2,114 research outputs found
The Limits of Popularity-Based Recommendations, and the Role of Social Ties
In this paper we introduce a mathematical model that captures some of the
salient features of recommender systems that are based on popularity and that
try to exploit social ties among the users. We show that, under very general
conditions, the market always converges to a steady state, for which we are
able to give an explicit form. Thanks to this we can tell rather precisely how
much a market is altered by a recommendation system, and determine the power of
users to influence others. Our theoretical results are complemented by
experiments with real world social networks showing that social graphs prevent
large market distortions in spite of the presence of highly influential users.Comment: 10 pages, 9 figures, KDD 201
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
UNICON: A unified framework for behavior-based consumer segmentation in e-commerce
Data-driven personalization is a key practice in fashion e-commerce,
improving the way businesses serve their consumers needs with more relevant
content. While hyper-personalization offers highly targeted experiences to each
consumer, it requires a significant amount of private data to create an
individualized journey. To alleviate this, group-based personalization provides
a moderate level of personalization built on broader common preferences of a
consumer segment, while still being able to personalize the results. We
introduce UNICON, a unified deep learning consumer segmentation framework that
leverages rich consumer behavior data to learn long-term latent representations
and utilizes them to extract two pivotal types of segmentation catering various
personalization use-cases: lookalike, expanding a predefined target seed
segment with consumers of similar behavior, and data-driven, revealing
non-obvious consumer segments with similar affinities. We demonstrate through
extensive experimentation our framework effectiveness in fashion to identify
lookalike Designer audience and data-driven style segments. Furthermore, we
present experiments that showcase how segment information can be incorporated
in a hybrid recommender system combining hyper and group-based personalization
to exploit the advantages of both alternatives and provide improvements on
consumer experience
A doctor recommender system based on collaborative and content filtering
The volume of healthcare information available on the internet has exploded in recent years. Nowadays, many online healthcare platforms provide patients with detailed information about doctors. However, one of the most important challenges of such platforms is the lack of personalized services for supporting patients in selecting the best-suited doctors. In particular, it becomes extremely time-consuming and difficult for patients to search through all the available doctors. Recommender systems provide a solution to this problem by helping patients gain access to accommodating personalized services, specifically, finding doctors who match their preferences and needs. This paper proposes a hybrid content-based multi-criteria collaborative filtering approach for helping patients find the best-suited doctors who meet their preferences accurately. The proposed approach exploits multi-criteria decision making, doctor reputation score, and content information of doctors in order to increase the quality of recommendations and reduce the influence of data sparsity. The experimental results based on a real-world healthcare multi-criteria (MC) rating dataset show that the proposed approach works effectively with regard to predictive accuracy and coverage under extreme levels of sparsity
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