We study the cold-start link prediction problem where edges between vertices is unavailable by learning vertex-based similarity metrics. Existing metric learning methods for link prediction fail to consider communities which can be observed in many real-world social networks. Because di↵erent communities usually exhibit di↵erent intra-community homogeneities, learning a global similarity metric...

The cold start problem, frequent with recommender systems, addresses the issue in cases where we don’t know enough about our users (e.g., the user hasn’t rated anything yet, or there are no user activities) in that specific domain. In our paper we present a simple and robust transfer learning approach where we model users’ behavior in a source domain, transferring that knowledge to a new, targe...

Generating personalized recommendations for new users is particularly challenging, because in this case, the recommender system has little or no user record of previously rated items. Connecting the newcomer to an underlying trust network among the users of the recommender system alleviates this socalled cold start problem. In this paper, we study the effect of guiding the new user through the ...

We employ universal schema for slot filling and cold start. In universal schema, we allow each surface pattern from raw text, and each type defined in ontology, i.e. TACKBP slots to represent relations. And we use matrix factorization to discover implications among surface patterns and target slots. First, we identify mentions of entities from the whole text corpus and extract relations between...

In this chapter we present a report of the ESWC 2014 Challenge on Linked Open Data-enabled Recommender Systems, which consisted of three tasks in the context of book recommendation: rating prediction in cold-start situations, top N recommendations from binary user feedback, and diversity in content-based recommendations. Participants were requested to address the tasks by means of recommendatio...

This project develops a hybrid model that combines content-based with collaborative filtering (CF) for hotel recommendation. This model considers both hotel popularity in input destination and users preference. It produces the prediction with 53.6% accuracy on test data-4% improvement on purely content-based model. Addtionally, three issues are well-resolved when implementing CF: sparsity in ut...

I wish to thank very much Messrs. Smith and Howard of the Boston Safe Deposit and Trust Company for inviting me to speak before you. I am indeed delighted to be here. Nevertheless, I am mindful that I start from a rather disadvantageous position for two reasons « First of all it may be very difficult for me, even under the most auspicious of circumstances, to keep up with the overall tone of pr...

We investigate the C-start escape response of larval fish by combining flow simulations using remeshed vortex methods with an evolutionary optimization. We test the hypothesis of the optimality of C-start of larval fish by simulations of larval-shaped, twoand three-dimensional self-propelled swimmers. We optimize for the distance travelled by the swimmer during its initial bout, bounding the sh...

A solution is presented to the problem of quickly detecting the end of an asynchronous parallel computation, in which processors exchange task request messages asynchronously, and termination occurs when all processors are idle and no messages are in transit. The proposed termination detection scheme is based on a nite automaton consisting of only two states, and one possible transition between...