3,858 research outputs found
A Distributed and Accountable Approach to Offline Recommender Systems Evaluation
Different software tools have been developed with the purpose of performing
offline evaluations of recommender systems. However, the results obtained with
these tools may be not directly comparable because of subtle differences in the
experimental protocols and metrics. Furthermore, it is difficult to analyze in
the same experimental conditions several algorithms without disclosing their
implementation details. For these reasons, we introduce RecLab, an open source
software for evaluating recommender systems in a distributed fashion. By
relying on consolidated web protocols, we created RESTful APIs for training and
querying recommenders remotely. In this way, it is possible to easily integrate
into the same toolkit algorithms realized with different technologies. In
details, the experimenter can perform an evaluation by simply visiting a web
interface provided by RecLab. The framework will then interact with all the
selected recommenders and it will compute and display a comprehensive set of
measures, each representing a different metric. The results of all experiments
are permanently stored and publicly available in order to support
accountability and comparative analyses.Comment: REVEAL 2018 Workshop on Offline Evaluation for Recommender System
Personality in Computational Advertising: A Benchmark
In the last decade, new ways of shopping online have increased the
possibility of buying products and services more easily and faster
than ever. In this new context, personality is a key determinant
in the decision making of the consumer when shopping. A person’s
buying choices are influenced by psychological factors like
impulsiveness; indeed some consumers may be more susceptible
to making impulse purchases than others. Since affective metadata
are more closely related to the user’s experience than generic
parameters, accurate predictions reveal important aspects of user’s
attitudes, social life, including attitude of others and social identity.
This work proposes a highly innovative research that uses a personality
perspective to determine the unique associations among the
consumer’s buying tendency and advert recommendations. In fact,
the lack of a publicly available benchmark for computational advertising
do not allow both the exploration of this intriguing research
direction and the evaluation of recent algorithms. We present the
ADS Dataset, a publicly available benchmark consisting of 300 real
advertisements (i.e., Rich Media Ads, Image Ads, Text Ads) rated
by 120 unacquainted individuals, enriched with Big-Five users’
personality factors and 1,200 personal users’ pictures
Image-based Recommendations on Styles and Substitutes
Humans inevitably develop a sense of the relationships between objects, some
of which are based on their appearance. Some pairs of objects might be seen as
being alternatives to each other (such as two pairs of jeans), while others may
be seen as being complementary (such as a pair of jeans and a matching shirt).
This information guides many of the choices that people make, from buying
clothes to their interactions with each other. We seek here to model this human
sense of the relationships between objects based on their appearance. Our
approach is not based on fine-grained modeling of user annotations but rather
on capturing the largest dataset possible and developing a scalable method for
uncovering human notions of the visual relationships within. We cast this as a
network inference problem defined on graphs of related images, and provide a
large-scale dataset for the training and evaluation of the same. The system we
develop is capable of recommending which clothes and accessories will go well
together (and which will not), amongst a host of other applications.Comment: 11 pages, 10 figures, SIGIR 201
A Process Framework for Semantics-aware Tourism Information Systems
The growing sophistication of user requirements in tourism due to the advent of new technologies such as the Semantic Web and mobile computing has imposed new possibilities for improved intelligence in Tourism Information Systems (TIS). Traditional software engineering and web engineering approaches cannot suffice, hence the need to find new product development approaches that would sufficiently enable the next generation of TIS. The next generation of TIS are expected among other things to: enable
semantics-based information processing, exhibit natural language capabilities, facilitate inter-organization exchange of information in a seamless way, and
evolve proactively in tandem with dynamic user requirements. In this paper, a product development approach called Product Line for Ontology-based Semantics-Aware Tourism Information Systems (PLOSATIS) which is a novel
hybridization of software product line engineering, and Semantic Web engineering concepts is proposed. PLOSATIS is presented as potentially effective, predictable and amenable to software process improvement initiatives
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