896 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
Recommender Systems Based on Deep Learning Techniques
Tese de mestrado em Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2020O atual aumento do número de opções disponíveis aquando a tomada de uma decisão, faz com que vários indivíduos se sintam sobrecarregados, o que origina experiências de utilização frustrantes e demoradas. Sistemas de Recomendação são ferramentas fundamentais para a mitigação deste acontecimento, ao remover certas alternativas que provavelmente serão irrelevantes para cada indivíduo. Desenvolver estes sistemas apresenta vários desafios, tornando-se assim uma tarefa de difícil realização. Para tal, vários sistemas (frameworks) para facilitar estes desenvolvimentos foram propostos, ajudando assim a reduzir os custos de desenvolvimento, através da oferta de ferramentas reutilizáveis, tal como implementações de estratégias comuns e modelos populares. Contudo, ainda é difícil encontrar um sistema (framework) que também ofereça uma abstração completa na conversão de conjuntos de dados, suporte para abordagens baseadas em aprendizagem profunda, modelos extensíveis, e avaliações reproduzíveis. Este trabalho introduz o DRecPy, um novo sistema (framework) que oferece vários módulos para evitar trabalho de desenvolvimento repetitivo, mas também para auxiliar os praticantes nos desafios mencionados anteriormente. O DRecPy contém módulos para lidar com: tarefas de carregar e converter conjuntos de dados; divisão de conjuntos de dados para treino, validação e teste de modelos; amostragem de pontos de dados através de estratégias distintas; criação de sistemas de recomendação complexos e extensíveis, ao seguir uma estrutura de modelo definida mas flexível; juntamente com vários processos de avaliação que originam resultados determinísticos por padrão. Para avaliar este novo sistema (framework), a sua consistência é analisada através da comparação dos resultados produzidos, com os resultados publicados na literatura. Para mostrar que o DRecPy pode ser uma ferramenta valiosa para a comunidade de sistemas de recomendação, várias características são também avaliadas e comparadas com ferramentas existentes, tais como extensibilidade, reutilização e reprodutibilidade.The current increase in available options makes individuals feel overwhelmed whenever facing a decision, resulting in a frustrating and time-consuming user experience. Recommender systems are a fundamental tool to solve this issue, filtering out the options that are most likely to be irrelevant for each person. Developing these systems presents us with a vast number of challenges, making it a difficult task to accomplish. To this end, various frameworks to aid their development have been proposed, helping reducing development costs by offering reusable tools, as well as implementations of common strategies and popular models. However, it is still hard to find a framework that also provides full abstraction over data set conversion, support for deep learning-based approaches, extensible models, and reproducible evaluations. This work introduces DRecPy, a novel framework that not only provides several modules to avoid repetitive development work, but also to assist practitioners with the above challenges. DRecPy contains modules to deal with: data set import and conversion tasks; splitting data sets for model training, validation, and testing; sampling data points using distinct strategies; creating extensible and complex recommenders, by following a defined but flexible model structure; together with many evaluation procedures that provide deterministic results by default. To evaluate this new framework, its consistency is analyzed by comparing the results generated by DRecPy against the results published by others using the same algorithms. Also, to show that DRecPy can be a valuable tool for the recommender systems’ community, several framework characteristics are evaluated and compared against existing tools, such as extensibility, reusability, and reproducibility
Weighted Random Walk Sampling for Multi-Relational Recommendation
In the information overloaded web, personalized recommender systems are
essential tools to help users find most relevant information. The most
heavily-used recommendation frameworks assume user interactions that are
characterized by a single relation. However, for many tasks, such as
recommendation in social networks, user-item interactions must be modeled as a
complex network of multiple relations, not only a single relation. Recently
research on multi-relational factorization and hybrid recommender models has
shown that using extended meta-paths to capture additional information about
both users and items in the network can enhance the accuracy of recommendations
in such networks. Most of this work is focused on unweighted heterogeneous
networks, and to apply these techniques, weighted relations must be simplified
into binary ones. However, information associated with weighted edges, such as
user ratings, which may be crucial for recommendation, are lost in such
binarization. In this paper, we explore a random walk sampling method in which
the frequency of edge sampling is a function of edge weight, and apply this
generate extended meta-paths in weighted heterogeneous networks. With this
sampling technique, we demonstrate improved performance on multiple data sets
both in terms of recommendation accuracy and model generation efficiency
Are We Wasting Time? A Fast, Accurate Performance Evaluation Framework for Knowledge Graph Link Predictors
The standard evaluation protocol for measuring the quality of Knowledge Graph
Completion methods - the task of inferring new links to be added to a graph -
typically involves a step which ranks every entity of a Knowledge Graph to
assess their fit as a head or tail of a candidate link to be added. In
Knowledge Graphs on a larger scale, this task rapidly becomes prohibitively
heavy. Previous approaches mitigate this problem by using random sampling of
entities to assess the quality of links predicted or suggested by a method.
However, we show that this approach has serious limitations since the ranking
metrics produced do not properly reflect true outcomes. In this paper, we
present a thorough analysis of these effects along with the following findings.
First, we empirically find and theoretically motivate why sampling uniformly at
random vastly overestimates the ranking performance of a method. We show that
this can be attributed to the effect of easy versus hard negative candidates.
Second, we propose a framework that uses relational recommenders to guide the
selection of candidates for evaluation. We provide both theoretical and
empirical justification of our methodology, and find that simple and fast
methods can work extremely well, and that they match advanced neural
approaches. Even when a large portion of true candidates for a property are
missed, the estimation barely deteriorates. With our proposed framework, we can
reduce the time and computation needed similar to random sampling strategies
while vastly improving the estimation; on ogbl-wikikg2, we show that accurate
estimations of the full, filtered ranking can be obtained in 20 seconds instead
of 30 minutes. We conclude that considerable computational effort can be saved
by effective preprocessing and sampling methods and still reliably predict
performance accurately of the true performance for the entire ranking
procedure
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