1,014 research outputs found
Flavour Enhanced Food Recommendation
We propose a mechanism to use the features of flavour to enhance the quality
of food recommendations. An empirical method to determine the flavour of food
is incorporated into a recommendation engine based on major gustatory nerves.
Such a system has advantages of suggesting food items that the user is more
likely to enjoy based upon matching with their flavour profile through use of
the taste biological domain knowledge. This preliminary intends to spark more
robust mechanisms by which flavour of food is taken into consideration as a
major feature set into food recommendation systems. Our long term vision is to
integrate this with health factors to recommend healthy and tasty food to users
to enhance quality of life.Comment: In Proceedings of 5th International Workshop on Multimedia Assisted
Dietary Management, Nice, France, October 21, 2019, MADiMa 2019, 6 page
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
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