Efficiently leveraging multi-level user intent for session-based recommendation via atten-mixer network
Session-based recommendation (SBR) aims to predict the user's next action based on short
and dynamic sessions. Recently, there has been an increasing interest in utilizing various …
and dynamic sessions. Recently, there has been an increasing interest in utilizing various …
Price does matter! modeling price and interest preferences in session-based recommendation
Session-based recommendation aims to predict items that an anonymous user would like to
purchase based on her short behavior sequence. The current approaches towards session …
purchase based on her short behavior sequence. The current approaches towards session …
Pessimistic reward models for off-policy learning in recommendation
O Jeunen, B Goethals - Proceedings of the 15th ACM Conference on …, 2021 - dl.acm.org
Methods for bandit learning from user interactions often require a model of the reward a
certain context-action pair will yield–for example, the probability of a click on a …
certain context-action pair will yield–for example, the probability of a click on a …
Dynamic intent-aware iterative denoising network for session-based recommendation
Session-based recommendation aims to predict items that a user will interact with based on
historical behaviors in anonymous sessions. It has long faced two challenges:(1) the …
historical behaviors in anonymous sessions. It has long faced two challenges:(1) the …
Multi-faceted global item relation learning for session-based recommendation
As an emerging paradigm, session-based recommendation is aimed at recommending the
next item based on a set of anonymous sessions. Effectively representing a session that is …
next item based on a set of anonymous sessions. Effectively representing a session that is …
Pessimistic decision-making for recommender systems
O Jeunen, B Goethals - ACM Transactions on Recommender Systems, 2023 - dl.acm.org
Modern recommender systems are often modelled under the sequential decision-making
paradigm, where the system decides which recommendations to show in order to maximise …
paradigm, where the system decides which recommendations to show in order to maximise …
S-Walk: Accurate and scalable session-based recommendation with random walks
Session-based recommendation (SR) predicts the next items from a sequence of previous
items consumed by an anonymous user. Most existing SR models focus only on modeling …
items consumed by an anonymous user. Most existing SR models focus only on modeling …
Item similarity mining for multi-market recommendation
Real-world web applications such as Amazon and Netflix often provide services in multiple
countries and regions (ie, markets) around the world. Generally, different markets share …
countries and regions (ie, markets) around the world. Generally, different markets share …
Session‐Based Recommendation with GNN and Time‐Aware Memory Network
Y Wen, S Kang, Q Zeng, H Duan… - Mobile Information …, 2022 - Wiley Online Library
The goal of the session‐based recommendation system (SBRS) is to predict the user's next
behavior based on anonymous sessions. Since long‐term historical information of users is …
behavior based on anonymous sessions. Since long‐term historical information of users is …
Offline approaches to recommendation with online success
O Jeunen - 2021 - repository.uantwerpen.be
Recommender systems are information retrieval applications that provide users with
algorithmic recommendations, in order to assist decision-making when sufficient knowledge …
algorithmic recommendations, in order to assist decision-making when sufficient knowledge …