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Intelligent Automated Sliding Door Robot Control System
Authors: - IJCTMrs. Sangeetha Lakshmi .G, Ms. Arun kumari .G
Number of views: 1157
A location-aware news feed (LANF) system generates news feeds for a mobile user based on her spatial
preference her current location and future locations) and non-spatial preference (i.e., her interest). 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). We argue that
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, our 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, we formulate the problem into two parts, namely, a decision problem and
an optimization problem. For the decision problem, we provide an exact solution by modeling it as a maximum
flow problem and proving its correctness. The optimization problem is solved by our proposed three-stage
heuristic algorithm.
We conduct a user study and experiments to evaluate the performance of D-MobiFeed using a real data
set crawled from Foursquare. Experimental results show that our proposed three-stage heuristic scheduling
algorithm outperforms the brute-force optimal algorithm by at least an order of magnitude in terms of running
time and the relative error incurred by the heuristic algorithm is below 1%. D-MobiFeed with the location
prediction method effectively improves the relevance, diversity, and efficiency of news feeds.