45,142 research outputs found
BanditMF: Multi-Armed Bandit Based Matrix Factorization Recommender System
Multi-armed bandits (MAB) provide a principled online learning approach to
attain the balance between exploration and exploitation.Due to the superior
performance and low feedback learning without the learning to act in multiple
situations, Multi-armed Bandits drawing widespread attention in applications
ranging such as recommender systems. Likewise, within the recommender system,
collaborative filtering (CF) is arguably the earliest and most influential
method in the recommender system. Crucially, new users and an ever-changing
pool of recommended items are the challenges that recommender systems need to
address. For collaborative filtering, the classical method is training the
model offline, then perform the online testing, but this approach can no longer
handle the dynamic changes in user preferences which is the so-called
\textit{cold start}. So how to effectively recommend items to users in the
absence of effective information? To address the aforementioned problems, a
multi-armed bandit based collaborative filtering recommender system has been
proposed, named BanditMF. BanditMF is designed to address two challenges in the
multi-armed bandits algorithm and collaborative filtering: (1) how to solve the
cold start problem for collaborative filtering under the condition of scarcity
of valid information, (2) how to solve the sub-optimal problem of bandit
algorithms in strong social relations domains caused by independently
estimating unknown parameters associated with each user and ignoring
correlations between users.Comment: MSc dissertatio
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
Hete-CF : Social-Based Collaborative Filtering Recommendation using Heterogeneous Relations
The work described here was funded by the National Natural Science Foundation of China (NSFC) under Grant No. 61373051; the National Science and Technology Pillar Program (Grant No.2013BAH07F05), the Key Laboratory for Symbolic Computation and Knowledge Engineering, Ministry of Education, China, and the UK Economic & Social Research Council (ESRC); award reference: ES/M001628/1.Preprin
Second Screen User Profiling and Multi-level Smart Recommendations in the context of Social TVs
In the context of Social TV, the increasing popularity of first and second
screen users, interacting and posting content online, illustrates new business
opportunities and related technical challenges, in order to enrich user
experience on such environments. SAM (Socializing Around Media) project uses
Social Media-connected infrastructure to deal with the aforementioned
challenges, providing intelligent user context management models and mechanisms
capturing social patterns, to apply collaborative filtering techniques and
personalized recommendations towards this direction. This paper presents the
Context Management mechanism of SAM, running in a Social TV environment to
provide smart recommendations for first and second screen content. Work
presented is evaluated using real movie rating dataset found online, to
validate the SAM's approach in terms of effectiveness as well as efficiency.Comment: In: Wu TT., Gennari R., Huang YM., Xie H., Cao Y. (eds) Emerging
Technologies for Education. SETE 201
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