Publication details
Factoring Personalization in Social Media Recommendations
| Authors | |
|---|---|
| Year of publication | 2019 |
| Type | Article in Proceedings |
| Conference | Proceedings of the 13th IEEE International Conference on Semantic Computing |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1109/ICOSC.2019.8665624 |
| Keywords | recommender systems; personalization |
| Description | Nowadays, since social media sites and online social networks have created big media data, it is thus complex and time-consuming for users to find the preferred social media from a large media catalog. Social media recommender systems are therefore emerged to recommend personalized media objects. However, most media recommender systems only focus on one aspect of social media. It is lacking a big picture of how to build an effective social media recommender system. Therefore, this paper tackles this challenge first for specifying the distinct features of media object that can be used for recommender systems, and then discusses five critical aspects that can affect the design of social media recommender systems. This paper further indicates how to assemble these critical aspects and concludes that when we apply traditional recommender algorithms in the media context, those are the critical aspects to improve and optimize social media recommneder systems. |