ABSTRACT:
Location recommendation plays an essential role in helping people find
attractive places. Though recent research has studied how to recommend
locations with social and geographical information, few of them addressed the
cold-start problem of new users. Because mobility records are often shared on
social networks, semantic information can be leveraged to tackle this
challenge. A typical method is to feed them into explicit-feedback-based
content-aware collaborative filtering, but they require drawing negative
samples for better learning performance, as users’ negative preference is not
observable in human mobility. However, prior studies have empirically shown
sampling-based methods do not perform well. To this end, we propose a scalable
Implicit-feedback-based Content-aware Collaborative Filtering (ICCF) framework
to incorporate semantic content and to steer clear of negative sampling. We
then develop an efficient optimization algorithm, scaling linearly with data
size and feature size, and quadratically with the
dimension of latent space. We further establish its relationship with graph Laplacian regularized matrix factorization. Finally, we
evaluate ICCF with a large-scale LBSN dataset in which users have profiles and
textual content. The results show that ICCF outperforms several competing
baselines, and that user information is not only effective for improving
recommendations but also coping with cold-start scenarios.
SYSTEM
REQUIREMENTS:
HARDWARE
REQUIREMENTS:
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System : Pentium Dual Core.
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Hard Disk : 120 GB.
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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.
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Coding
Language : JAVA/J2EE
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Tool : Netbeans 7.2.1
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Database
: MYSQL
REFERENCE:
Defu Lian,
Yong Ge, Fuzheng Zhang,
Nicholas Jing Yuan, Xing Xie, Tao Zhou, and Yong Rui, “Scalable Content-Aware Collaborative Filtering for
Location Recommendation”, IEEE Transactions on Knowledge and Data Engineering,
2018.
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