2,737 research outputs found
Multi-modal Embedding Fusion-based Recommender
Recommendation systems have lately been popularized globally, with primary
use cases in online interaction systems, with significant focus on e-commerce
platforms. We have developed a machine learning-based recommendation platform,
which can be easily applied to almost any items and/or actions domain. Contrary
to existing recommendation systems, our platform supports multiple types of
interaction data with multiple modalities of metadata natively. This is
achieved through multi-modal fusion of various data representations. We
deployed the platform into multiple e-commerce stores of different kinds, e.g.
food and beverages, shoes, fashion items, telecom operators. Here, we present
our system, its flexibility and performance. We also show benchmark results on
open datasets, that significantly outperform state-of-the-art prior work.Comment: 7 pages, 8 figure
Validation of a recommender system for prompting omitted foods in online dietary assessment surveys
Recall assistance methods are among the key aspects that improve the accuracy
of online dietary assessment surveys. These methods still mainly rely on
experience of trained interviewers with nutritional background, but data driven
approaches could improve cost-efficiency and scalability of automated dietary
assessment. We evaluated the effectiveness of a recommender algorithm developed
for an online dietary assessment system called Intake24, that automates the
multiple-pass 24-hour recall method. The recommender builds a model of eating
behavior from recalls collected in past surveys. Based on foods they have
already selected, the model is used to remind respondents of associated foods
that they may have omitted to report. The performance of prompts generated by
the model was compared to that of prompts hand-coded by nutritionists in two
dietary studies. The results of our studies demonstrate that the recommender
system is able to capture a higher number of foods omitted by respondents of
online dietary surveys than prompts hand-coded by nutritionists. However, the
considerably lower precision of generated prompts indicates an opportunity for
further improvement of the system
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
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
- …