140,040 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
Social Information Processing in Social News Aggregation
The rise of the social media sites, such as blogs, wikis, Digg and Flickr
among others, underscores the transformation of the Web to a participatory
medium in which users are collaboratively creating, evaluating and distributing
information. The innovations introduced by social media has lead to a new
paradigm for interacting with information, what we call 'social information
processing'. In this paper, we study how social news aggregator Digg exploits
social information processing to solve the problems of document recommendation
and rating. First, we show, by tracking stories over time, that social networks
play an important role in document recommendation. The second contribution of
this paper consists of two mathematical models. The first model describes how
collaborative rating and promotion of stories emerges from the independent
decisions made by many users. The second model describes how a user's
influence, the number of promoted stories and the user's social network,
changes in time. We find qualitative agreement between predictions of the model
and user data gathered from Digg.Comment: Extended version of the paper submitted to IEEE Internet Computing's
special issue on Social Searc
Designing electronic collaborative learning environments
Electronic collaborative learning environments for learning and working are in vogue. Designers design them according to their own constructivist interpretations of what collaborative learning is and what it should achieve. Educators employ them with different educational approaches and in diverse situations to achieve different ends. Students use them, sometimes very enthusiastically, but often in a perfunctory way. Finally, researchers study them and—as is usually the case when apples and oranges are compared—find no conclusive evidence as to whether or not they work, where they do or do not work, when they do or do not work and, most importantly, why, they do or do not work. This contribution presents an affordance framework for such collaborative learning environments; an interaction design procedure for designing, developing, and implementing them; and an educational affordance approach to the use of tasks in those environments. It also presents the results of three projects dealing with these three issues
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