65,721 research outputs found
Information Filtering on Coupled Social Networks
In this paper, based on the coupled social networks (CSN), we propose a
hybrid algorithm to nonlinearly integrate both social and behavior information
of online users. Filtering algorithm based on the coupled social networks,
which considers the effects of both social influence and personalized
preference. Experimental results on two real datasets, \emph{Epinions} and
\emph{Friendfeed}, show that hybrid pattern can not only provide more accurate
recommendations, but also can enlarge the recommendation coverage while
adopting global metric. Further empirical analyses demonstrate that the mutual
reinforcement and rich-club phenomenon can also be found in coupled social
networks where the identical individuals occupy the core position of the online
system. This work may shed some light on the in-depth understanding structure
and function of coupled social networks
Current Challenges and Visions in Music Recommender Systems Research
Music recommender systems (MRS) have experienced a boom in recent years,
thanks to the emergence and success of online streaming services, which
nowadays make available almost all music in the world at the user's fingertip.
While today's MRS considerably help users to find interesting music in these
huge catalogs, MRS research is still facing substantial challenges. In
particular when it comes to build, incorporate, and evaluate recommendation
strategies that integrate information beyond simple user--item interactions or
content-based descriptors, but dig deep into the very essence of listener
needs, preferences, and intentions, MRS research becomes a big endeavor and
related publications quite sparse.
The purpose of this trends and survey article is twofold. We first identify
and shed light on what we believe are the most pressing challenges MRS research
is facing, from both academic and industry perspectives. We review the state of
the art towards solving these challenges and discuss its limitations. Second,
we detail possible future directions and visions we contemplate for the further
evolution of the field. The article should therefore serve two purposes: giving
the interested reader an overview of current challenges in MRS research and
providing guidance for young researchers by identifying interesting, yet
under-researched, directions in the field
How Algorithmic Confounding in Recommendation Systems Increases Homogeneity and Decreases Utility
Recommendation systems are ubiquitous and impact many domains; they have the
potential to influence product consumption, individuals' perceptions of the
world, and life-altering decisions. These systems are often evaluated or
trained with data from users already exposed to algorithmic recommendations;
this creates a pernicious feedback loop. Using simulations, we demonstrate how
using data confounded in this way homogenizes user behavior without increasing
utility
Recurrent Poisson Factorization for Temporal Recommendation
Poisson factorization is a probabilistic model of users and items for
recommendation systems, where the so-called implicit consumer data is modeled
by a factorized Poisson distribution. There are many variants of Poisson
factorization methods who show state-of-the-art performance on real-world
recommendation tasks. However, most of them do not explicitly take into account
the temporal behavior and the recurrent activities of users which is essential
to recommend the right item to the right user at the right time. In this paper,
we introduce Recurrent Poisson Factorization (RPF) framework that generalizes
the classical PF methods by utilizing a Poisson process for modeling the
implicit feedback. RPF treats time as a natural constituent of the model and
brings to the table a rich family of time-sensitive factorization models. To
elaborate, we instantiate several variants of RPF who are capable of handling
dynamic user preferences and item specification (DRPF), modeling the
social-aspect of product adoption (SRPF), and capturing the consumption
heterogeneity among users and items (HRPF). We also develop a variational
algorithm for approximate posterior inference that scales up to massive data
sets. Furthermore, we demonstrate RPF's superior performance over many
state-of-the-art methods on synthetic dataset, and large scale real-world
datasets on music streaming logs, and user-item interactions in M-Commerce
platforms.Comment: Submitted to KDD 2017 | Halifax, Nova Scotia - Canada - sigkdd, Codes
are available at https://github.com/AHosseini/RP
A Broad Learning Approach for Context-Aware Mobile Application Recommendation
With the rapid development of mobile apps, the availability of a large number
of mobile apps in application stores brings challenge to locate appropriate
apps for users. Providing accurate mobile app recommendation for users becomes
an imperative task. Conventional approaches mainly focus on learning users'
preferences and app features to predict the user-app ratings. However, most of
them did not consider the interactions among the context information of apps.
To address this issue, we propose a broad learning approach for
\textbf{C}ontext-\textbf{A}ware app recommendation with \textbf{T}ensor
\textbf{A}nalysis (CATA). Specifically, we utilize a tensor-based framework to
effectively integrate user's preference, app category information and
multi-view features to facilitate the performance of app rating prediction. The
multidimensional structure is employed to capture the hidden relationships
between multiple app categories with multi-view features. We develop an
efficient factorization method which applies Tucker decomposition to learn the
full-order interactions within multiple categories and features. Furthermore,
we employ a group norm regularization to learn the group-wise
feature importance of each view with respect to each app category. Experiments
on two real-world mobile app datasets demonstrate the effectiveness of the
proposed method
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
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