7,361 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
Ethical Challenges in Data-Driven Dialogue Systems
The use of dialogue systems as a medium for human-machine interaction is an
increasingly prevalent paradigm. A growing number of dialogue systems use
conversation strategies that are learned from large datasets. There are well
documented instances where interactions with these system have resulted in
biased or even offensive conversations due to the data-driven training process.
Here, we highlight potential ethical issues that arise in dialogue systems
research, including: implicit biases in data-driven systems, the rise of
adversarial examples, potential sources of privacy violations, safety concerns,
special considerations for reinforcement learning systems, and reproducibility
concerns. We also suggest areas stemming from these issues that deserve further
investigation. Through this initial survey, we hope to spur research leading to
robust, safe, and ethically sound dialogue systems.Comment: In Submission to the AAAI/ACM conference on Artificial Intelligence,
Ethics, and Societ
Word Embeddings for Entity-annotated Texts
Learned vector representations of words are useful tools for many information
retrieval and natural language processing tasks due to their ability to capture
lexical semantics. However, while many such tasks involve or even rely on named
entities as central components, popular word embedding models have so far
failed to include entities as first-class citizens. While it seems intuitive
that annotating named entities in the training corpus should result in more
intelligent word features for downstream tasks, performance issues arise when
popular embedding approaches are naively applied to entity annotated corpora.
Not only are the resulting entity embeddings less useful than expected, but one
also finds that the performance of the non-entity word embeddings degrades in
comparison to those trained on the raw, unannotated corpus. In this paper, we
investigate approaches to jointly train word and entity embeddings on a large
corpus with automatically annotated and linked entities. We discuss two
distinct approaches to the generation of such embeddings, namely the training
of state-of-the-art embeddings on raw-text and annotated versions of the
corpus, as well as node embeddings of a co-occurrence graph representation of
the annotated corpus. We compare the performance of annotated embeddings and
classical word embeddings on a variety of word similarity, analogy, and
clustering evaluation tasks, and investigate their performance in
entity-specific tasks. Our findings show that it takes more than training
popular word embedding models on an annotated corpus to create entity
embeddings with acceptable performance on common test cases. Based on these
results, we discuss how and when node embeddings of the co-occurrence graph
representation of the text can restore the performance.Comment: This paper is accepted in 41st European Conference on Information
Retrieva
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