![REFERENCES
»O. Ajao, J. Hong, and W. Liu. A survey of location inference techniques on twitter. Journal of
Information Science, 1:1–10, 2015.
»E. Amig´ o, J. C. De Albornoz, I. Chugur, A. Corujo, J. Gonzalo, T. Mart´ın, E. Meij, M. De
Rijke, and D. Spina. Overview of replab 2013: Evaluating online reputation monitoring
systems. In Proceedings of CLEF, pages 333–352. Springer, 2013.
»F. Atefeh and W. Khreich. A survey of techniques for event detection in twitter.
Computational Intelligence, 31(1):132–164, 2015.
»] H. Bo, P. Cook, and T. Baldwin. Geolocation prediction in social media data by finding
location indicative words. In Proceedings of COLING, pages 1045–1062, 2012.
»J. D. Burger, J. Henderson, G. Kim, and G. Zarrella. Discriminating gender on twitter. In
Proceedings of EMNLP, pages 1301–1309, 2011.
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L injection toward effective collaborative filtering using uninteresting items
- 1. l-Injection: Toward Effective Collaborative Filtering Using Uninteresting Items
- 2. ABSTRACT Using this notion, we identify uninteresting items that have not been rated yet but are likely to receive low ratings from users, and selectively impute them as low values. As our proposed approach is method-agnostic, it can be easily applied to a variety of CF algorithms. Through comprehensive experiments with three real-life datasets (e.g., Movielens, Ciao, and Watcha), we demonstrate that our solution consistently and universally enhances the accuracies of existing CF algorithms More info on how to use this template at www.slidescarnival.com/help-use-presentation-template This template is free to use under Creative Commons Attribution license. You can keep the Credits slide or mention SlidesCarnival and other resources used in a slide footer. 2 » Filtering Using Uninteresting Items We develop a novel framework, named as l- injection, to address the sparsity problem of recommender systems. By carefully injecting low values to a selected set of unrated user-item pairs in a user-item matrix, we demonstrate that top-N recommendation accuracies of various collaborative filtering (CF) techniques can be significantly and consistently improved. We first adopt the notion of pre-use preferences of users toward a vast amount of unrated items.
- 3. » In general, CF methods are categorized into two approaches: memory-based and model-based . First, memory based methods predict the ratings of a user using the similarity of her neighborhoods, and recommend the items with high ratings. Second, model-based methods, build a model capturing a users’ ratings on items, and then predict her unknown ratings based on the learned model. Most CF methods, despite their wide adoption in practice, suffer from low accuracy if most users rate only a few items (thus producing a very sparse rating matrix), called the data sparsity problem. 3EXISTING SYSTEM:
- 4. DISADVANTAGE: » This approach could mistakenly assign low values to the items that users might like, thereby affecting an overall accuracy in recommendation. » 0-injection simply considers all uninteresting items as zero, it may neglect to the characteristics of users or items. 4
- 5. PROPOSED SYSTEM: The proposed l-injection approach can improve the accuracy of top-N recommendation based on two strategies by using Collaborative filter algorithm and Rank Prediction Technique. » preventing uninteresting items from being included in the top-N recommendation. » Exploiting both uninteresting and rated items to predict the relative preferences of unrated items more accurately. » Diverse device hand photos by Facebook 5
- 6. ADVANTAGE: » By using the Location Verification algorithm we can block the user who are all using fake location. » The proposed work is very effective compare to the Existing method. 6
- 7. ALGORITHM: » Collaborative Filter Algorithm(used for for filtering the uninterested items) » Rank Prediction Technique(used to show the top rank products) » ieee 2018-2019 services computing projects 7
- 8. FUTURE WORK We improve the efficiency of successfully demonstrated that the proposed approach is effective and practical, dramatically improving the accuracies of existing CF methods by 2.5 to 5 times. 8
- 10. HARDWARE CONFIGURATION » System : Pentium IV 2.4 GHz. » Hard Disk : 40 GB. » Monitor : 15 VGA Colour. » Mouse : Logitech. » Ram : 1 GB. SYSTEM CONFIGURATION SOFTWARE CONFIGURATION » Operating system : Windows XP/7/8. » Coding Language : JAVA/J2EE » IDE : Eclipse » Database : MYSQL 10
- 11. REFERENCES »O. Ajao, J. Hong, and W. Liu. A survey of location inference techniques on twitter. Journal of Information Science, 1:1–10, 2015. »E. Amig´ o, J. C. De Albornoz, I. Chugur, A. Corujo, J. Gonzalo, T. Mart´ın, E. Meij, M. De Rijke, and D. Spina. Overview of replab 2013: Evaluating online reputation monitoring systems. In Proceedings of CLEF, pages 333–352. Springer, 2013. »F. Atefeh and W. Khreich. A survey of techniques for event detection in twitter. Computational Intelligence, 31(1):132–164, 2015. »] H. Bo, P. Cook, and T. Baldwin. Geolocation prediction in social media data by finding location indicative words. In Proceedings of COLING, pages 1045–1062, 2012. »J. D. Burger, J. Henderson, G. Kim, and G. Zarrella. Discriminating gender on twitter. In Proceedings of EMNLP, pages 1301–1309, 2011. 11
- 13. OUR PROCESS IS EASY first second last 13