3 research outputs found
Boosting Item-based Collaborative Filtering via Nearly Uncoupled Random Walks
Item-based models are among the most popular collaborative filtering
approaches for building recommender systems. Random walks can provide a
powerful tool for harvesting the rich network of interactions captured within
these models. They can exploit indirect relations between the items, mitigate
the effects of sparsity, ensure wider itemspace coverage, as well as increase
the diversity of recommendation lists. Their potential, however, can be
hindered by the tendency of the walks to rapidly concentrate towards the
central nodes of the graph, thereby significantly restricting the range of
K-step distributions that can be exploited for personalized recommendations. In
this work we introduce RecWalk; a novel random walk-based method that leverages
the spectral properties of nearly uncoupled Markov chains to provably lift this
limitation and prolong the influence of users' past preferences on the
successive steps of the walk---allowing the walker to explore the underlying
network more fruitfully. A comprehensive set of experiments on real-world
datasets verify the theoretically predicted properties of the proposed approach
and indicate that they are directly linked to significant improvements in top-n
recommendation accuracy. They also highlight RecWalk's potential in providing a
framework for boosting the performance of item-based models. RecWalk achieves
state-of-the-art top-n recommendation quality outperforming several competing
approaches, including recently proposed methods that rely on deep neural
networks.Comment: 26 pages, complete version of the RecWalk conference paper that
appeared in ACM WSDM 201
LLFR: A Lanczos-Based Latent Factor Recommender for Big Data Scenarios
The purpose if this master's thesis is to study and develop a new algorithmic
framework for Collaborative Filtering to produce recommendations in the top-N
recommendation problem. Thus, we propose Lanczos Latent Factor Recommender
(LLFR); a novel "big data friendly" collaborative filtering algorithm for top-N
recommendation. Using a computationally efficient Lanczos-based procedure, LLFR
builds a low dimensional item similarity model, that can be readily exploited
to produce personalized ranking vectors over the item space. A number of
experiments on real datasets indicate that LLFR outperforms other
state-of-the-art top-N recommendation methods from a computational as well as a
qualitative perspective. Our experimental results also show that its relative
performance gains, compared to competing methods, increase as the data get
sparser, as in the Cold Start Problem. More specifically, this is true both
when the sparsity is generalized - as in the New Community Problem, a very
common problem faced by real recommender systems in their beginning stages,
when there is not sufficient number of ratings for the collaborative filtering
algorithms to uncover similarities between items or users - and in the very
interesting case where the sparsity is localized in a small fraction of the
dataset - as in the New Users Problem, where new users are introduced to the
system, they have not rated many items and thus, the CF algorithm can not make
reliable personalized recommendations yet.Comment: 65 pages, MSc Thesis (in Greek
Exploiting Hierarchy for Ranking-based Recommendation
The purpose of this master's thesis is to study and develop a new algorithmic
framework for collaborative filtering (CF) to generate recommendations. The
method we propose is based on the exploitation of the hierarchical structure of
the item space and intuitively "stands" on the property of Near Complete
Decomposability (NCD) which is inherent in the structure of the majority of
hierarchical systems. Building on the intuition behind the NCDawareRank
algorithm and its related concept of NCD proximity, we model our system in a
way that illuminates its endemic characteristics and we propose a new
algorithmic framework for recommendations, called HIR. We focus on combining
the direct with the NCD "neighborhoods" of items to achieve better
characterization of the inter-item relations, in order to improve the quality
of recommendations and alleviate sparsity related problems.Comment: 81 pages, M.Sc. Thesis (in Greek), Department of Computer Engineering
and Informatics, University of Patra