Fatemeh Vahedian

DePaul University · College of Computing and Digital Media

The social web is characterized by a wide variety of connections between individuals and entities. A challenge for recommendation is to represent and synthesize all useful aspects of a user’s profile. Typically, researchers focus on a limited set of relations (for example, person to person ties for user recommendation or annotations in social taggi...

The social web is characterized by a wide variety of connections between individuals and entities. A challenge for social web recommendation is make the most effective use of a diverse set of relations. Typically, researchers focus on a limited set of relations (for example, person to person ties for user recommendation or annotations in social tag...

An effective technique for recommendation in social media and other heterogeneous networks is the weighted hybrid of low-dimensional components (WHyLDR). Recent studies have shown this technique is comparable to other in-tegrative approaches while being considerably more flexible. One key issue for the implementation of a WHyLDR system is the choic...

Social media sites accumulate a wide variety of information about users: likes and ratings, friend and follower links, an- notations, posts, media uploads, just to name a few. Key challenges for recommender systems research are (a) to syn- thesize of all of this data into an integrated recommendation model and (b) to support a wide variety of recom...

The success of neural networks (NNs) in a wide range of applications has led to increased interest in understanding the underlying learning dynamics of these models. In this paper, we go beyond mere descriptions of the learning dynamics by taking a graph perspective and investigating the relationship between the graph structure of NNs and their per...

Depression and anxiety are critical public health issues affecting millions of people around the world. To identify individuals who are vulnerable to depression and anxiety, predictive models have been built that typically utilize data from one source. Unlike these traditional models, in this study, we leverage a rich heterogeneous dataset from the...

Network alignment, or the task of finding meaningful node correspondences between nodes in different graphs, is an important graph mining task with many scientific and industrial applications. An important principle for network alignment is matched neighborhood consistency (MNC): nodes that are close in one graph should be matched to nodes that are...

Network alignment, the process of finding correspondences between nodes in different graphs, has significant scientific and industrial applications. We find that many existing network alignment methods fail to achieve accurate alignments because they break up node neighborhoods during alignment, failing to preserve matched neighborhood consistency....

Depression and anxiety are critical public health issues affecting millions of people around the world. To identify individuals who are vulnerable to depression and anxiety, predictive models have been built that typically utilize data from one source. Unlike these traditional models, in this study, we leverage a rich heterogeneous data set from th...

In the information overloaded web, personalized recommender systems are essential tools to help users find most relevant information. The most heavily-used recommendation frameworks assume user interactions that are characterized by a single relation. However, for many tasks, such as recommendation in social networks, user-item interactions must be...

Recommender systems are key components in information-seeking contexts where personalization is sought. However, the dominant framework for recommendation is essentially two dimensional, with the interaction between users and items characterized by a single relation. In many cases, such as social networks, users and items are joined in a complex we...

In the information overloaded web, personalized recommender systems are essential tools to help users find most relevant information. The most heavily-used recommendation frameworks assume user interactions that are characterized by a single relation. However, for many tasks, such as recommendation in social networks, user-item interactions must be...

In social networks, users and items are joined in a complex web of relations, which can be modeled as heterogeneous information networks. Such networks include a variety of object types and the rich relations among them. Recent research has shown that a hybrid recommendation approach combining components built from extended meta-paths in the networ...

Multi-relational matrix factorization is an effective technique for incorporating heterogeneous data into prediction tasks, such as personalized recommendation. Recent research has extended the set of relations that can be applied within heterogeneous network settings by composing non-local relations using network metapaths. One of the key problems...

Complex heterogeneous networks contain many types of relations, both local to a particular entity and distant in the network. Multi-relational factorization schemes that incorporate multiple local relations have shown improved recommendation accuracy. This paper extends this prior work on multi-relational factorization to include extended relations...