3,067 research outputs found
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
CDMF: A Deep Learning Model based on Convolutional and Dense-layer Matrix Factorization for Context-Aware Recommendation
We proposes a novel deep neural network based recommendation model named Convolutional and Dense-layer Matrix Factorization (CDMF) for Context-aware recommendation, which is to combine multi-source information from item description and tag information. CDMF adopts a convolution neural network to extract hidden feature from item description as document and then fuses it with tag information via a full connection layer, thus generates a comprehensive feature vector. Based on the matrix factorization method, CDMF makes rating prediction based on the fused information of both users and items. Experiments on a real dataset show that the proposed deep learning model obviously outperforms the state-of-art recommendation methods
Recherche d'Information Sociale et Recommandation: Etat d'art et travaux futurs
International audienceThe explosion of web 2.0 and social networks has created an enormous and rewarding source of information that has motivated researchers in different fields to exploit it. Our work revolves around the issue of access and identification of social information and their use in building a user profile enriched with a social dimension, and operating in a process of personalization and recommendation. We study several approaches of Social IR (Information Retrieval), distinguished by the type of incorporated social information. We also study various social recommendation approaches classified by the type of recommendation. We then present a study of techniques for modeling the social user profile dimension, followed by a critical discussion. Thus, we propose our social recommendation approach integrating an advanced social user profile model.Lâexplosion du web 2.0 et des rĂ©seaux sociaux a crĂ©e une source dâinformation Ă©norme et enrichissante qui a motivĂ© les chercheurs dans diffĂ©rents domaines Ă lâexploiter. Notre travail sâarticule autour de la problĂ©matique dâaccĂšs et dâidentification des informations sociales et leur exploitation dans la construction dâun profil utilisateur enrichi dâune dimension sociale, et son exploitation dans un processus de personnalisation et de recommandation. Nous Ă©tudions diffĂ©rentes approches sociales de RI (Recherche dâInformation), distinguĂ©es par le type dâinformations sociales incorporĂ©es. Nous Ă©tudions Ă©galement diverses approches de recommandation sociale classĂ©es par le type de recommandation. Nous exposons ensuite une Ă©tude des techniques de modĂ©lisation de la dimension sociale du profil utilisateur, suivie par une discussion critique. Ainsi, nous prĂ©sentons notre approche de recommandation sociale proposĂ©e intĂ©grant un modĂšle avancĂ© de profil utilisateur social
Tensor Learning for Recovering Missing Information: Algorithms and Applications on Social Media
Real-time social systems like Facebook, Twitter, and Snapchat have been growing
rapidly, producing exabytes of data in different views or aspects. Coupled with more
and more GPS-enabled sharing of videos, images, blogs, and tweets that provide valuable
information regarding âwhoâ, âwhereâ, âwhenâ and âwhatâ, these real-time human
sensor data promise new research opportunities to uncover models of user behavior, mobility,
and information sharing. These real-time dynamics in social systems usually come
in multiple aspects, which are able to help better understand the social interactions of the
underlying network. However, these multi-aspect datasets are often raw and incomplete
owing to various unpredictable or unavoidable reasons; for instance, API limitations and
data sampling policies can lead to an incomplete (and often biased) perspective on these
multi-aspect datasets. This missing data could raise serious concerns such as biased estimations
on structural properties of the network and properties of information cascades in
social networks. In order to recover missing values or information in social systems, we
identify â4Sâ challenges: extreme sparsity of the observed multi-aspect datasets, adoption
of rich side information that is able to describe the similarities of entities, generation of
robust models rather than limiting them on specific applications, and scalability of models
to handle real large-scale datasets (billions of observed entries). With these challenges
in mind, this dissertation aims to develop scalable and interpretable tensor-based frameworks,
algorithms and methods for recovering missing information on social media. In
particular, this dissertation research makes four unique contributions:
_ The first research contribution of this dissertation research is to propose a scalable
framework based on low-rank tensor learning in the presence of incomplete information.
Concretely, we formally define the problem of recovering the spatio-temporal dynamics of online memes and tackle this problem by proposing a novel tensor-based
factorization approach based on the alternative direction method of multipliers
(ADMM) with the integration of the latent relationships derived from contextual
information among locations, memes, and times.
_ The second research contribution of this dissertation research is to evaluate the generalization
of the proposed tensor learning framework and extend it to the recommendation
problem. In particular, we develop a novel tensor-based approach to
solve the personalized expert recommendation by integrating both the latent relationships
between homogeneous entities (e.g., users and users, experts and experts)
and the relationships between heterogeneous entities (e.g., users and experts, topics
and experts) from the geo-spatial, topical, and social contexts.
_ The third research contribution of this dissertation research is to extend the proposed
tensor learning framework to the user topical profiling problem. Specifically,
we propose a tensor-based contextual regularization model embedded into a matrix
factorization framework, which leverages the social, textual, and behavioral contexts
across users, in order to overcome identified challenges.
_ The fourth research contribution of this dissertation research is to scale up the proposed
tensor learning framework to be capable of handling real large-scale datasets
that are too big to fit in the main memory of a single machine. Particularly, we
propose a novel distributed tensor completion algorithm with the trace-based regularization
of the auxiliary information based on ADMM under the proposed tensor
learning framework, which is designed to scale up to real large-scale tensors (e.g.,
billions of entries) by efficiently computing auxiliary variables, minimizing intermediate
data, and reducing the workload of updating new tensors
Personalized Expert Recommendation: Models and Algorithms
Many large-scale information sharing systems including social media systems, questionanswering
sites and rating and reviewing applications have been growing rapidly, allowing
millions of human participants to generate and consume information on an unprecedented
scale. To manage the sheer growth of information generation, there comes the need to enable
personalization of information resources for users â to surface high-quality content
and feeds, to provide personally relevant suggestions, and so on. A fundamental task in
creating and supporting user-centered personalization systems is to build rich user profile
to aid recommendation for better user experience.
Therefore, in this dissertation research, we propose models and algorithms to facilitate
the creation of new crowd-powered personalized information sharing systems. Specifically,
we first give a principled framework to enable personalization of resources so that
information seekers can be matched with customized knowledgeable users based on their
previous historical actions and contextual information; We then focus on creating rich
user models that allows accurate and comprehensive modeling of user profiles for long
tail users, including discovering userâs known-for profile, userâs opinion bias and userâs
geo-topic profile. In particular, this dissertation research makes two unique contributions:
First, we introduce the problem of personalized expert recommendation and propose
the first principled framework for addressing this problem. To overcome the sparsity issue,
we investigate the use of userâs contextual information that can be exploited to build robust
models of personal expertise, study how spatial preference for personally-valuable expertise
varies across regions, across topics and based on different underlying social communities,
and integrate these different forms of preferences into a matrix factorization-based
personalized expert recommender.
Second, to support the personalized recommendation on experts, we focus on modeling
and inferring user profiles in online information sharing systems. In order to tap
the knowledge of most majority of users, we provide frameworks and algorithms to accurately
and comprehensively create user models by discovering userâs known-for profile,
userâs opinion bias and userâs geo-topic profile, with each described shortly as follows:
âWe develop a probabilistic model called Bayesian Contextual Poisson Factorization
to discover what users are known for by others. Our model considers as input a small fraction
of users whose known-for profiles are already known and the vast majority of users for
whom we have little (or no) information, learns the implicit relationships between user?s
known-for profiles and their contextual signals, and finally predict known-for profiles for
those majority of users.
âWe explore userâs topic-sensitive opinion bias, propose a lightweight semi-supervised
system called âBiasWatchâ to semi-automatically infer the opinion bias of long-tail users,
and demonstrate how userâs opinion bias can be exploited to recommend other users with
similar opinion in social networks.
â We study how a userâs topical profile varies geo-spatially and how we can model
a userâs geo-spatial known-for profile as the last step in our dissertation for creation of
rich user profile. We propose a multi-layered Bayesian hierarchical user factorization to
overcome user heterogeneity and an enhanced model to alleviate the sparsity issue by integrating
user contexts into the two-layered hierarchical user model for better representation
of userâs geo-topic preference by others
Extending CRM in the Retail Industry: An RFID-Based Personal Shopping Assistant System
This paper describes the research and development of a radio frequency identification (RFID)-based personal shopping assistant (PSA) system for retail stores. RFID technology was employed as the key enabler to build a PSA system to optimize operational efficiency and deliver a superior customer shopping experience in retail stores. We show that an RFID-based PSA system can deliver significant results to improve the customer shopping experience and retail store operational efficiency, by increasing customer convenience, providing flexibility in service delivery, enhancing promotional campaign efficiency, and increasing product cross selling and upselling through a customer relationship management (CRM) system. In this study, an RFID value grid for retail stores is proposed that allows managers to use RFID technology in stores to add value to the shopping experience of their customers. Four propositions are presented as the research agenda for examining the ability of RFID technology to improve the operations management of retail stores
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