About The Talk Personalized recommendation, whose objective is to generate a limited list of items (e.g., products on Amazon, movies on Netflix, or pins on Pinterest, etc.) for each user, has gained extensive attention from both researchers and practitioners in the last decade. The necessity of personalized recommendation is driven by the explosion of available options online, which makes it difficult, if not downright impossible, for users to investigate every option. Product and service providers rely on recommendation algorithms to identify manageable number of the most likely or preferred options to be presented to each user. Also, due to the limited screen estate of computing devices, this manageable number maybe relatively small, yet the selection of items to be recommended are personalized to each individual users. The basic entities of a personalized recommendation systems are items and users. Personalization can be achieved through custom alternatives for delivering the right experience to the right user at the right time on the right device. Therefore, personalized recommendation can appear in many forms, depending on the characteristics of the items and the desired experience that the system want users to have. In this thesis, we encompass two perspectives of personalized recommendation: preference learning and similarity learning. The former refers to the personalization on which the recommendation is tailored towards users' preference. The latter, on the other hand, refers to personalization approach in which recommendation is generated based on the users' personal perceptions of similarity between the items. In the preference learning perspective, we focus on the task of retrieving recommendations efficiently and propose two techniques for this objective. Both proposed techniques aim at producing vector representation for users and items that natively supports efficient retrieval via indexing strategies. Extensive experiments on publicly available datasets show significant improvement of proposed methods over the baselines. In the similarity learning perspective, we are interested in the setting where there are multiple similarity perceptions in the data. Towards modelling these perceptions effectively, we propose a multiperspective graph-theoretic framework that yields a similarity measure for any pair of objects for a perspective. Experimental results showcase the utility of multiperspective modelling compared to uniperspective methods. |
Speaker Biography LE Duy Dung is a PhD candidate in the Information Systems program at Singapore Management University (SMU). Formerly, he earned his Degree of Engineer in Mathematics and Informatics from Hanoi University of Science and Technology, in 2014. His research interests include recommender systems, information retrieval, and visual analytics, with publications in major data mining venues such as CIKM and SDM. |
