11 research outputs found
Hybrid moments of the Riemann zeta-function
The "hybrid" moments
of the Riemann zeta-function on the critical line are
studied. The expected upper bound for the above expression is
. This is shown to be true for certain specific
values of the natural numbers , and the explicitly determined range
of . The application to a mean square bound for the Mellin
transform function of is given.Comment: 27 page
Intent-Aware Contextual Recommendation System
Recommender systems take inputs from user history, use an internal ranking
algorithm to generate results and possibly optimize this ranking based on
feedback. However, often the recommender system is unaware of the actual intent
of the user and simply provides recommendations dynamically without properly
understanding the thought process of the user. An intelligent recommender
system is not only useful for the user but also for businesses which want to
learn the tendencies of their users. Finding out tendencies or intents of a
user is a difficult problem to solve.
Keeping this in mind, we sought out to create an intelligent system which
will keep track of the user's activity on a web-application as well as
determine the intent of the user in each session. We devised a way to encode
the user's activity through the sessions. Then, we have represented the
information seen by the user in a high dimensional format which is reduced to
lower dimensions using tensor factorization techniques. The aspect of intent
awareness (or scoring) is dealt with at this stage. Finally, combining the user
activity data with the contextual information gives the recommendation score.
The final recommendations are then ranked using filtering and collaborative
recommendation techniques to show the top-k recommendations to the user. A
provision for feedback is also envisioned in the current system which informs
the model to update the various weights in the recommender system. Our overall
model aims to combine both frequency-based and context-based recommendation
systems and quantify the intent of a user to provide better recommendations.
We ran experiments on real-world timestamped user activity data, in the
setting of recommending reports to the users of a business analytics tool and
the results are better than the baselines. We also tuned certain aspects of our
model to arrive at optimized results.Comment: Presented at the 5th International Workshop on Data Science and Big
Data Analytics (DSBDA), 17th IEEE International Conference on Data Mining
(ICDM) 2017; 8 pages; 4 figures; Due to the limitation "The abstract field
cannot be longer than 1,920 characters," the abstract appearing here is
slightly shorter than the one in the PDF fil
PersonaSAGE: A Multi-Persona Graph Neural Network
Graph Neural Networks (GNNs) have become increasingly important in recent
years due to their state-of-the-art performance on many important downstream
applications. Existing GNNs have mostly focused on learning a single node
representation, despite that a node often exhibits polysemous behavior in
different contexts. In this work, we develop a persona-based graph neural
network framework called PersonaSAGE that learns multiple persona-based
embeddings for each node in the graph. Such disentangled representations are
more interpretable and useful than a single embedding. Furthermore, PersonaSAGE
learns the appropriate set of persona embeddings for each node in the graph,
and every node can have a different number of assigned persona embeddings. The
framework is flexible enough and the general design helps in the wide
applicability of the learned embeddings to suit the domain. We utilize publicly
available benchmark datasets to evaluate our approach and against a variety of
baselines. The experiments demonstrate the effectiveness of PersonaSAGE for a
variety of important tasks including link prediction where we achieve an
average gain of 15% while remaining competitive for node classification.
Finally, we also demonstrate the utility of PersonaSAGE with a case study for
personalized recommendation of different entity types in a data management
platform.Comment: 10 pages, 6 figures, 7 table
CGC: Contrastive Graph Clustering for Community Detection and Tracking
Given entities and their interactions in the web data, which may have
occurred at different time, how can we find communities of entities and track
their evolution? In this paper, we approach this important task from graph
clustering perspective. Recently, state-of-the-art clustering performance in
various domains has been achieved by deep clustering methods. Especially, deep
graph clustering (DGC) methods have successfully extended deep clustering to
graph-structured data by learning node representations and cluster assignments
in a joint optimization framework. Despite some differences in modeling choices
(e.g., encoder architectures), existing DGC methods are mainly based on
autoencoders and use the same clustering objective with relatively minor
adaptations. Also, while many real-world graphs are dynamic, previous DGC
methods considered only static graphs. In this work, we develop CGC, a novel
end-to-end framework for graph clustering, which fundamentally differs from
existing methods. CGC learns node embeddings and cluster assignments in a
contrastive graph learning framework, where positive and negative samples are
carefully selected in a multi-level scheme such that they reflect hierarchical
community structures and network homophily. Also, we extend CGC for
time-evolving data, where temporal graph clustering is performed in an
incremental learning fashion, with the ability to detect change points.
Extensive evaluation on real-world graphs demonstrates that the proposed CGC
consistently outperforms existing methods.Comment: TheWebConf 2022 Research Trac