1,032 research outputs found
Message-Passing Inference on a Factor Graph for Collaborative Filtering
This paper introduces a novel message-passing (MP) framework for the
collaborative filtering (CF) problem associated with recommender systems. We
model the movie-rating prediction problem popularized by the Netflix Prize,
using a probabilistic factor graph model and study the model by deriving
generalization error bounds in terms of the training error. Based on the model,
we develop a new MP algorithm, termed IMP, for learning the model. To show
superiority of the IMP algorithm, we compare it with the closely related
expectation-maximization (EM) based algorithm and a number of other matrix
completion algorithms. Our simulation results on Netflix data show that, while
the methods perform similarly with large amounts of data, the IMP algorithm is
superior for small amounts of data. This improves the cold-start problem of the
CF systems in practice. Another advantage of the IMP algorithm is that it can
be analyzed using the technique of density evolution (DE) that was originally
developed for MP decoding of error-correcting codes
Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design
It is well known that collaborative filtering (CF) based recommender systems
provide better modeling of users and items associated with considerable rating
history. The lack of historical ratings results in the user and the item
cold-start problems. The latter is the main focus of this work. Most of the
current literature addresses this problem by integrating content-based
recommendation techniques to model the new item. However, in many cases such
content is not available, and the question arises is whether this problem can
be mitigated using CF techniques only. We formalize this problem as an
optimization problem: given a new item, a pool of available users, and a budget
constraint, select which users to assign with the task of rating the new item
in order to minimize the prediction error of our model. We show that the
objective function is monotone-supermodular, and propose efficient optimal
design based algorithms that attain an approximation to its optimum. Our
findings are verified by an empirical study using the Netflix dataset, where
the proposed algorithms outperform several baselines for the problem at hand.Comment: 11 pages, 2 figure
To Index or Not to Index: Optimizing Exact Maximum Inner Product Search
Exact Maximum Inner Product Search (MIPS) is an important task that is widely
pertinent to recommender systems and high-dimensional similarity search. The
brute-force approach to solving exact MIPS is computationally expensive, thus
spurring recent development of novel indexes and pruning techniques for this
task. In this paper, we show that a hardware-efficient brute-force approach,
blocked matrix multiply (BMM), can outperform the state-of-the-art MIPS solvers
by over an order of magnitude, for some -- but not all -- inputs.
In this paper, we also present a novel MIPS solution, MAXIMUS, that takes
advantage of hardware efficiency and pruning of the search space. Like BMM,
MAXIMUS is faster than other solvers by up to an order of magnitude, but again
only for some inputs. Since no single solution offers the best runtime
performance for all inputs, we introduce a new data-dependent optimizer,
OPTIMUS, that selects online with minimal overhead the best MIPS solver for a
given input. Together, OPTIMUS and MAXIMUS outperform state-of-the-art MIPS
solvers by 3.2 on average, and up to 10.9, on widely studied
MIPS datasets.Comment: 12 pages, 8 figures, 2 table
A Harmonic Extension Approach for Collaborative Ranking
We present a new perspective on graph-based methods for collaborative ranking
for recommender systems. Unlike user-based or item-based methods that compute a
weighted average of ratings given by the nearest neighbors, or low-rank
approximation methods using convex optimization and the nuclear norm, we
formulate matrix completion as a series of semi-supervised learning problems,
and propagate the known ratings to the missing ones on the user-user or
item-item graph globally. The semi-supervised learning problems are expressed
as Laplace-Beltrami equations on a manifold, or namely, harmonic extension, and
can be discretized by a point integral method. We show that our approach does
not impose a low-rank Euclidean subspace on the data points, but instead
minimizes the dimension of the underlying manifold. Our method, named LDM (low
dimensional manifold), turns out to be particularly effective in generating
rankings of items, showing decent computational efficiency and robust ranking
quality compared to state-of-the-art methods
An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise
Collaborative filtering based recommender systems have proven to be extremely
successful in settings where user preference data on items is abundant.
However, collaborative filtering algorithms are hindered by their weakness
against the item cold-start problem and general lack of interpretability.
Ontology-based recommender systems exploit hierarchical organizations of users
and items to enhance browsing, recommendation, and profile construction. While
ontology-based approaches address the shortcomings of their collaborative
filtering counterparts, ontological organizations of items can be difficult to
obtain for items that mostly belong to the same category (e.g., television
series episodes). In this paper, we present an ontology-based recommender
system that integrates the knowledge represented in a large ontology of
literary themes to produce fiction content recommendations. The main novelty of
this work is an ontology-based method for computing similarities between items
and its integration with the classical Item-KNN (K-nearest neighbors)
algorithm. As a study case, we evaluated the proposed method against other
approaches by performing the classical rating prediction task on a collection
of Star Trek television series episodes in an item cold-start scenario. This
transverse evaluation provides insights into the utility of different
information resources and methods for the initial stages of recommender system
development. We found our proposed method to be a convenient alternative to
collaborative filtering approaches for collections of mostly similar items,
particularly when other content-based approaches are not applicable or
otherwise unavailable. Aside from the new methods, this paper contributes a
testbed for future research and an online framework to collaboratively extend
the ontology of literary themes to cover other narrative content.Comment: 25 pages, 6 figures, 5 tables, minor revision
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
Effect of Neighborhood Approximation on Downstream Analytics
Nearest neighbor search algorithms have been successful in finding practically useful solutions to computationally difficult problems. In the nearest neighbor search problem, the brute force approach is often more efficient than other algorithms for high-dimensional spaces. A special case exists for objects represented as sparse vectors, where algorithms take advantage of the fact that an object has a zero value for most features. In general, since exact nearest neighbor search methods suffer from the “curse of dimensionality,” many practitioners use approximate nearest neighbor search algorithms when faced with high dimensionality or large datasets. To a reasonable degree, it is known that relying on approximate nearest neighbors leads to some error in the solutions to the underlying data mining problems the neighbors are used to solve. However, no one has attempted to quantify this error or provide practitioners with guidance in choosing appropriate search methods for their task. In this thesis, we conduct several experiments on recommender systems with a goal to find the degree to which approximate nearest neighbor algorithms are subject to these types of error propagation problems. Additionally, we provide persuasive evidence on the trade-off between search performance and analytics effectiveness. Our experimental evaluation demonstrates that a state-of-the-art approximate nearest neighbor search method (L2KNNGApprox) is not an effective solution in most cases. When tuned to achieve high search recall (80% or higher), it provides a fairly competitive recommendation performance compared to an efficient exact search method but offers no advantage in terms of efficiency (0.1x—1.5x speedup). Low search recall (\u3c60%) leads to poor recommendation performance. Finally, medium recall values (60%—80%) lead to reasonable recommendation performance but are hard to achieve and offer only a modest gain in efficiency (1.5x—2.3x)
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