Dietary Behavior Based Food Recommender System Using Deep Learning and Clustering Techniques

Deep learning algorithms have been highly successful in various domains, including the development of collaborative filtering recommender systems. However, one of the challenges associated with deep learning-based collaborative filtering methods is that they require the involvement of all users to construct the latent representation of the input data, which is then utilized to predict the missing ratings of each user. This can be problematic as some users may have different preferences or interests, which may affect the accuracy of the prediction generation process. The research proposed a food recommender system, which tries to find users with similar dietary behavior and involve them in the recommendations generation process by combining clustering technique with denoising autoencoder to generate a rate prediction model. It is applied to “Food.com Recipes and Interactions” dataset. RMSE score was used to evaluate the performance of the proposed model which is 0.1927. It outperformed the other models that used autoencoder and denoising autoencoder without clustering where the RMSE values are 0. 4358 and 0.4354 consequently

Recommendations for item set completion: On the semantics of item co-occurrence with data sparsity, input size, and input modalities

We address the problem of recommending relevant items to a user in order to "complete" a partial set of items already known. We consider the two scenarios of citation and subject label recommendation, which resemble different semantics of item co-occurrence: relatedness for co-citations and diversity for subject labels. We assess the influence of the completeness of an already known partial item set on the recommender performance. We also investigate data sparsity through a pruning parameter and the influence of using additional metadata. As recommender models, we focus on different autoencoders, which are particularly suited for reconstructing missing items in a set. We extend autoencoders to exploit a multi-modal input of text and structured data. Our experiments on six real-world datasets show that supplying the partial item set as input is helpful when item co-occurrence resembles relatedness, while metadata are effective when co-occurrence implies diversity. This outcome means that the semantics of item co-occurrence is an important factor. The simple item co-occurrence model is a strong baseline for citation recommendation. However, autoencoders have the advantage to enable exploiting additional metadata besides the partial item set as input and achieve comparable performance. For the subject label recommendation task, the title is the most important attribute. Adding more input modalities sometimes even harms the result. In conclusion, it is crucial to consider the semantics of the item co-occurrence for the choice of an appropriate recommendation model and carefully decide which metadata to exploit