Leveraging Aggregate Ratings for Better Recommendations

The paper presents a method that uses aggregate ratings provided by various segments of users for various categories of items to derive better estimations of unknown individual ratings. This is achieved by converting the aggregate ratings into constraints on the parameters of a rating estimation model presented in the paper. The paper also demonstrates theoretically that these additional constraints reduce rating estimation errors resulting in better rating predictions

Leveraging Aggregate Ratings for Better Recommendations

The paper presents a method that uses aggregate ratings provided by various segments of users for various categories of items to derive better estimations of unknown individual ratings. This is achieved by converting the aggregate ratings into constraints on the parameters of a rating estimation model presented in the paper. The paper also demonstrates theoretically that these additional constraints reduce rating estimation errors resulting in better rating predictions

Finding co-solvers on Twitter, with a little help from Linked Data

In this paper we propose a method for suggesting potential collaborators for solving innovation challenges online, based on their competence, similarity of interests and social proximity with the user. We rely on Linked Data to derive a measure of semantic relatedness that we use to enrich both user profiles and innovation problems with additional relevant topics, thereby improving the performance of co-solver recommendation. We evaluate this approach against state of the art methods for query enrichment based on the distribution of topics in user profiles, and demonstrate its usefulness in recommending collaborators that are both complementary in competence and compatible with the user. Our experiments are grounded using data from the social networking service Twitter.com

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

A Comprehensive Economic Stimulus for our Failing Economy

This paper presents a comprehensive plan to fix the ailing American economy, through a five-step approach. First, the Federal Reserve must continue to broaden the scope of monetary policy, by purchasing and selling long-term securities. Manipulating expectations through FOMC statements is another tool at the Federal Reserve’s disposal. Secondly, the government must enact fiscal stimulus to stabilize the economy in the short and medium runs, through investment in infrastructure projects, green technology, fusion technology, and science education. Additionally, the new fiscal policy must tackle the mortgage meltdown, which is weighing down the entire economy. Third, the regulatory system must be changed to reduce the likelihood of another financial collapse, starting with the nationalization of the ratings agencies. Ratings should be updated faster, with a numeric grading system rather than the pre-existing letter grades. Fourth, our globalized economy insures that a coordinated globalized response is necessary to recover. Global cooperation to reduce inflation and avoid protectionist policies is vital. Finally, the American bailout policy must be made clear, only giving bailouts to companies that are sound but financially strapped and those that are too big to fail