6,675 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
LambdaOpt: Learn to Regularize Recommender Models in Finer Levels
Recommendation models mainly deal with categorical variables, such as
user/item ID and attributes. Besides the high-cardinality issue, the
interactions among such categorical variables are usually long-tailed, with the
head made up of highly frequent values and a long tail of rare ones. This
phenomenon results in the data sparsity issue, making it essential to
regularize the models to ensure generalization. The common practice is to
employ grid search to manually tune regularization hyperparameters based on the
validation data. However, it requires non-trivial efforts and large computation
resources to search the whole candidate space; even so, it may not lead to the
optimal choice, for which different parameters should have different
regularization strengths. In this paper, we propose a hyperparameter
optimization method, LambdaOpt, which automatically and adaptively enforces
regularization during training. Specifically, it updates the regularization
coefficients based on the performance of validation data. With LambdaOpt, the
notorious tuning of regularization hyperparameters can be avoided; more
importantly, it allows fine-grained regularization (i.e. each parameter can
have an individualized regularization coefficient), leading to better
generalized models. We show how to employ LambdaOpt on matrix factorization, a
classical model that is representative of a large family of recommender models.
Extensive experiments on two public benchmarks demonstrate the superiority of
our method in boosting the performance of top-K recommendation.Comment: Accepted by KDD 201
Current Challenges and Visions in Music Recommender Systems Research
Music recommender systems (MRS) have experienced a boom in recent years,
thanks to the emergence and success of online streaming services, which
nowadays make available almost all music in the world at the user's fingertip.
While today's MRS considerably help users to find interesting music in these
huge catalogs, MRS research is still facing substantial challenges. In
particular when it comes to build, incorporate, and evaluate recommendation
strategies that integrate information beyond simple user--item interactions or
content-based descriptors, but dig deep into the very essence of listener
needs, preferences, and intentions, MRS research becomes a big endeavor and
related publications quite sparse.
The purpose of this trends and survey article is twofold. We first identify
and shed light on what we believe are the most pressing challenges MRS research
is facing, from both academic and industry perspectives. We review the state of
the art towards solving these challenges and discuss its limitations. Second,
we detail possible future directions and visions we contemplate for the further
evolution of the field. The article should therefore serve two purposes: giving
the interested reader an overview of current challenges in MRS research and
providing guidance for young researchers by identifying interesting, yet
under-researched, directions in the field
LambdaFM: Learning Optimal Ranking with Factorization Machines Using Lambda Surrogates
State-of-the-art item recommendation algorithms, which apply
Factorization Machines (FM) as a scoring function and
pairwise ranking loss as a trainer (PRFM for short), have
been recently investigated for the implicit feedback based
context-aware recommendation problem (IFCAR). However,
good recommenders particularly emphasize on the accuracy
near the top of the ranked list, and typical pairwise loss functions
might not match well with such a requirement. In this
paper, we demonstrate, both theoretically and empirically,
PRFM models usually lead to non-optimal item recommendation
results due to such a mismatch. Inspired by the success
of LambdaRank, we introduce Lambda Factorization
Machines (LambdaFM), which is particularly intended for
optimizing ranking performance for IFCAR. We also point
out that the original lambda function suffers from the issue
of expensive computational complexity in such settings due
to a large amount of unobserved feedback. Hence, instead
of directly adopting the original lambda strategy, we create
three effective lambda surrogates by conducting a theoretical
analysis for lambda from the top-N optimization perspective.
Further, we prove that the proposed lambda surrogates
are generic and applicable to a large set of pairwise
ranking loss functions. Experimental results demonstrate
LambdaFM significantly outperforms state-of-the-art algorithms
on three real-world datasets in terms of four standard
ranking measures
Who are Like-minded: Mining User Interest Similarity in Online Social Networks
In this paper, we mine and learn to predict how similar a pair of users'
interests towards videos are, based on demographic (age, gender and location)
and social (friendship, interaction and group membership) information of these
users. We use the video access patterns of active users as ground truth (a form
of benchmark). We adopt tag-based user profiling to establish this ground
truth, and justify why it is used instead of video-based methods, or many
latent topic models such as LDA and Collaborative Filtering approaches. We then
show the effectiveness of the different demographic and social features, and
their combinations and derivatives, in predicting user interest similarity,
based on different machine-learning methods for combining multiple features. We
propose a hybrid tree-encoded linear model for combining the features, and show
that it out-performs other linear and treebased models. Our methods can be used
to predict user interest similarity when the ground-truth is not available,
e.g. for new users, or inactive users whose interests may have changed from old
access data, and is useful for video recommendation. Our study is based on a
rich dataset from Tencent, a popular service provider of social networks, video
services, and various other services in China
KGAT: Knowledge Graph Attention Network for Recommendation
To provide more accurate, diverse, and explainable recommendation, it is
compulsory to go beyond modeling user-item interactions and take side
information into account. Traditional methods like factorization machine (FM)
cast it as a supervised learning problem, which assumes each interaction as an
independent instance with side information encoded. Due to the overlook of the
relations among instances or items (e.g., the director of a movie is also an
actor of another movie), these methods are insufficient to distill the
collaborative signal from the collective behaviors of users. In this work, we
investigate the utility of knowledge graph (KG), which breaks down the
independent interaction assumption by linking items with their attributes. We
argue that in such a hybrid structure of KG and user-item graph, high-order
relations --- which connect two items with one or multiple linked attributes
--- are an essential factor for successful recommendation. We propose a new
method named Knowledge Graph Attention Network (KGAT) which explicitly models
the high-order connectivities in KG in an end-to-end fashion. It recursively
propagates the embeddings from a node's neighbors (which can be users, items,
or attributes) to refine the node's embedding, and employs an attention
mechanism to discriminate the importance of the neighbors. Our KGAT is
conceptually advantageous to existing KG-based recommendation methods, which
either exploit high-order relations by extracting paths or implicitly modeling
them with regularization. Empirical results on three public benchmarks show
that KGAT significantly outperforms state-of-the-art methods like Neural FM and
RippleNet. Further studies verify the efficacy of embedding propagation for
high-order relation modeling and the interpretability benefits brought by the
attention mechanism.Comment: KDD 2019 research trac
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