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 secure computing in chennai

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
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» 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.
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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
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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.
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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
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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.
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9. ARCHITECUTRE: 9

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
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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.
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12. We Welcome You
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13. OUR PROCESS IS EASY
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