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.
PROJECT OUTPUT
VIDEO: (Click the below link to see the project output video):
EXISTING SYSTEM:
·
Among existing solutions in recommender systems RS, in particular,
collaborative filtering (CF) methods have been shown to be widely effective.
Based on the past behavior of users such as explicit user ratings and implicit
click logs, CF methods exploit the similarities between users’ behavior
patterns.
·
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. This is because the number of
unrated items is significantly more than that of rated items.
·
To address this problem, some existing work attempted to infer users’
ratings on unrated items based on additional information such as clicks and
bookmarks
DISADVANTAGES OF EXISTING SYSTEM:
·
These works require an overhead of collecting extra data, which itself
may have another data sparsity problem.
·
0-injection simply considers all uninteresting items as zero, it may
neglect to the characteristics of users or items. In contrast, l-injection not
only maximizes the impact of filling missing ratings but also considers the
characteristics of users and items, by imputing uninteresting items with low
peruse preferences.
PROPOSED SYSTEM:
·
In this work, we develop a more general l-injection to infer different
user preferences for uninteresting items for users, and show that l-injection
mostly outperforms 0-injection.
·
The proposed l-injection approach can improve the accuracy of top-N
recommendation based on two strategies: (1) preventing uninteresting items from
being included in the top-N recommendation, and (2) exploiting both
uninteresting and rated items to predict the relative preferences of unrated
items more accurately.
·
With the first strategy, because users are aware of the existence of
uninteresting items but do not like them, such uninteresting items are likely
to be false positives if included in top-N recommendation. Therefore, it is
effective to exclude uninteresting items from top-N recommendation results.
·
Next, the second strategy can be interpreted using the concept of
typical memory based CF methods.
ADVANTAGES OF PROPOSED SYSTEM:
·
We introduce a new notion of uninteresting items, and classify user
preferences into pre-use and post-use preferences to identify uninteresting
items.
·
We propose to identify uninteresting items via peruse preferences by
solving the OCCF problem and show its implications and effectiveness.
·
We propose low-value injection (called l-injection) to improve the
accuracy of top-N recommendation in existing CF algorithms.
·
While existing CF methods only employ user preferences on rated items,
the proposed approach employs both peruse and post-use preferences.
Specifically, the proposed approach first infers pre-use preferences of unrated
items and identifies uninteresting items.
SYSTEM ARCHITECTURE:
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
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System : Pentium Dual Core.
·
Hard Disk : 120 GB.
·
Monitor : 15’’ LED
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Input Devices : Keyboard, Mouse
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Ram : 1 GB
SOFTWARE REQUIREMENTS:
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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, 2017