467 research outputs found
A Cross-Domain Recommender System with Kernel-Induced Knowledge Transfer for Overlapping Entities
© 2012 IEEE. The aim of recommender systems is to automatically identify user preferences within collected data, then use those preferences to make recommendations that help with decisions. However, recommender systems suffer from data sparsity problem, which is particularly prevalent in newly launched systems that have not yet had enough time to amass sufficient data. As a solution, cross-domain recommender systems transfer knowledge from a source domain with relatively rich data to assist recommendations in the target domain. These systems usually assume that the entities either fully overlap or do not overlap at all. In practice, it is more common for the entities in the two domains to partially overlap. Moreover, overlapping entities may have different expressions in each domain. Neglecting these two issues reduces prediction accuracy of cross-domain recommender systems in the target domain. To fully exploit partially overlapping entities and improve the accuracy of predictions, this paper presents a cross-domain recommender system based on kernel-induced knowledge transfer, called KerKT. Domain adaptation is used to adjust the feature spaces of overlapping entities, while diffusion kernel completion is used to correlate the non-overlapping entities between the two domains. With this approach, knowledge is effectively transferred through the overlapping entities, thus alleviating data sparsity issues. Experiments conducted on four data sets, each with three sparsity ratios, show that KerKT has 1.13%-20% better prediction accuracy compared with six benchmarks. In addition, the results indicate that transferring knowledge from the source domain to the target domain is both possible and beneficial with even small overlaps
A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions
Traditional recommendation systems are faced with two long-standing
obstacles, namely, data sparsity and cold-start problems, which promote the
emergence and development of Cross-Domain Recommendation (CDR). The core idea
of CDR is to leverage information collected from other domains to alleviate the
two problems in one domain. Over the last decade, many efforts have been
engaged for cross-domain recommendation. Recently, with the development of deep
learning and neural networks, a large number of methods have emerged. However,
there is a limited number of systematic surveys on CDR, especially regarding
the latest proposed methods as well as the recommendation scenarios and
recommendation tasks they address. In this survey paper, we first proposed a
two-level taxonomy of cross-domain recommendation which classifies different
recommendation scenarios and recommendation tasks. We then introduce and
summarize existing cross-domain recommendation approaches under different
recommendation scenarios in a structured manner. We also organize datasets
commonly used. We conclude this survey by providing several potential research
directions about this field
Time Interval-enhanced Graph Neural Network for Shared-account Cross-domain Sequential Recommendation
Shared-account Cross-domain Sequential Recommendation (SCSR) task aims to
recommend the next item via leveraging the mixed user behaviors in multiple
domains. It is gaining immense research attention as more and more users tend
to sign up on different platforms and share accounts with others to access
domain-specific services. Existing works on SCSR mainly rely on mining
sequential patterns via Recurrent Neural Network (RNN)-based models, which
suffer from the following limitations: 1) RNN-based methods overwhelmingly
target discovering sequential dependencies in single-user behaviors. They are
not expressive enough to capture the relationships among multiple entities in
SCSR. 2) All existing methods bridge two domains via knowledge transfer in the
latent space, and ignore the explicit cross-domain graph structure. 3) None
existing studies consider the time interval information among items, which is
essential in the sequential recommendation for characterizing different items
and learning discriminative representations for them. In this work, we propose
a new graph-based solution, namely TiDA-GCN, to address the above challenges.
Specifically, we first link users and items in each domain as a graph. Then, we
devise a domain-aware graph convolution network to learn userspecific node
representations. To fully account for users' domainspecific preferences on
items, two effective attention mechanisms are further developed to selectively
guide the message passing process. Moreover, to further enhance item- and
account-level representation learning, we incorporate the time interval into
the message passing, and design an account-aware self-attention module for
learning items' interactive characteristics. Experiments demonstrate the
superiority of our proposed method from various aspects.Comment: 15 pages, 6 figure
Distributional Domain-Invariant Preference Matching for Cross-Domain Recommendation
Learning accurate cross-domain preference mappings in the absence of
overlapped users/items has presented a persistent challenge in Non-overlapping
Cross-domain Recommendation (NOCDR). Despite the efforts made in previous
studies to address NOCDR, several limitations still exist. Specifically, 1)
while some approaches substitute overlapping users/items with overlapping
behaviors, they cannot handle NOCDR scenarios where such auxiliary information
is unavailable; 2) often, cross-domain preference mapping is modeled by
learning deterministic explicit representation matchings between sampled users
in two domains. However, this can be biased due to individual preferences and
thus fails to incorporate preference continuity and universality of the general
population. In light of this, we assume that despite the scattered nature of
user behaviors, there exists a consistent latent preference distribution shared
among common people. Modeling such distributions further allows us to capture
the continuity in user behaviors within each domain and discover preference
invariance across domains. To this end, we propose a Distributional
domain-invariant Preference Matching method for non-overlapping Cross-Domain
Recommendation (DPMCDR). For each domain, we hierarchically approximate a
posterior of domain-level preference distribution with empirical evidence
derived from user-item interactions. Next, we aim to build distributional
implicit matchings between the domain-level preferences of two domains. This
process involves mapping them to a shared latent space and seeking a consensus
on domain-invariant preference by minimizing the distance between their
distributional representations therein. In this way, we can identify the
alignment of two non-overlapping domains if they exhibit similar patterns of
domain-invariant preference.Comment: 9 pages, 5 figures, full research paper accepted by ICDM 202
Contextual Social Networking
The thesis centers around the multi-faceted research question of how contexts may
be detected and derived that can be used for new context aware Social Networking
services and for improving the usefulness of existing Social Networking services, giving
rise to the notion of Contextual Social Networking. In a first foundational part,
we characterize the closely related fields of Contextual-, Mobile-, and Decentralized
Social Networking using different methods and focusing on different detailed
aspects. A second part focuses on the question of how short-term and long-term
social contexts as especially interesting forms of context for Social Networking may
be derived. We focus on NLP based methods for the characterization of social relations
as a typical form of long-term social contexts and on Mobile Social Signal
Processing methods for deriving short-term social contexts on the basis of geometry
of interaction and audio. We furthermore investigate, how personal social agents
may combine such social context elements on various levels of abstraction. The third
part discusses new and improved context aware Social Networking service concepts.
We investigate special forms of awareness services, new forms of social information
retrieval, social recommender systems, context aware privacy concepts and services
and platforms supporting Open Innovation and creative processes.
This version of the thesis does not contain the included publications because of
copyrights of the journals etc. Contact in terms of the version with all included
publications: Georg Groh, [email protected] zentrale Gegenstand der vorliegenden Arbeit ist die vielschichtige Frage, wie Kontexte detektiert und abgeleitet werden können, die dazu dienen können, neuartige kontextbewusste Social Networking Dienste zu schaffen und bestehende Dienste in ihrem Nutzwert zu verbessern. Die (noch nicht abgeschlossene) erfolgreiche Umsetzung dieses Programmes führt auf ein Konzept, das man als Contextual Social Networking bezeichnen kann. In einem grundlegenden ersten Teil werden die eng zusammenhängenden Gebiete Contextual Social Networking, Mobile Social Networking und Decentralized Social Networking mit verschiedenen Methoden und unter Fokussierung auf verschiedene Detail-Aspekte näher beleuchtet und in Zusammenhang gesetzt. Ein zweiter Teil behandelt die Frage, wie soziale Kurzzeit- und Langzeit-Kontexte als für das Social Networking besonders interessante Formen von Kontext gemessen und abgeleitet werden können. Ein Fokus liegt hierbei auf NLP Methoden zur Charakterisierung sozialer Beziehungen als einer typischen Form von sozialem Langzeit-Kontext. Ein weiterer Schwerpunkt liegt auf Methoden aus dem Mobile Social Signal Processing zur Ableitung sinnvoller sozialer Kurzzeit-Kontexte auf der Basis von Interaktionsgeometrien und Audio-Daten. Es wird ferner untersucht, wie persönliche soziale Agenten Kontext-Elemente verschiedener Abstraktionsgrade miteinander kombinieren können. Der dritte Teil behandelt neuartige und verbesserte Konzepte für kontextbewusste Social Networking Dienste. Es werden spezielle Formen von Awareness Diensten, neue Formen von sozialem Information Retrieval, Konzepte für kontextbewusstes Privacy Management und Dienste und Plattformen zur Unterstützung von Open Innovation und Kreativität untersucht und vorgestellt. Diese Version der Habilitationsschrift enthält die inkludierten Publikationen zurVermeidung von Copyright-Verletzungen auf Seiten der Journals u.a. nicht. Kontakt in Bezug auf die Version mit allen inkludierten Publikationen: Georg Groh, [email protected]
Understanding Variability-Aware Analysis in Low-Maturity Variant-Rich Systems
Context: Software systems often exist in many variants to support varying stakeholder requirements, such as specific market segments or hardware constraints. Systems with many variants (a.k.a. variant-rich systems) are highly complex due to the variability introduced to support customization. As such, assuring the quality of these systems is also challenging since traditional single-system analysis techniques do not scale when applied. To tackle this complexity, several variability-aware analysis techniques have been conceived in the last two decades to assure the quality of a branch of variant-rich systems called software product lines. Unfortunately, these techniques find little application in practice since many organizations do use product-line engineering techniques, but instead rely on low-maturity \clo~strategies to manage their software variants. For instance, to perform an analysis that checks that all possible variants that can be configured by customers (or vendors) in a car personalization system conform to specified performance requirements, an organization needs to explicitly model system variability. However, in low-maturity variant-rich systems, this and similar kinds of analyses are challenging to perform due to (i) immature architectures that do not systematically account for variability, (ii) redundancy that is not exploited to reduce analysis effort, and (iii) missing essential meta-information, such as relationships between features and their implementation in source code.Objective: The overarching goal of the PhD is to facilitate quality assurance in low-maturity variant-rich systems. Consequently, in the first part of the PhD (comprising this thesis) we focus on gaining a better understanding of quality assurance needs in such systems and of their properties.Method: Our objectives are met by means of (i) knowledge-seeking research through case studies of open-source systems as well as surveys and interviews with practitioners; and (ii) solution-seeking research through the implementation and systematic evaluation of a recommender system that supports recording the information necessary for quality assurance in low-maturity variant-rich systems. With the former, we investigate, among other things, industrial needs and practices for analyzing variant-rich systems; and with the latter, we seek to understand how to obtain information necessary to leverage variability-aware analyses.Results: Four main results emerge from this thesis: first, we present the state-of-practice in assuring the quality of variant-rich systems, second, we present our empirical understanding of features and their characteristics, including information sources for locating them; third, we present our understanding of how best developers\u27 proactive feature location activities can be supported during development; and lastly, we present our understanding of how features are used in the code of non-modular variant-rich systems, taking the case of feature scattering in the Linux kernel.Future work: In the second part of the PhD, we will focus on processes for adapting variability-aware analyses to low-maturity variant-rich systems.Keywords:\ua0Variant-rich Systems, Quality Assurance, Low Maturity Software Systems, Recommender Syste
Workshop NotesInternational Workshop ``What can FCA do for Artificial Intelligence?'' (FCA4AI 2015)
International audienceThis volume includes the proceedings of the fourth edition of the FCA4AI --What can FCA do for Artificial Intelligence?-- Workshop co-located with the IJCAI 2015 Conference in Buenos Aires (Argentina). Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classification. FCA allows one to build a concept lattice and a system of dependencies (implications) which can be used for many AI needs, e.g. knowledge discovery, learning, knowledge representation, reasoning, ontology engineering, as well as information retrieval and text processing. There are many ``natural links'' between FCA and AI, and the present workshop is organized for discussing about these links and more generally for improving the links between knowledge discovery based on FCA and knowledge management in artificial intelligence
Computation in Complex Networks
Complex networks are one of the most challenging research focuses of disciplines, including physics, mathematics, biology, medicine, engineering, and computer science, among others. The interest in complex networks is increasingly growing, due to their ability to model several daily life systems, such as technology networks, the Internet, and communication, chemical, neural, social, political and financial networks. The Special Issue “Computation in Complex Networks" of Entropy offers a multidisciplinary view on how some complex systems behave, providing a collection of original and high-quality papers within the research fields of: • Community detection • Complex network modelling • Complex network analysis • Node classification • Information spreading and control • Network robustness • Social networks • Network medicin
- …