A SURVEY ON FEED RECOMMENDATION

PhD Qualifying Examination


Title: "A SURVEY ON FEED RECOMMENDATION"

by

Miss Wenyi XIAO


Abstract:

Nowadays, people have been overwhelmed by the flood of information on the 
Internet. Thus, various news feeds Apps based on Recommender System(RS) are 
developed to provide people with interesting information according to their 
past reading histories. On average, online users can spent more than an hour 
everyday on Feeds apps. It has been known that better personalized 
recommendation of news can lead to more spending time of users on the app, and 
consequently it is more profitable for the App developer. To provide better 
user experiences, personalized feeds recommendation have been widely adopted by 
these platforms. In real-worlds social media platforms, such as Facebook and 
Twitter, can naturally serve the feeds function and social connections can be 
very useful for feeds recommendation.

This survey aims to provide a comprehensive review of recent research efforts 
on deep learning based news feed recommendation towards fostering innovations 
of recommender system research. A taxonomy of deep learning based 
recommendation models and their applications on news domain are presented. Open 
problems are identified based on the analytics of the reviewed works and 
discussed potential solutions.


Date:			Thursday, 28 June 2018

Time:                  	3:30pm - 5:30pm

Venue:                  Room 5508
                         Lifts 25/26

Committee Members:	Prof. Qiang Yang (Supervisor)
 			Prof. Gary Chan (Chairperson)
 			Dr. Yangqiu Song
 			Prof. Nevin Zhang


**** ALL are Welcome ****