Open Access Journal

ISSN : 2394-2320 (Online)

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

Open Access Journal

International Journal of Engineering Research in Computer Science and Engineering (IJERCSE)

Monthly Journal for Computer Science and Engineering

ISSN : 2394-2320 (Online)

News Feed Recommendation & Diversification for Mobile User

Author : Miss.Vanita M.Gaikwad 1 Prof.Yogita S.Pagar 2

Date of Publication :10th April 2017

Abstract: A location-aware news feed (LANF) system generates news feeds for a mobile user supported their abstraction preference (i.e., their current location and future locations) and non-spatial preference (i.e., their interest). Existing LANF systems merely send the foremost relevant geo-tagged messages to their users. unfortunately, the key limitation of such associate existing approach is that, a news feed might contain messages related to identical location (i.e., point-of-interest) or identical class of locations (e.g., food, diversion or sport). We argue that diversity may be an important feature for location-aware news feeds as a result of it helps users discover new places and activities. In this paper, we propose News-Feed; a replacement LANF system allows a user to specify the minimum variety of message classes (h) for the messages in a very news feed. In News-Feed, our objective is to with efficiency schedule news feeds for a mobile user at their current and expected locations, such (i) every news feed contains messages happiness to a minimum of h completely different classes, and (ii) their total connectedness to the user is maximized. To attain this objective, we formulate the matter into 2 components, namely, a choice drawback and an improvement drawback. For the choice drawback, we offer a definite answer by modeling it as a most flow drawback and proving its correctness. The improvement drawback is resolved by our projected three-stage heuristic formula. we conduct a user study and experiments to estimating the performance of News-Feed employing a real information set crawled from Experimental results show that our projected three-stage heuristic planning formula outperforms the brute-force optimum formula by a minimum of associate order of magnitude in terms of period of time and also the relative error incurred by the heuristic formula is below 125th. News-Feed with the placement prediction methodology effectively improves the connectedness, diversity, and potency of reports feeds.

Reference :

    1. 2010CensusTIGER/Line Shapefiles. http:// www.census.gov/ geo/ www/ tiger/ tgrshp2010/ tgrshp2010.html.
    2. Z. Abbassi, V. S. Mirrokni, and. Thakur. Diversity maximization under matroid constraints. In ACM KDD, 2013.
    3. G. Adomavicius and Y. Kwon. Improving aggregate recommendation diversity using ranking-based techniques. IEEE TKDE, 24(5):896–911, 2012.
    4. G. Adomavicius and A. Tuzhilin. Toward the next generation of recommender Systems: A survey of the state-of-the-art and possible extensions. IEEE TKDE, 17(6):734–749, 2005.
    5. R. Agrawal, S. Gollapudi, A. Halverson, and S. Leong. Diversifying search results. In ACM WSDM, 2009.
    6. R. K. Ahuja, T. L. Magnanti, and J. B. Orlin. Network Flows: Theory, Algorithms, and Applications. Prentice Hall, 1993.
    7. J. Bao, M. F. Mokbel, and C.-Y. Chow. GeoFeed: Location-aware news feed system. In IEEE ICDE, 2012
    8. J. Carbonell and J. Goldstein. The use of mmr, diversity-based reranking for reordering documents and producing summaries. In ACM SIGIR, 1998.
    9. H. Jeung, M. L. Yiu, X. Zhou, and C. S. Jensen. Path prediction and predictive range querying in road network databases.19 (4):585–602, 2010
    10. lpsolve 5.5.5.0. http:// lpsolve.sourceforge.net /5.5/.
    11. A. Machanavajjhala, D. Kifer, J. Gehrke, and M.Venkitasubramaniam. ldiversity: Privacy beyondkanonymity.ACM TKDD, 1(1):3, 2007.
    12. C. D. Manning, P. Raghavan, and H. Sch ̈utze.Introduction to InformationRetrieval. Cambridge University Press, 2008.
    13. Privacybeyondkanonymity.ACM TKDD, 1(1):3, 2007.
    14. A. Silberstein, J. Terrace, B. F. Cooper, and R. Ramakrishnan. Feeding Frenzy: Selectively materializing user‘s event feed. In ACM SIGMOD, 2010.
    15. C. X. Zhai,W.W. Cohen, and J. Lafferty. Beyond independent relevance: Methods and evaluation metrics for subtopic retrieval. In ACM SIGIR, 2003.

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