1,580 research outputs found
ACCAMS: Additive Co-Clustering to Approximate Matrices Succinctly
Matrix completion and approximation are popular tools to capture a user's
preferences for recommendation and to approximate missing data. Instead of
using low-rank factorization we take a drastically different approach, based on
the simple insight that an additive model of co-clusterings allows one to
approximate matrices efficiently. This allows us to build a concise model that,
per bit of model learned, significantly beats all factorization approaches to
matrix approximation. Even more surprisingly, we find that summing over small
co-clusterings is more effective in modeling matrices than classic
co-clustering, which uses just one large partitioning of the matrix.
Following Occam's razor principle suggests that the simple structure induced
by our model better captures the latent preferences and decision making
processes present in the real world than classic co-clustering or matrix
factorization. We provide an iterative minimization algorithm, a collapsed
Gibbs sampler, theoretical guarantees for matrix approximation, and excellent
empirical evidence for the efficacy of our approach. We achieve
state-of-the-art results on the Netflix problem with a fraction of the model
complexity.Comment: 22 pages, under review for conference publicatio
On content-based recommendation and user privacy in social-tagging systems
Recommendation systems and content filtering approaches based on annotations and ratings, essentially rely on users expressing their preferences and interests through their actions, in order to provide personalised content. This activity, in which users engage collectively has been named social tagging, and it is one of the most popular in which users engage online, and although it has opened new possibilities for application interoperability on the semantic web, it is also posing new privacy threats. It, in fact, consists of describing online or offline resources by using free-text labels (i.e. tags), therefore exposing the user profile and activity to privacy attacks. Users, as a result, may wish to adopt a privacy-enhancing strategy in order not to reveal their interests completely. Tag forgery is a privacy enhancing technology consisting of generating tags for categories or resources that do not reflect the user's actual preferences. By modifying their profile, tag forgery may have a negative impact on the quality of the recommendation system, thus protecting user privacy to a certain extent but at the expenses of utility loss. The impact of tag forgery on content-based recommendation is, therefore, investigated in a real-world application scenario where different forgery strategies are evaluated, and the consequent loss in utility is measured and compared.Peer ReviewedPostprint (author’s final draft
Comparing Context-Aware Recommender Systems in Terms of Accuracy and Diversity: Which Contextual Modeling, Pre-filtering and Post-Filtering Methods Perform the Best
Although the area of Context-Aware Recommender Systems (CARS) has made a
significant progress over the last several years, the problem of
comparing various contextual pre-filtering, post-filtering and
contextual modeling methods remained fairly unexplored. In this paper,
we address this problem and compare several contextual pre-filtering,
post-filtering and contextual modeling methods in terms of the accuracy
and diversity of their recommendations to determine which methods
outperform the others and under which circumstances. To this end, we
consider three major factors affecting performance of CARS methods, such
as the type of the recommendation task, context granularity and the type
of the recommendation data. We show that none of the considered CARS
methods uniformly dominates the others across all of these factors and
other experimental settings; but that a certain group of contextual
modeling methods constitutes a reliable “best bet” when
choosing a sound CARS approach since they provide a good balance of
accuracy and diversity of contextual recommendations.Politecnico di Bari, Italy; NYU Stern School of Busines
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