3,424 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
Extracting Implicit Social Relation for Social Recommendation Techniques in User Rating Prediction
Recommendation plays an increasingly important role in our daily lives.
Recommender systems automatically suggest items to users that might be
interesting for them. Recent studies illustrate that incorporating social trust
in Matrix Factorization methods demonstrably improves accuracy of rating
prediction. Such approaches mainly use the trust scores explicitly expressed by
users. However, it is often challenging to have users provide explicit trust
scores of each other. There exist quite a few works, which propose Trust
Metrics to compute and predict trust scores between users based on their
interactions. In this paper, first we present how social relation can be
extracted from users' ratings to items by describing Hellinger distance between
users in recommender systems. Then, we propose to incorporate the predicted
trust scores into social matrix factorization models. By analyzing social
relation extraction from three well-known real-world datasets, which both:
trust and recommendation data available, we conclude that using the implicit
social relation in social recommendation techniques has almost the same
performance compared to the actual trust scores explicitly expressed by users.
Hence, we build our method, called Hell-TrustSVD, on top of the
state-of-the-art social recommendation technique to incorporate both the
extracted implicit social relations and ratings given by users on the
prediction of items for an active user. To the best of our knowledge, this is
the first work to extend TrustSVD with extracted social trust information. The
experimental results support the idea of employing implicit trust into matrix
factorization whenever explicit trust is not available, can perform much better
than the state-of-the-art approaches in user rating prediction
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