mood transition information of the user by using context history, etc. This paper also discusses different techniques like Decision Tree algorithm, Artificial Intelligence and Data Mining based Machine learning techniques, Content and Collaborative based Recommender algorithms used for prediction
TimeKit: A Time-series Forecasting-based Upgrade Kit for Collaborative Filtering
Recommender systems are a long-standing research problem in data mining and
machine learning. They are incremental in nature, as new user-item interaction
logs arrive. In real-world applications, we need to periodically train a
collaborative filtering algorithm to extract user/item embedding vectors and
therefore, a time-series of embedding vectors can be naturally defined. We
present a time-series forecasting-based upgrade kit (TimeKit), which works in
the following way: it i) first decides a base collaborative filtering
algorithm, ii) extracts user/item embedding vectors with the base algorithm
from user-item interaction logs incrementally, e.g., every month, iii) trains
our time-series forecasting model with the extracted time-series of embedding
vectors, and then iv) forecasts the future embedding vectors and recommend with
their dot-product scores owing to a recent breakthrough in processing
complicated time-series data, i.e., neural controlled differential equations
(NCDEs). Our experiments with four real-world benchmark datasets show that the
proposed time-series forecasting-based upgrade kit can significantly enhance
existing popular collaborative filtering algorithms.Comment: Accepted at IEEE BigData 202
A Survey on Various Techniques in Internet of Things (IoT) Implementation: A Comparative Study
As per the current trends in computing research socialization and Personalization in Internet of Things (IOT) environment are quite trending and they are being widely used. The main aim of research work is to provide socialized and personalized services along with creating awareness of predicting the service. Here various kind of methods are discussed which can be used for predicting user intention in large variety of IOT based applications such as smart mobile, smart television, web mining, weather forecasting, health-care/medical, robotics, road-traffic, educational data mining, natural calamities, retail banking, e-commerce, wireless networks & social networking. By common consent it is found that the prediction is made usually for finding techniques that can be accessed by the mobile user, predicting the next page that is most likely to be used by web user, predicting favorite and most likely TV program that can be viewed by user, getting a list of browsing usage and need of user and also predicting user navigational patterns, predicting future climate conditions, predicting the health and welfare of user, predicting user intention so that implicit could be made and human-like interactions could be possible by accepting implicit commands, predicting the exact amount of traffic at a particular location, predicting curricular performance of student in schools & colleges, having prediction of frequency of natural calamities and their occurrences such as floods, earthquakes over a long period of time & also the required time in which precautionary measures could be adopted, predicting & detecting the frauds in which false user try to make transaction in the name of genuine user, predicting the steps and work done by the user to improve the business, predicting & detecting the intruder acting in the network, by the help of context history predicting the mood transition information of the user, etc. Here in this topic of discussion, different techniques such as Decision Tree algorithm, Artificial Intelligence and Data Mining based Machine learning techniques, Content and Collaborative based Recommender algorithms are used for prediction
Comparing time series with machine learning-based prediction approaches for violation management in cloud SLAs
© 2018 In cloud computing, service level agreements (SLAs) are legal agreements between a service provider and consumer that contain a list of obligations and commitments which need to be satisfied by both parties during the transaction. From a service provider's perspective, a violation of such a commitment leads to penalties in terms of money and reputation and thus has to be effectively managed. In the literature, this problem has been studied under the domain of cloud service management. One aspect required to manage cloud services after the formation of SLAs is to predict the future Quality of Service (QoS) of cloud parameters to ascertain if they lead to violations. Various approaches in the literature perform this task using different prediction approaches however none of them study the accuracy of each. However, it is important to do this as the results of each prediction approach vary according to the pattern of the input data and selecting an incorrect choice of a prediction algorithm could lead to service violation and penalties. In this paper, we test and report the accuracy of time series and machine learning-based prediction approaches. In each category, we test many different techniques and rank them according to their order of accuracy in predicting future QoS. Our analysis helps the cloud service provider to choose an appropriate prediction approach (whether time series or machine learning based) and further to utilize the best method depending on input data patterns to obtain an accurate prediction result and better manage their SLAs to avoid violation penalties
Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
RNNs have been shown to be excellent models for sequential data and in
particular for data that is generated by users in an session-based manner. The
use of RNNs provides impressive performance benefits over classical methods in
session-based recommendations. In this work we introduce novel ranking loss
functions tailored to RNNs in the recommendation setting. The improved
performance of these losses over alternatives, along with further tricks and
refinements described in this work, allow for an overall improvement of up to
35% in terms of MRR and Recall@20 over previous session-based RNN solutions and
up to 53% over classical collaborative filtering approaches. Unlike data
augmentation-based improvements, our method does not increase training times
significantly. We further demonstrate the performance gain of the RNN over
baselines in an online A/B test.Comment: CIKM'18, authors' versio
Service workload patterns for QoS-driven cloud resource management
Cloud service providers negotiate SLAs for customer services they offer based on the reliability of performance and availability of their lower-level platform infrastructure. While availability management is more mature, performance management is less reliable. In order to support a continuous approach that supports the initial static infrastructure configuration as well as dynamic reconfiguration and auto-scaling, an accurate and efficient solution is required. We propose a prediction technique that combines a workload pattern mining approach with a traditional collaborative filtering solution to meet the accuracy and efficiency requirements. Service workload patterns abstract common infrastructure workloads from monitoring logs and act as a part of a first-stage high-performant configuration mechanism before more complex traditional methods are considered. This enhances current reactive rule-based scalability approaches and basic prediction techniques by a hybrid prediction solution. Uncertainty and noise are additional challenges that emerge in multi-layered, often federated cloud architectures. We specifically add log smoothing combined with a fuzzy logic approach to make the prediction solution more robust in the context of these challenges
- …