9,201 research outputs found
Survey On Moving Towards Frequent Pattern Growth for Infrequent Weighted Itemset Mining
In data mining and knowledge discovery technique domain, frequent pattern mining plays an important role but it does not consider different weight value of the items. Association Rule Mining is to find the correlation between data. The frequent itemsets are patterns or items like itemsets, substructures, or subsequences that come out in a data set frequently or continuously. In this paper we are presenting survey of various frequent pattern mining and weighted itemset mining. Different articles related to frequent and weighted infrequent itemset mining were proposed. This paper focus on survey of various Existing Algorithms related to frequent and infrequent itemset mining which creates a path for future researches in the field of Association Rule Mining
Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World
This report documents the program and the outcomes of GI-Dagstuhl Seminar
16394 "Software Performance Engineering in the DevOps World".
The seminar addressed the problem of performance-aware DevOps. Both, DevOps
and performance engineering have been growing trends over the past one to two
years, in no small part due to the rise in importance of identifying
performance anomalies in the operations (Ops) of cloud and big data systems and
feeding these back to the development (Dev). However, so far, the research
community has treated software engineering, performance engineering, and cloud
computing mostly as individual research areas. We aimed to identify
cross-community collaboration, and to set the path for long-lasting
collaborations towards performance-aware DevOps.
The main goal of the seminar was to bring together young researchers (PhD
students in a later stage of their PhD, as well as PostDocs or Junior
Professors) in the areas of (i) software engineering, (ii) performance
engineering, and (iii) cloud computing and big data to present their current
research projects, to exchange experience and expertise, to discuss research
challenges, and to develop ideas for future collaborations
TransAct: Transformer-based Realtime User Action Model for Recommendation at Pinterest
Sequential models that encode user activity for next action prediction have
become a popular design choice for building web-scale personalized
recommendation systems. Traditional methods of sequential recommendation either
utilize end-to-end learning on realtime user actions, or learn user
representations separately in an offline batch-generated manner. This paper (1)
presents Pinterest's ranking architecture for Homefeed, our personalized
recommendation product and the largest engagement surface; (2) proposes
TransAct, a sequential model that extracts users' short-term preferences from
their realtime activities; (3) describes our hybrid approach to ranking, which
combines end-to-end sequential modeling via TransAct with batch-generated user
embeddings. The hybrid approach allows us to combine the advantages of
responsiveness from learning directly on realtime user activity with the
cost-effectiveness of batch user representations learned over a longer time
period. We describe the results of ablation studies, the challenges we faced
during productionization, and the outcome of an online A/B experiment, which
validates the effectiveness of our hybrid ranking model. We further demonstrate
the effectiveness of TransAct on other surfaces such as contextual
recommendations and search. Our model has been deployed to production in
Homefeed, Related Pins, Notifications, and Search at Pinterest.Comment: \c{opyright} {ACM} {2023}. This is the author's version of the work.
It is posted here for your personal use. Not for redistribution. The
definitive Version of Record was published in KDD'23,
http://dx.doi.org/10.1145/3580305.359991
AliCG: Fine-grained and Evolvable Conceptual Graph Construction for Semantic Search at Alibaba
Conceptual graphs, which is a particular type of Knowledge Graphs, play an
essential role in semantic search. Prior conceptual graph construction
approaches typically extract high-frequent, coarse-grained, and time-invariant
concepts from formal texts. In real applications, however, it is necessary to
extract less-frequent, fine-grained, and time-varying conceptual knowledge and
build taxonomy in an evolving manner. In this paper, we introduce an approach
to implementing and deploying the conceptual graph at Alibaba. Specifically, We
propose a framework called AliCG which is capable of a) extracting fine-grained
concepts by a novel bootstrapping with alignment consensus approach, b) mining
long-tail concepts with a novel low-resource phrase mining approach, c)
updating the graph dynamically via a concept distribution estimation method
based on implicit and explicit user behaviors. We have deployed the framework
at Alibaba UC Browser. Extensive offline evaluation as well as online A/B
testing demonstrate the efficacy of our approach.Comment: Accepted by KDD 2021 (Applied Data Science Track
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