l-Injection: Toward Effective CollaborativeFiltering Using Uninteresting Items

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l-Injection: Toward Effective Collaborative Filtering Using Uninteresting Items To buy this project in ONLINE, Contact: Email: jpinfotechprojects@gmail.com, Website: https://www.jpinfotech.org

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l-Injection: Toward Effective Collaborative Filtering Using
Uninteresting Items
ABSTRACT:
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. 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 (e.g., item-based CF,
SVD-based CF, and SVD++) by 2.5 to 5 times on average. Furthermore, our
solution improves the running time of those CF methods by 1.2 to 2.3 times when
its setting produces the best accuracy. The datasets and codes that we used in the
experiments are available at: https://goo.gl/KUrmip.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
 System : Pentium Dual Core.

 Hard Disk : 120 GB.
 Monitor : 15’’ LED
 Input Devices : Keyboard, Mouse
 Ram : 1 GB
SOFTWARE REQUIREMENTS:
 Operating system : Windows 7.
 Coding Language : JAVA/J2EE
 Tool : Netbeans 7.2.1
 Database : MYSQL
REFERENCE:
Jongwuk Lee Won-Seok Hwang Juan Parc Youngnam Lee Sang-Wook Kim
Dongwon Lee, “l-Injection: Toward Effective Collaborative Filtering Using
Uninteresting Items”, IEEE Transactions on Knowledge and Data Engineering,
2019.

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l-Injection: Toward Effective CollaborativeFiltering Using Uninteresting Items

1. l-Injection: Toward Effective Collaborative Filtering Using Uninteresting Items ABSTRACT: 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. 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 (e.g., item-based CF, SVD-based CF, and SVD++) by 2.5 to 5 times on average. Furthermore, our solution improves the running time of those CF methods by 1.2 to 2.3 times when its setting produces the best accuracy. The datasets and codes that we used in the experiments are available at: https://goo.gl/KUrmip. SYSTEM REQUIREMENTS: HARDWARE REQUIREMENTS:  System : Pentium Dual Core.

2.  Hard Disk : 120 GB.  Monitor : 15’’ LED  Input Devices : Keyboard, Mouse  Ram : 1 GB SOFTWARE REQUIREMENTS:  Operating system : Windows 7.  Coding Language : JAVA/J2EE  Tool : Netbeans 7.2.1  Database : MYSQL REFERENCE: Jongwuk Lee Won-Seok Hwang Juan Parc Youngnam Lee Sang-Wook Kim Dongwon Lee, “l-Injection: Toward Effective Collaborative Filtering Using Uninteresting Items”, IEEE Transactions on Knowledge and Data Engineering, 2019.

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