23,608 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
The reinforcing influence of recommendations on global diversification
Recommender systems are promising ways to filter the overabundant information
in modern society. Their algorithms help individuals to explore decent items,
but it is unclear how they allocate popularity among items. In this paper, we
simulate successive recommendations and measure their influence on the
dispersion of item popularity by Gini coefficient. Our result indicates that
local diffusion and collaborative filtering reinforce the popularity of hot
items, widening the popularity dispersion. On the other hand, the heat
conduction algorithm increases the popularity of the niche items and generates
smaller dispersion of item popularity. Simulations are compared to mean-field
predictions. Our results suggest that recommender systems have reinforcing
influence on global diversification.Comment: 6 pages, 6 figure
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
Neural Attentive Session-based Recommendation
Given e-commerce scenarios that user profiles are invisible, session-based
recommendation is proposed to generate recommendation results from short
sessions. Previous work only considers the user's sequential behavior in the
current session, whereas the user's main purpose in the current session is not
emphasized. In this paper, we propose a novel neural networks framework, i.e.,
Neural Attentive Recommendation Machine (NARM), to tackle this problem.
Specifically, we explore a hybrid encoder with an attention mechanism to model
the user's sequential behavior and capture the user's main purpose in the
current session, which are combined as a unified session representation later.
We then compute the recommendation scores for each candidate item with a
bi-linear matching scheme based on this unified session representation. We
train NARM by jointly learning the item and session representations as well as
their matchings. We carried out extensive experiments on two benchmark
datasets. Our experimental results show that NARM outperforms state-of-the-art
baselines on both datasets. Furthermore, we also find that NARM achieves a
significant improvement on long sessions, which demonstrates its advantages in
modeling the user's sequential behavior and main purpose simultaneously.Comment: Proceedings of the 2017 ACM on Conference on Information and
Knowledge Management. arXiv admin note: text overlap with arXiv:1511.06939,
arXiv:1606.08117 by other author
Herding Effect based Attention for Personalized Time-Sync Video Recommendation
Time-sync comment (TSC) is a new form of user-interaction review associated
with real-time video contents, which contains a user's preferences for videos
and therefore well suited as the data source for video recommendations.
However, existing review-based recommendation methods ignore the
context-dependent (generated by user-interaction), real-time, and
time-sensitive properties of TSC data. To bridge the above gaps, in this paper,
we use video images and users' TSCs to design an Image-Text Fusion model with a
novel Herding Effect Attention mechanism (called ITF-HEA), which can predict
users' favorite videos with model-based collaborative filtering. Specifically,
in the HEA mechanism, we weight the context information based on the semantic
similarities and time intervals between each TSC and its context, thereby
considering influences of the herding effect in the model. Experiments show
that ITF-HEA is on average 3.78\% higher than the state-of-the-art method upon
F1-score in baselines.Comment: ACCEPTED for ORAL presentation at IEEE ICME 201
Personalized Video Recommendation Using Rich Contents from Videos
Video recommendation has become an essential way of helping people explore
the massive videos and discover the ones that may be of interest to them. In
the existing video recommender systems, the models make the recommendations
based on the user-video interactions and single specific content features. When
the specific content features are unavailable, the performance of the existing
models will seriously deteriorate. Inspired by the fact that rich contents
(e.g., text, audio, motion, and so on) exist in videos, in this paper, we
explore how to use these rich contents to overcome the limitations caused by
the unavailability of the specific ones. Specifically, we propose a novel
general framework that incorporates arbitrary single content feature with
user-video interactions, named as collaborative embedding regression (CER)
model, to make effective video recommendation in both in-matrix and
out-of-matrix scenarios. Our extensive experiments on two real-world
large-scale datasets show that CER beats the existing recommender models with
any single content feature and is more time efficient. In addition, we propose
a priority-based late fusion (PRI) method to gain the benefit brought by the
integrating the multiple content features. The corresponding experiment shows
that PRI brings real performance improvement to the baseline and outperforms
the existing fusion methods
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