Learning Dysfunctions in Information Systems Development: Overcoming the Social Defenses with Transitional Objects

[ACM Press the 32nd international ACM SI

[ACM Press the 32nd international ACM SIGIR conference - Boston, MA, USA (2009.07.19-2009.07.23)] Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval - SIGIR '09 - Learning to recommend with social trust ensemble

✍ Ma, Hao; King, Irwin; Lyu, Michael R. 📂 Article 📅 2009 🏛 ACM Press ⚖ 874 KB

[ACM Press the 34th international ACM SI

[ACM Press the 34th international ACM SIGIR conference - Beijing, China (2011.07.24-2011.07.28)] Proceedings of the 34th international ACM SIGIR conference on Research and development in Information - SIGIR '11 - A large scale machine learning system for recommending heterogeneous content in social networks

✍ Shi, Yanxin; Ye, David; Goder, Andrey; Narayanan, Srinivas 📂 Article 📅 2011 🏛 ACM Press ⚖ 356 KB

[ACM Press the 34th international ACM SI

[ACM Press the 34th international ACM SIGIR conference - Beijing, China (2011.07.24-2011.07.28)] Proceedings of the 34th international ACM SIGIR conference on Research and development in Information - SIGIR '11 - A large scale machine learning system for recommending heterogeneous content in social networks

✍ Shi, Yanxin; Ye, David; Goder, Andrey; Narayanan, Srinivas 📂 Article 📅 2011 🏛 ACM Press ⚖ 356 KB

The goal of the Facebook recommendation engine is to compare and rank heterogeneous types of content in order to find the most relevant recommendations based on user preference and page context. The challenges for such a recommendation engine include several aspects: 1) the online queries being proc