Reference number : GC-2017-Q1-00499, amendment number : , agreement type : G, recipient legal name : Ding, Chen (Ryerson University), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Engage Grants for Universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : The proposed research is in the area of recommender systems and predictive analytics, and should have an_x000D_
impact in Canadian ICT (Information and Communications Technology) sector. PostBeyond is a_x000D_
Toronto-based company providing employee communications solutions to primarily large enterprises. Using_x000D_
PostBeyond's product, company employees can have easy access to company-related information through a_x000D_
central content hub, with the option to share the information externally across their social networks. The_x000D_
current solution is one size fit all. Sometimes it could be hard for individual employees to find content they are_x000D_
particularly interested in and would like to share with others. To solve this problem, in this project, we propose_x000D_
to build a personalized recommender system that could suggest content for employees to access, consume and_x000D_
share based on their individual preferences. A few research challenges we must tackle include: 1) the lack of_x000D_
historical data on users' content access and sharing patterns; 2) the need to define content profile that is both_x000D_
generic and extensible; 3) the need to accommodate users' dynamic behavior into the system; 4) the difficulty_x000D_
of effectively integrating group recommendation features into the personalized recommendation system. To_x000D_
successfully build the proposed recommender system, the following three tasks are identified: 1) developing a_x000D_
logging tool to save users' content access and social sharing records; 2) applying log mining techniques to mine_x000D_
the user patterns, behaviors and observe the general content consumption or sharing trends; 3) designing and_x000D_
implementing a hybrid recommender system to do personalized content suggestion and prediction. The_x000D_
proposed project could benefit PostBeyond by making their current communications solutions more efficient_x000D_
and customized towards each individual employee's behavior, and thus a more attractive solution to their_x000D_
potential customers. It also drives economic benefits for both PostBeyond and the many Canadian enterprises_x000D_
that use their solutions., program purpose french : The proposed research is in the area of recommender systems and predictive analytics, and should have an_x000D_
impact in Canadian ICT (Information and Communications Technology) sector. PostBeyond is a_x000D_
Toronto-based company providing employee communications solutions to primarily large enterprises. Using_x000D_
PostBeyond's product, company employees can have easy access to company-related information through a_x000D_
central content hub, with the option to share the information externally across their social networks. The_x000D_
current solution is one size fit all. Sometimes it could be hard for individual employees to find content they are_x000D_
particularly interested in and would like to share with others. To solve this problem, in this project, we propose_x000D_
to build a personalized recommender system that could suggest content for employees to access, consume and_x000D_
share based on their individual preferences. A few research challenges we must tackle include: 1) the lack of_x000D_
historical data on users' content access and sharing patterns; 2) the need to define content profile that is both_x000D_
generic and extensible; 3) the need to accommodate users' dynamic behavior into the system; 4) the difficulty_x000D_
of effectively integrating group recommendation features into the personalized recommendation system. To_x000D_
successfully build the proposed recommender system, the following three tasks are identified: 1) developing a_x000D_
logging tool to save users' content access and social sharing records; 2) applying log mining techniques to mine_x000D_
the user patterns, behaviors and observe the general content consumption or sharing trends; 3) designing and_x000D_
implementing a hybrid recommender system to do personalized content suggestion and prediction. The_x000D_
proposed project could benefit PostBeyond by making their current communications solutions more efficient_x000D_
and customized towards each individual employee's behavior, and thus a more attractive solution to their_x000D_
potential customers. It also drives economic benefits for both PostBeyond and the many Canadian enterprises_x000D_
that use their solutions., agreement title english : A Hybrid Personalized Recommender System for Content Suggestion, agreement title french : A Hybrid Personalized Recommender System for Content Suggestion, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-06-14, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2018-2019), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2018-2019), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada