A Location- and Diversity-aware News Feed System for Mobile Users
A location-aware news feed (LANF) system generates news feeds for a mobile user based on her spatial preference and non-spatial preference. Existing LANF systems simply send the most relevant geo-tagged messages to their users. Unfortunately, the major limitation of such an existing approach is that, a news feed may contain messages related to the same location (i.e., point-of-interest) or the same category of locations (e.g., food, entertainment or sport). Diversity is a very important feature for location-aware news feeds because it helps users discover new places and activities. In this paper, we propose D-MobiFeed; a new LANF system enables a user to specify the minimum number of message categories (h) for the messages in a news feed. In D-MobiFeed, objective is to efficiently schedule news feeds for a mobile user at her current and predicted locations, such that (i) each news feed contains messages belonging to at least h different categories, and (ii) their total relevance to the user is maximized. To achieve this objective, formulate the problem into two parts, namely, a decision problem and an optimization problem. For the decision problem, an exact solution is provided by modeling it as a maximum flow problem and proving its correctness. The optimization problem is solved by proposed three-stage heuristic algorithm. D-MobiFeed with the location prediction method effectively improves the relevance, diversity, and efficiency of news feeds.