Social network systems (SNSs) such as Facebook and Twitter have recently attracted millions of users by providing social network based services to support easy message posting, information sharing and inter-friend communication. With the rapid growth of social networks, users of SNSs may easily get overwhelmed by the excessive volume of information feeds and felt challenging to digest and find truly valuable information. In this paper, we introduce a personalized feed recommendation service for SNS users based on user interests and social network contexts. Our approach incorporates both the topical preference and topological locality of a user in determining a feed's relevance. We propose a popularity diffusion model to propagate feeds in social networks and support our recommendation service with a set of personalized indices for feed-based information retrieval. A suite of efficient index manipulation algorithms are developed in our framework to address the need of managing the dynamics in social networks. We conduct an extensive performance evaluation to compare our proposal with alternative solutions using both real and synthetic social network data, which suggests our proposal outperforms in both efficiency and relevance.