4,515 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
Overcoming data sparsity
Unilever is currently designing and testing recommendation algorithms that would make recommendations about products to online customers given the customer ID and the current content of their basket. Unilever collected a large amount of purchasing data that demonstrates that most of the items (around 80%) are purchased infrequently and account for 20% of the data while frequently purchased items account for 80% of the data. Therefore, the data is sparse, skewed and demonstrates a long tail. Attempts to incorporate the data from the long tail, so far have proved difficult and current Unilever recommendation systems do not incorporate the information about infrequently purchased items. At the same time, these items are more indicative of customers' preferences and Unilever would like to make recommendations from/about these items, i.e. give a rank ordering of available products in real time.
Study Group suggested to use the approach of bipartite networks to construct a similarity matrix that would allow the recommendation scores for different products to be computed. Given a current basket and a customer ID, this approach gives recommendation scores for each available item and recommends the item with the highest score that is not already in the basket. The similarity matrix can be computed offline, while recommendation score calculations can be performed live. This report contains the summary of Study Group findings together with the insights into properties of the similarity matrix and other related issues, such as recommendation for the data collection
On Sampling Strategies for Neural Network-based Collaborative Filtering
Recent advances in neural networks have inspired people to design hybrid
recommendation algorithms that can incorporate both (1) user-item interaction
information and (2) content information including image, audio, and text.
Despite their promising results, neural network-based recommendation algorithms
pose extensive computational costs, making it challenging to scale and improve
upon. In this paper, we propose a general neural network-based recommendation
framework, which subsumes several existing state-of-the-art recommendation
algorithms, and address the efficiency issue by investigating sampling
strategies in the stochastic gradient descent training for the framework. We
tackle this issue by first establishing a connection between the loss functions
and the user-item interaction bipartite graph, where the loss function terms
are defined on links while major computation burdens are located at nodes. We
call this type of loss functions "graph-based" loss functions, for which varied
mini-batch sampling strategies can have different computational costs. Based on
the insight, three novel sampling strategies are proposed, which can
significantly improve the training efficiency of the proposed framework (up to
times speedup in our experiments), as well as improving the
recommendation performance. Theoretical analysis is also provided for both the
computational cost and the convergence. We believe the study of sampling
strategies have further implications on general graph-based loss functions, and
would also enable more research under the neural network-based recommendation
framework.Comment: This is a longer version (with supplementary attached) of the KDD'17
pape
Entropy-based randomisation of rating networks
In the last years, due to the great diffusion of e-commerce, online rating
platforms quickly became a common tool for purchase recommendations. However,
instruments for their analysis did not evolve at the same speed. Indeed,
interesting information about users' habits and tastes can be recovered just
considering the bipartite network of users and products, in which links have
different weights due to the score assigned to items. With respect to other
weighted bipartite networks, in these systems we observe a maximum possible
weight per link, that limits the variability of the outcomes. In the present
article we propose an entropy-based randomisation of (bipartite) rating
networks by extending the Configuration Model framework: the randomised network
satisfies the constraints of the degree per rating, i.e. the number of given
ratings received by the specified product or assigned by the single user. We
first show that such a null model is able to reproduce several non-trivial
features of the real network better than other null models. Then, using it as a
benchmark, we project the information contained in the real system on one of
the layers, showing, for instance, the division in communities of music albums
due to the taste of customers, or, in movies due the audience.Comment: 12 pages, 30 figure
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