Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Kannan Achan"'
Personalized diversification of complementary recommendations with user preference in online grocery
Publikováno v:
Frontiers in Big Data, Vol 6 (2023)
Complementary recommendations play an important role in surfacing the relevant items to the customers. In the cross-selling scenario, some customers might present more exploratory shopping behaviors and prefer more diverse complements, while other cu
Externí odkaz:
https://doaj.org/article/600085d3a7af483699739209f70ee388
Autor:
Reza Yousefi Maragheh, Ramin Giahi, Jianpeng Xu, Lalitesh Morishetti, Shanu Vashishtha, Kaushiki Nag, Jason Cho, Evren Korpeoglu, Sushant Kumar, Kannan Achan
Publikováno v:
2022 IEEE International Conference on Big Data (Big Data).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d1d707564d3fe5543039dfc75f0941b3
https://doi.org/10.1109/bigdata55660.2022.10020542
https://doi.org/10.1109/bigdata55660.2022.10020542
The complementary item recommender system (CIRS) recommends the complementary items for a given query item. Existing CIRS models consider the item co-purchase signal as a proxy of the complementary relationship due to the lack of human-curated labels
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::406cbecfe2f29e3064a2ca38a26e8874
http://arxiv.org/abs/2202.05456
http://arxiv.org/abs/2202.05456
Publikováno v:
2021 IEEE International Conference on Big Data (Big Data).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2a6d217b3d0dd43a2f738dbecefe266e
https://doi.org/10.1109/bigdata52589.2021.9671649
https://doi.org/10.1109/bigdata52589.2021.9671649
Recently, Graph Neural Networks (GNNs) have proven their effectiveness for recommender systems. Existing studies have applied GNNs to capture collaborative relations in the data. However, in real-world scenarios, the relations in a recommendation gra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::982b229bbefc312ddf0c2235c975028c
http://arxiv.org/abs/2111.14036
http://arxiv.org/abs/2111.14036
Publikováno v:
KDD
Recommender systems are powerful tools for information filtering with the ever-growing amount of online data. Despite its success and wide adoption in various web applications and personalized products, many existing recommender systems still suffer
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a82cb2c621f4a2050183519892835063
https://doi.org/10.1145/3447548.3467234
https://doi.org/10.1145/3447548.3467234
Publikováno v:
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining.
Selecting the optimal recommender via online exploration-exploitation is catching increasing attention where the traditional A/B testing can be slow and costly, and offline evaluations are prone to the bias of history data. Finding the optimal online
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::536a171a2ae9823a99f92e20b4ef0e2f
https://doi.org/10.1145/3447548.3467192
https://doi.org/10.1145/3447548.3467192
Publikováno v:
Companion Proceedings of the Web Conference 2021.
Through recent advancements in speech technologies and introduction of smart assistants, such as Amazon Alexa, Apple Siri and Google Home, increasing number of users are interacting with various applications through voice commands. E-commerce compani
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3755d4fd4d19fab15c7927069fae915b
https://doi.org/10.1145/3442442.3451893
https://doi.org/10.1145/3442442.3451893
Publikováno v:
IEEE BigData
The problem of basket recommendation~(BR) is to recommend a ranking list of items to the current basket. Existing methods solve this problem by assuming the items within the same basket are correlated by one semantic relation, thus optimizing the ite
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5f1f31e80ba4d5371675e0298e864f1c
http://arxiv.org/abs/2010.11419
http://arxiv.org/abs/2010.11419
Publikováno v:
SIGIR
Recommender systems based on collaborative filtering are highly vulnerable to data poisoning attacks, where a determined attacker injects fake users with false user-item feedback, with an objective to either corrupt the recommender system or promote/
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f94344a104204122ada3b4de9755e56e
https://doi.org/10.1145/3397271.3401301
https://doi.org/10.1145/3397271.3401301