TY - CHAP
T1 - On Cross-Domain Transfer in Venue Recommendation
AU - Manotumruksa, Jarana
AU - Rafailidis, Dimitrios
AU - Macdonald, Craig
AU - Ounis, Iadh
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Venue recommendation strategies are built upon collaborative filtering techniques that rely on matrix factorisation (mf), to model users’ preferences. Various cross-domain strategies have been proposed to enhance the effectiveness of mf-based models on a target domain, by transferring knowledge from a source domain. Such cross-domain recommendation strategies often require user overlap, that is common users on the different domains. However, in practice, common users across different domains may not be available. To tackle this problem, recently, several cross-domains strategies without users’ overlaps have been introduced. In this paper, we investigate the performance of state-of-the-art cross-domain recommendation that do not require overlap of users for the venue recommendation task on three large location-based social networks (lbsn) datasets. Moreover, in the context of cross-domain recommendation we extend a state-of-the-art sequential-based deep learning model to boost the recommendation accuracy. Our experimental results demonstrate that state-of-the-art cross-domain recommendation does not clearly contribute to the improvements of venue recommendation systems, and, further we validate this result on the latest sequential deep learning-based venue recommendation approach. Finally, for reproduction purposes we make our implementations publicly available.keywordscross-domain recommendationvenue suggestiontransfer learning.
AB - Venue recommendation strategies are built upon collaborative filtering techniques that rely on matrix factorisation (mf), to model users’ preferences. Various cross-domain strategies have been proposed to enhance the effectiveness of mf-based models on a target domain, by transferring knowledge from a source domain. Such cross-domain recommendation strategies often require user overlap, that is common users on the different domains. However, in practice, common users across different domains may not be available. To tackle this problem, recently, several cross-domains strategies without users’ overlaps have been introduced. In this paper, we investigate the performance of state-of-the-art cross-domain recommendation that do not require overlap of users for the venue recommendation task on three large location-based social networks (lbsn) datasets. Moreover, in the context of cross-domain recommendation we extend a state-of-the-art sequential-based deep learning model to boost the recommendation accuracy. Our experimental results demonstrate that state-of-the-art cross-domain recommendation does not clearly contribute to the improvements of venue recommendation systems, and, further we validate this result on the latest sequential deep learning-based venue recommendation approach. Finally, for reproduction purposes we make our implementations publicly available.keywordscross-domain recommendationvenue suggestiontransfer learning.
U2 - 10.1007/978-3-030-15712-8_29
DO - 10.1007/978-3-030-15712-8_29
M3 - Chapter
SN - 978-3-030-15711-1
VL - 11437
T3 - Lecture Notes in Computer Science
SP - 443
EP - 456
BT - Advances in Information Retrieval
A2 - Azzopardi, L.
A2 - Stein, B.
A2 - Fuhr, N.
A2 - Mayr, P.
A2 - Hauff, C.
A2 - Hiemstra, D.
PB - Springer, Cham
T2 - European Conference on Information Retrieval Research
Y2 - 14 April 2019 through 18 April 2019
ER -