@conference {337, title = {A Fairness-aware Hybrid Recommender System}, booktitle = {RecSys Workshop on Responsible Recommendation (FATREC)}, year = {2018}, abstract = {

Recommender systems are used in variety of domains affecting people{\textquoteright}s lives. This has raised concerns about possible biases and discrimination that such systems might exacerbate. There are two primary kinds of biases inherent in recommender systems: observation bias and bias stemming from imbalanced data. Observation bias exists due to a feedback loop which causes the model to learn to only predict recommendations similar to previous ones. Imbalance in data occurs when systematic societal, historical, or other ambient bias is present in the data. In this paper, we address both biases by proposing a hybrid fairness-aware recommender system. Our model provides efficient and accurate recommendations by incorporating multiple user-user and item-item similarity measures, content, and demographic information, while addressing recommendation biases. We implement our model using a powerful and expressive probabilistic programming language called probabilistic soft logic. We experimentally evaluate our approach on a popular movie recommendation dataset, showing that our proposed model can provide more accurate and fairer recommendations, compared to a state-of-the art fair recommender system.

}, author = {Golnoosh Farnadi and Kouki, Pigi and Spencer K. Thompson and Sriram Srinivasan and Lise Getoor} } @conference {kouki:icdm17, title = {Collective Entity Resolution in Familial Networks}, booktitle = {IEEE International Conference on Data Mining (ICDM)}, year = {2017}, note = {To Appear}, abstract = {

Entity resolution in settings with rich relational structure often introduces complex dependencies between coreferences. Exploiting these dependencies is challenging {\textendash} it requires seamlessly combining statistical, relational, and logical dependencies. One task of particular interest is entity resolution in familial networks. In this setting, multiple partial representations of a family tree are provided, from the perspective of different family members, and the challenge is to reconstruct a family tree from these multiple, noisy, partial views. This reconstruction is crucial for applications such as understanding genetic inheritance, tracking disease contagion, and performing census surveys. Here, we design a model that incorporates statistical signals, such as name similarity, relational information, such as sibling overlap, and logical constraints, such as transitivity and bijective matching, in a collective model. We show how to integrate these features using probabilistic soft logic, a scalable probabilistic programming framework. In experiments on realworld data, our model significantly outperforms state-of-theart classifiers that use relational features but are incapable of collective reasoning. I

}, url = {https://github.com/pkouki/icdm2017}, author = {Kouki, Pigi and Pujara, Jay and Marcum, Christopher and Koehly, Laura and Lise Getoor} } @conference {kouki:recsys17, title = {User Preferences for Hybrid Explanations}, booktitle = {11th ACM Conference on Recommender Systems (RecSys)}, year = {2017}, author = {Kouki, Pigi and Schaffer, James and Pujara, Jay and ODonovan, John and Lise Getoor} } @conference {kouki:recsys15, title = {HyPER: A Flexible and Extensible Probabilistic Framework for Hybrid Recommender Systems}, booktitle = {9th ACM Conference on Recommender Systems (RecSys)}, year = {2015}, publisher = {ACM}, organization = {ACM}, author = {Kouki, Pigi and Fakhraei, Shobeir and Foulds, James and Eirinaki, Magdalini and Lise Getoor} }