181,347 research outputs found
Emergence of scale-free leadership structure in social recommender systems
The study of the organization of social networks is important for
understanding of opinion formation, rumor spreading, and the emergence of
trends and fashion. This paper reports empirical analysis of networks extracted
from four leading sites with social functionality (Delicious, Flickr, Twitter
and YouTube) and shows that they all display a scale-free leadership structure.
To reproduce this feature, we propose an adaptive network model driven by
social recommending. Artificial agent-based simulations of this model highlight
a "good get richer" mechanism where users with broad interests and good
judgments are likely to become popular leaders for the others. Simulations also
indicate that the studied social recommendation mechanism can gradually improve
the user experience by adapting to tastes of its users. Finally we outline
implications for real online resource-sharing systems
What attracts vehicle consumersâ buying:A Saaty scale-based VIKOR (SSC-VIKOR) approach from after-sales textual perspective?
Purpose:
The increasingly booming e-commerce development has stimulated vehicle consumers to express individual reviews through online forum. The purpose of this paper is to probe into the vehicle consumer consumption behavior and make recommendations for potential consumers from textual comments viewpoint.
Design/methodology/approach:
A big data analytic-based approach is designed to discover vehicle consumer consumption behavior from online perspective. To reduce subjectivity of expert-based approaches, a parallel NaĂŻve Bayes approach is designed to analyze the sentiment analysis, and the Saaty scale-based (SSC) scoring rule is employed to obtain specific sentimental value of attribute class, contributing to the multi-grade sentiment classification. To achieve the intelligent recommendation for potential vehicle customers, a novel SSC-VIKOR approach is developed to prioritize vehicle brand candidates from a big data analytical viewpoint.
Findings:
The big data analytics argue that âcost-effectivenessâ characteristic is the most important factor that vehicle consumers care, and the data mining results enable automakers to better understand consumer consumption behavior.
Research limitations/implications:
The case study illustrates the effectiveness of the integrated method, contributing to much more precise operations management on marketing strategy, quality improvement and intelligent recommendation.
Originality/value:
Researches of consumer consumption behavior are usually based on survey-based methods, and mostly previous studies about comments analysis focus on binary analysis. The hybrid SSC-VIKOR approach is developed to fill the gap from the big data perspective
Learning Tree-based Deep Model for Recommender Systems
Model-based methods for recommender systems have been studied extensively in
recent years. In systems with large corpus, however, the calculation cost for
the learnt model to predict all user-item preferences is tremendous, which
makes full corpus retrieval extremely difficult. To overcome the calculation
barriers, models such as matrix factorization resort to inner product form
(i.e., model user-item preference as the inner product of user, item latent
factors) and indexes to facilitate efficient approximate k-nearest neighbor
searches. However, it still remains challenging to incorporate more expressive
interaction forms between user and item features, e.g., interactions through
deep neural networks, because of the calculation cost.
In this paper, we focus on the problem of introducing arbitrary advanced
models to recommender systems with large corpus. We propose a novel tree-based
method which can provide logarithmic complexity w.r.t. corpus size even with
more expressive models such as deep neural networks. Our main idea is to
predict user interests from coarse to fine by traversing tree nodes in a
top-down fashion and making decisions for each user-node pair. We also show
that the tree structure can be jointly learnt towards better compatibility with
users' interest distribution and hence facilitate both training and prediction.
Experimental evaluations with two large-scale real-world datasets show that the
proposed method significantly outperforms traditional methods. Online A/B test
results in Taobao display advertising platform also demonstrate the
effectiveness of the proposed method in production environments.Comment: Accepted by KDD 201
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
Unbiased Offline Evaluation of Contextual-bandit-based News Article Recommendation Algorithms
Contextual bandit algorithms have become popular for online recommendation
systems such as Digg, Yahoo! Buzz, and news recommendation in general.
\emph{Offline} evaluation of the effectiveness of new algorithms in these
applications is critical for protecting online user experiences but very
challenging due to their "partial-label" nature. Common practice is to create a
simulator which simulates the online environment for the problem at hand and
then run an algorithm against this simulator. However, creating simulator
itself is often difficult and modeling bias is usually unavoidably introduced.
In this paper, we introduce a \emph{replay} methodology for contextual bandit
algorithm evaluation. Different from simulator-based approaches, our method is
completely data-driven and very easy to adapt to different applications. More
importantly, our method can provide provably unbiased evaluations. Our
empirical results on a large-scale news article recommendation dataset
collected from Yahoo! Front Page conform well with our theoretical results.
Furthermore, comparisons between our offline replay and online bucket
evaluation of several contextual bandit algorithms show accuracy and
effectiveness of our offline evaluation method.Comment: 10 pages, 7 figures, revised from the published version at the WSDM
2011 conferenc
Emergence, Evolution and Scaling of Online Social Networks
This work was partially supported by AFOSR under Grant No. FA9550-10-1-0083, NSF under Grant No. CDI-1026710, NSF of China under Grants Nos. 61473060 and 11275003, and NBRPC under Grant No. 2010CB731403. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD
Who are Like-minded: Mining User Interest Similarity in Online Social Networks
In this paper, we mine and learn to predict how similar a pair of users'
interests towards videos are, based on demographic (age, gender and location)
and social (friendship, interaction and group membership) information of these
users. We use the video access patterns of active users as ground truth (a form
of benchmark). We adopt tag-based user profiling to establish this ground
truth, and justify why it is used instead of video-based methods, or many
latent topic models such as LDA and Collaborative Filtering approaches. We then
show the effectiveness of the different demographic and social features, and
their combinations and derivatives, in predicting user interest similarity,
based on different machine-learning methods for combining multiple features. We
propose a hybrid tree-encoded linear model for combining the features, and show
that it out-performs other linear and treebased models. Our methods can be used
to predict user interest similarity when the ground-truth is not available,
e.g. for new users, or inactive users whose interests may have changed from old
access data, and is useful for video recommendation. Our study is based on a
rich dataset from Tencent, a popular service provider of social networks, video
services, and various other services in China
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