6,761 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
Hete-CF: Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations
Collaborative filtering algorithms haven been widely used in recommender
systems. However, they often suffer from the data sparsity and cold start
problems. With the increasing popularity of social media, these problems may be
solved by using social-based recommendation. Social-based recommendation, as an
emerging research area, uses social information to help mitigate the data
sparsity and cold start problems, and it has been demonstrated that the
social-based recommendation algorithms can efficiently improve the
recommendation performance. However, few of the existing algorithms have
considered using multiple types of relations within one social network. In this
paper, we investigate the social-based recommendation algorithms on
heterogeneous social networks and proposed Hete-CF, a Social Collaborative
Filtering algorithm using heterogeneous relations. Distinct from the exiting
methods, Hete-CF can effectively utilize multiple types of relations in a
heterogeneous social network. In addition, Hete-CF is a general approach and
can be used in arbitrary social networks, including event based social
networks, location based social networks, and any other types of heterogeneous
information networks associated with social information. The experimental
results on two real-world data sets, DBLP (a typical heterogeneous information
network) and Meetup (a typical event based social network) show the
effectiveness and efficiency of our algorithm
Trust based collaborative filtering
k-nearest neighbour (kNN) collaborative filtering (CF), the widely successful
algorithm supporting recommender systems, attempts to relieve the problem
of information overload by generating predicted ratings for items users have not
expressed their opinions about; to do so, each predicted rating is computed based
on ratings given by like-minded individuals. Like-mindedness, or similarity-based
recommendation, is the cause of a variety of problems that plague recommender
systems. An alternative view of the problem, based on trust, offers the potential to
address many of the previous limiations in CF. In this work we present a varation of
kNN, the trusted k-nearest recommenders (or kNR) algorithm, which allows users
to learn who and how much to trust one another by evaluating the utility of the rating
information they receive. This method redefines the way CF is performed, and
while avoiding some of the pitfalls that similarity-based CF is prone to, outperforms
the basic similarity-based methods in terms of prediction accuracy
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
Knowledge-aware Complementary Product Representation Learning
Learning product representations that reflect complementary relationship
plays a central role in e-commerce recommender system. In the absence of the
product relationships graph, which existing methods rely on, there is a need to
detect the complementary relationships directly from noisy and sparse customer
purchase activities. Furthermore, unlike simple relationships such as
similarity, complementariness is asymmetric and non-transitive. Standard usage
of representation learning emphasizes on only one set of embedding, which is
problematic for modelling such properties of complementariness. We propose
using knowledge-aware learning with dual product embedding to solve the above
challenges. We encode contextual knowledge into product representation by
multi-task learning, to alleviate the sparsity issue. By explicitly modelling
with user bias terms, we separate the noise of customer-specific preferences
from the complementariness. Furthermore, we adopt the dual embedding framework
to capture the intrinsic properties of complementariness and provide geometric
interpretation motivated by the classic separating hyperplane theory. Finally,
we propose a Bayesian network structure that unifies all the components, which
also concludes several popular models as special cases. The proposed method
compares favourably to state-of-art methods, in downstream classification and
recommendation tasks. We also develop an implementation that scales efficiently
to a dataset with millions of items and customers
Trust- and Distrust-Based Recommendations for Controversial Reviews
Recommender systems that incorporate a social trust network among their users have the potential to make more personalized recommendations compared to traditional collaborative filtering systems, provided they succeed in utilizing the additional trust and distrust information to their advantage. We compare the performance of several well-known trust-enhanced techniques for recommending controversial reviews from Epinions.com, and provide the first experimental study of using distrust in the recommendation process
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