4,599 research outputs found
Hierarchical Attention Network for Visually-aware Food Recommendation
Food recommender systems play an important role in assisting users to
identify the desired food to eat. Deciding what food to eat is a complex and
multi-faceted process, which is influenced by many factors such as the
ingredients, appearance of the recipe, the user's personal preference on food,
and various contexts like what had been eaten in the past meals. In this work,
we formulate the food recommendation problem as predicting user preference on
recipes based on three key factors that determine a user's choice on food,
namely, 1) the user's (and other users') history; 2) the ingredients of a
recipe; and 3) the descriptive image of a recipe. To address this challenging
problem, we develop a dedicated neural network based solution Hierarchical
Attention based Food Recommendation (HAFR) which is capable of: 1) capturing
the collaborative filtering effect like what similar users tend to eat; 2)
inferring a user's preference at the ingredient level; and 3) learning user
preference from the recipe's visual images. To evaluate our proposed method, we
construct a large-scale dataset consisting of millions of ratings from
AllRecipes.com. Extensive experiments show that our method outperforms several
competing recommender solutions like Factorization Machine and Visual Bayesian
Personalized Ranking with an average improvement of 12%, offering promising
results in predicting user preference for food. Codes and dataset will be
released upon acceptance
A TIERED RECOMMENDER SYSTEM FOR COST-EFFECTIVE CLOUD INSTANCE SELECTION
Cloud computing has greatly impacted the scientific community and the end users. By leveraging cloud computing, small research institutions and undergraduate colleges are able to alleviate costs and achieve research goals without purchasing and maintaining all the hardware and software. In addition, cloud computing allows researchers to access resources as their teams require and allows real-time collaboration with team members across the globe. Nowadays however, users are easily overwhelmed by the wide range of cloud servers and instances. Due to differences between the cloud server platforms and between instances within the platform, users find it difficult to identify the right instance match for their application.
Therefore, we propose the A2Cloud-Hierarchy (A2Cloud-H) framework that recommends Cloud instances to users for high-performance scientific computing. The framework comprises four components: training data collection, supervised learning (SL) module, unsupervised learning (USL) module, and a decision module. The training database comprise testing traces of previous application and Cloud instances; these are contributed by the scientific community. The SL module contains three popular supervised learning modules: logistic regression, support vector machine and random forest, which train using the database to qualitatively assess the instance performance for the target application. The USL module includes three collaborative filtering methods: application-based, instance-based and rank-based, which use the database to estimate the instances’ performance ratings for the target application. The decision module comprises multiple tiers of analytic hierarchy processing, which consolidate the instance recommendation from the SL and USL modules into a final instance recommendation.
The model is trained and validated by 8 real-world applications on 20 Cloud instances, yielding more than 90% modeling accuracy. The recommendation and integration method proposed in this thesis can help promote a better cloud computing environment for both end-users and cloud server platforms
Collaborative-demographic hybrid for financial: product recommendation
Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsDue to the increased availability of mature data mining and analysis technologies supporting CRM
processes, several financial institutions are striving to leverage customer data and integrate insights
regarding customer behaviour, needs, and preferences into their marketing approach. As decision
support systems assisting marketing and commercial efforts, Recommender Systems applied to the
financial domain have been gaining increased attention. This thesis studies a Collaborative-
Demographic Hybrid Recommendation System, applied to the financial services sector, based on real
data provided by a Portuguese private commercial bank. This work establishes a framework to support
account managers’ advice on which financial product is most suitable for each of the bank’s corporate
clients. The recommendation problem is further developed by conducting a performance comparison
for both multi-output regression and multiclass classification prediction approaches. Experimental
results indicate that multiclass architectures are better suited for the prediction task, outperforming
alternative multi-output regression models on the evaluation metrics considered. Withal, multiclass
Feed-Forward Neural Networks, combined with Recursive Feature Elimination, is identified as the topperforming
algorithm, yielding a 10-fold cross-validated F1 Measure of 83.16%, and achieving
corresponding values of Precision and Recall of 84.34%, and 85.29%, respectively. Overall, this study
provides important contributions for positioning the bank’s commercial efforts around customers’
future requirements. By allowing for a better understanding of customers’ needs and preferences, the
proposed Recommender allows for more personalized and targeted marketing contacts, leading to
higher conversion rates, corporate profitability, and customer satisfaction and loyalty
A Survey of Recommendation Systems and Performance Enhancing Methods
With the development of web services like E-commerce, job hunting websites, movie websites, recommendation system plays a more and more importance role in helping users finding their potential interests among the overloading information. There are a great number of researches available in this field, which leads to various recommendation approaches to choose from when researchers try to implement their recommendation systems.
This paper gives a systematic literature review of recommendation systems where the sources are extracted from Scopus. The research problem to address, similarity metrics used, proposed method and evaluation metrics used are the focus of summary of these papers. In spite of the methodology used in traditional recommendation systems, how additional performance enhancement methods like machine learning methods, matrix factorization techniques and big data tools are applied in several papers are also introduced.
Through reading this paper, researchers are able to understand what are the existing types of recommendation systems, what is the general process of recommendation systems, how the performance enhancement methods can be used to improve the system's performance. Therefore, they can choose a recommendation system which interests them for either implementation or research purpose
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