Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - SemanticSVD++
T2 - International Conference on Web Intelligence 2014
AU - Rowe, Matthew
PY - 2014/8/10
Y1 - 2014/8/10
N2 - Recommender systems profile the preferences of users and then use this information to forecast users' future ratings. One of the most common recommendation approaches is the use of matrix factorisation in which users' past ratings of items (i.e. Movies, books, etc.) are used to capture their affinity to implicit factors. A central limitation of such factorisation is that one cannot consider how a user's preferences for a factor have changed over time. In this paper we present the SemanticSVD++ model that overcomes this limitation by using the semantic categories of recommendation items as prior factors for a given user. We present a model to capture the semantic taste evolution of users over time, and demonstrate how such development is susceptible to global influence dynamics. We explain how the SemanticSVD++ model incorporates such evolution information within a matrix factorisation approach, and empirically demonstrate the improvement in predictive capability that this yields when tested on two independent movie recommendation datasets.
AB - Recommender systems profile the preferences of users and then use this information to forecast users' future ratings. One of the most common recommendation approaches is the use of matrix factorisation in which users' past ratings of items (i.e. Movies, books, etc.) are used to capture their affinity to implicit factors. A central limitation of such factorisation is that one cannot consider how a user's preferences for a factor have changed over time. In this paper we present the SemanticSVD++ model that overcomes this limitation by using the semantic categories of recommendation items as prior factors for a given user. We present a model to capture the semantic taste evolution of users over time, and demonstrate how such development is susceptible to global influence dynamics. We explain how the SemanticSVD++ model incorporates such evolution information within a matrix factorisation approach, and empirically demonstrate the improvement in predictive capability that this yields when tested on two independent movie recommendation datasets.
U2 - 10.1109/WI-IAT.2014.36
DO - 10.1109/WI-IAT.2014.36
M3 - Conference contribution/Paper
SN - 9781479941438
SP - 213
EP - 220
BT - Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2014 IEEE/WIC/ACM International Joint Conferences on (Volume:1 )
PB - IEEE
Y2 - 11 August 2014 through 14 August 2014
ER -