In many domains, there is an increasing reliance on Recommender Systems for helping identify products, services and people that meet some user-specified criteria. Given a pool of entities (e.g., movies, books, experts) and an objective function such systems have to identify a collection (i.e., a subset) of entities from the pool that optimizes the objective function. For example, in movie-recommendation systems (e.g., Netflix) the goal is to identify subsets of movies to recommend to registered users. Analogous problems arise in social networks and social media (e.g., Twitter, Facebook), where advertisers need to identify a small set of targets for their advertisements. Finally, project management teams in large organizations often use expertise management systems to identify the subset of experts needed to complete a specific project.
Current Recommender Systems suffer from severe limitations in settings where (i) the users multiple interactions with the system over time and the recommendations provided to a specific user at any given time need to take into account the past recommendations given to the same user or (ii) The entities that make up the recommended collections are rational entities, e.g., participants in a social network, or members of a project team, that have their own goals and preferences that influence their behavior as members of the collection. This project aims to address these two shortcomings of current Recommender Systems by designing, implementing, and evaluating combinatorial algorithms for identifying (a) sequences of collections, rather than a single collection and (b) collections of rational entities with individual goals, preferences, or objectives.
Broader impacts of this research include: new models and methods that signficantly advance the current state of the art in Recommender Systems, with broad applications in a number of domains including social networks (e.g., LinkedIn, Facebook, etc.), online recommendation systems (e.g., Amazon, Netflix, etc.), and daily-deal sites (e.g., Groupon, LivingSocial, etc.).
Acknowledgement: “This material is based upon work supported by the National Science Foundation under Grant No. 1253393.”
Disclaimer: “Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.”
Behzad Golshan, Evimaria Terzi, Panayiotis Tsaparas: ‘‘Collective Recommendations". Siam Data Mining Conference (SDM) 2019 (to appear). Code and datasets
Harshal A. Chaudhari, John W. Byers, Evimaria Terzi: Putting Data in the Driver’s Seat: Optimizing Earnings for On-Demand Ride-Hailing. ACM WSDM 2018.
Datasets and Code available here]
Sofia Maria Nikolakaki, Charalampos Mavroforakis, Alina Ene, Evimaria Terzi: Mining Tours and Paths in Activity Networks. WWW 2018.
Datasets and Code are available here
E. Galbrun, K. Pelechrinis, E, Terzi: Urban navigation beyond shortest route: The case of safe paths. Inf. Syst. Journal, 2016
N. Ruchansky, M. Crovella, E. Terzi: Matrix Completion with Queries. ACM SIGKDD International Conference on Data Mining and Knowledge Discovery, 2015.
D. Erdos, V. Ishakian, A. Bestavros, E. Terzi: A Divide-and-Conquer Algorithm for Betweenness Centrality. SIAM Data Mining Conferece, SDM 2015.
Behzad Golshan, Theodoros Lappas, Evimaria Terzi: Profit-maximizing Cluster Hires. ACM SIGKDD International Conference on Data Mining and Knowledge Discovery, 2014.
Rakesh Agrawal, Behzad Golshan, Evimaria Terzi: Grouping students in educational settings. ACM SIGKDD International Conference on Data Mining and Knowledge Discovery, 2014.
E. Galbrun, K. Pelechrinis, E. Terzi:
Safe Navigation in
Urban
Environments. ACM SIGKDD Workshop on Urban Computing, UrbComp ’14.
The work has also been featured in
Next City.
Behzad Golshan, Evimaria Terzi: Unveiling Variables in Systems of Linear Equations. SIAM Data Mining Conference, SDM 2014
A. Gionis, T. Lappas, K. Pelechrinis, E. Terzi: Customized Tour Recommendations in Urban Areas, WSDM 2014 .
B. Lawson E. Galbrun, E. Terzi: Demo on recipe recommendations that satisfy budget and dietary constraints.
One of the key achievements of this project is the development of a new data-science course by the Evimaria Terzi during spring 2015. The course is a data-science course that introduces the students to basic data-mining techniques using python. The PI developed the course from scratch using ipython notebooks, which allowed for the class to be very interactive. The class had one homework every approximately two weeks, and the students developed a project in the end of the class. The projects were presented in an open poster session. The course was a big success and the CS department is turing it into a regular course that is going to be taught every semester (i.e., both fall and spring). The material of this class is made publicly available here: