A Location- And Diversity-Aware News Feed System for Mobile Users

ABSTRACT

A location-aware news feed (LANF) system generates news feeds for a mobile user based on her spatial preference (i.e., 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.

                                                                 

 AIM

The aim of this paper is 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.

SCOPE

The scope of this paper is to achieve this objective, we formulate the problem into two parts, namely, a decision problem and an optimization problem.

EXISTING SYSTEM

MobiFeed the state-of-the-art location-aware news feed system schedules news feeds for mobile users. In MobiFeed, the relevance of a message m to Bob is measured by both the content similarity between m and Bob’s submitted messages (i.e., a non-spatial factor) and the distance between m and Bob (i.e., a spatial factor). MobiFeed is motivated by the fact that, if the news feeds are only computed based on a user’s location at the query time (i.e., it does not consider the user’s future locations, e.g., GeoFeed), the total relevance of news feeds is not optimized With the geographical distance between a message and a mobile user in a relevance measure model, the relevance of a message to a mobile user is changing as the user is moving. Such a dynamic environment gives us an opportunity to employ location prediction technique to improve the quality of news feeds and the system efficiency. Existing diversification problems focus on retrieving an individual list of items with a certain level of diversity. In contrast, with our location prediction techniques, we aim at improving the quality of news feeds by scheduling multiple location- and diversity-aware news feeds for mobile users simultaneously.

DISADVANTAGES

·      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).

·      In MobiFeed considers a mobile environment that makes our location- and diversity-aware news feed system unique and more challenging.

PROPOSED SYSTEM

In this project, 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.

ADVANTAGES

·      D-MobiFeed with the location prediction method effectively improves the relevance, diversity, and efficiency of news feeds.

·      D-MobiFeed can efficiently provide location- and diversity-aware news feeds when maintaining their high quality in terms of relevance

SYSTEM ARCHITECTURE

SYSTEM CONFIGURATION

HARDWARE REQUIREMENTS:-

·                Processor          -   Pentium –III

·                Speed                -    1.1 Ghz

·                RAM                 -    256 MB(min)

·                Hard Disk         -   20 GB

·                Floppy Drive    -    1.44 MB

·                Key Board                 -    Standard Windows Keyboard

·                Mouse               -    Two or Three Button Mouse

·                Monitor             -    SVGA

SOFTWARE REQUIREMENTS:-

·                Operating System      :Android OS             

·                Front End                  : JAVA

·                Database                   : SqLite

·                Tool                           :Eclipse

REFERENCES

Chow, C.Xu, W. “A Location- and Diversity-aware News Feed System for Mobile Users” IEEE Transactions ON Services Computing, Volume PP, Issue 99 MAY 2015.