1,592 research outputs found
Neural Collaborative Filtering
In recent years, deep neural networks have yielded immense success on speech
recognition, computer vision and natural language processing. However, the
exploration of deep neural networks on recommender systems has received
relatively less scrutiny. In this work, we strive to develop techniques based
on neural networks to tackle the key problem in recommendation -- collaborative
filtering -- on the basis of implicit feedback. Although some recent work has
employed deep learning for recommendation, they primarily used it to model
auxiliary information, such as textual descriptions of items and acoustic
features of musics. When it comes to model the key factor in collaborative
filtering -- the interaction between user and item features, they still
resorted to matrix factorization and applied an inner product on the latent
features of users and items. By replacing the inner product with a neural
architecture that can learn an arbitrary function from data, we present a
general framework named NCF, short for Neural network-based Collaborative
Filtering. NCF is generic and can express and generalize matrix factorization
under its framework. To supercharge NCF modelling with non-linearities, we
propose to leverage a multi-layer perceptron to learn the user-item interaction
function. Extensive experiments on two real-world datasets show significant
improvements of our proposed NCF framework over the state-of-the-art methods.
Empirical evidence shows that using deeper layers of neural networks offers
better recommendation performance.Comment: 10 pages, 7 figure
Multi-Relational Contrastive Learning for Recommendation
Personalized recommender systems play a crucial role in capturing users'
evolving preferences over time to provide accurate and effective
recommendations on various online platforms. However, many recommendation
models rely on a single type of behavior learning, which limits their ability
to represent the complex relationships between users and items in real-life
scenarios. In such situations, users interact with items in multiple ways,
including clicking, tagging as favorite, reviewing, and purchasing. To address
this issue, we propose the Relation-aware Contrastive Learning (RCL) framework,
which effectively models dynamic interaction heterogeneity. The RCL model
incorporates a multi-relational graph encoder that captures short-term
preference heterogeneity while preserving the dedicated relation semantics for
different types of user-item interactions. Moreover, we design a dynamic
cross-relational memory network that enables the RCL model to capture users'
long-term multi-behavior preferences and the underlying evolving cross-type
behavior dependencies over time. To obtain robust and informative user
representations with both commonality and diversity across multi-behavior
interactions, we introduce a multi-relational contrastive learning paradigm
with heterogeneous short- and long-term interest modeling. Our extensive
experimental studies on several real-world datasets demonstrate the superiority
of the RCL recommender system over various state-of-the-art baselines in terms
of recommendation accuracy and effectiveness.Comment: This paper has been published as a full paper at RecSys 202
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
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