TY - JOUR

T1 - Privacy-Aware Point-of-Interest Category Recommendation in Internet of Things

AU - Qi, Lianyong

AU - Liu, Yuwen

AU - Zhang, Yulan

AU - Xu, Xiaolong

AU - Bilal, Muhammad

AU - Song, Houbing

N1 - Publisher Copyright: © 2014 IEEE.

PY - 2022/11/1

Y1 - 2022/11/1

N2 - In location-based social networks (LBSNs), extensive user check-in data incorporating user preferences for location is collected through Internet of Things devices, including cell phones and other sensing devices. However, directly acquiring the preferences of spars users remains an open challenge. This article offers a point-of-interest (POI) category recommendation model based on group preferences (PPCM). This model is proposed for three reasons: 1) because data influence the training of a deep learning model, the group influence of users is taken into account. To protect the privacy of users' check-in records and classify similar users into the same group, locality-sensitive hashing (LSH) is used; 2) a successive POI category recommendation model should capture the long- and short-term dependence ability. The attention mechanism and a temporal sliding window are paired with the long short-term memory (LSTM). This paradigm is useful for efficiently mining users' long-term dependencies and interests; and 3) although the overall users' check-in data are vast, check-in location options are also massive for a single user. There is a scarcity of data that may be utilized to mine user interests. Thus, instead of using POI, we leverage the POI category to better mine the user's interests. On real check-in data sets from New York City and Tokyo, the PPCM is compared to other models. The comparison results indicate that the PPCM has improved recommendation performance.

AB - In location-based social networks (LBSNs), extensive user check-in data incorporating user preferences for location is collected through Internet of Things devices, including cell phones and other sensing devices. However, directly acquiring the preferences of spars users remains an open challenge. This article offers a point-of-interest (POI) category recommendation model based on group preferences (PPCM). This model is proposed for three reasons: 1) because data influence the training of a deep learning model, the group influence of users is taken into account. To protect the privacy of users' check-in records and classify similar users into the same group, locality-sensitive hashing (LSH) is used; 2) a successive POI category recommendation model should capture the long- and short-term dependence ability. The attention mechanism and a temporal sliding window are paired with the long short-term memory (LSTM). This paradigm is useful for efficiently mining users' long-term dependencies and interests; and 3) although the overall users' check-in data are vast, check-in location options are also massive for a single user. There is a scarcity of data that may be utilized to mine user interests. Thus, instead of using POI, we leverage the POI category to better mine the user's interests. On real check-in data sets from New York City and Tokyo, the PPCM is compared to other models. The comparison results indicate that the PPCM has improved recommendation performance.

KW - Group influence

KW - Internet of Things (IoT)

KW - locality-sensitive hashing (LSH)

KW - long short-term memory (LSTM)

KW - privacy protection

KW - sensors

U2 - 10.1109/JIOT.2022.3181136

DO - 10.1109/JIOT.2022.3181136

M3 - Journal article

AN - SCOPUS:85131727253

VL - 9

SP - 21398

EP - 21408

JO - IEEE Internet of Things Journal

JF - IEEE Internet of Things Journal

SN - 2327-4662

IS - 21

ER -