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A Contextual Attention Recurrent Architecture for Context-Aware Venue Recommendation
This work proposes a novel Contextual Attention Recurrent Architecture (CARA) that leverages both sequences of feedback and contextual information associated with the sequences to capture the users' dynamic preferences and significantly outperforms many state-of-the-art RNN architectures and factorisation approaches.
MultiWOZ 2.4: A Multi-Domain Task-Oriented Dialogue Dataset with Essential Annotation Corrections to Improve State Tracking Evaluation
This work introduces MultiWOZ 2.4, which reﬁnes the annotations in the validation set and test set of Multi woz 2.1 to elicit robust and noise-resilient model training.
A Deep Recurrent Collaborative Filtering Framework for Venue Recommendation
A Deep Recurrent Collaborative Filtering framework (DRCF) with a pairwise ranking function that aims to capture user-venue interactions in a CF manner from sequences of observed feedback by leveraging Multi-Layer Perception and Recurrent Neural Network architectures is proposed.
Modelling User Preferences using Word Embeddings for Context-Aware Venue Recommendation
Evaluation upon the test collection of the TREC 2015 Contextual Suggestion track demonstrates that this work can significantly enhance the effectiveness of a state-of-the-art venue recommendation approach, as well as produce context-aware recommendations that are at least as effective as the top TREC2015 systems.
Slot Self-Attentive Dialogue State Tracking
- Fanghua Ye, Jarana Manotumruksa, Qiang Zhang, Shenghui Li, Emine Yilmaz
- Computer ScienceWWW
- 22 January 2021
This paper proposes a slot self-attention mechanism that can learn the slot correlations automatically, and achieves state-of-the-art performance on both datasets, verifying the necessity and effectiveness of taking slot correlations into consideration.
Matrix Factorisation with Word Embeddings for Rating Prediction on Location-Based Social Networks
This paper proposes a novel MF-based approach that exploits word embeddings to effectively model users’ preferences and the characteristics of venues from the textual content of comments left by users, regardless of their relationship.
Regularising Factorised Models for Venue Recommendation using Friends and their Comments
This paper argues for a combined regularisation model, where the venues suggested for a user are influenced by friends with similar tastes (as defined by their comments), and proposes a MF regularisation technique that seamlessly incorporates both social network information and textual comments.
Sequential-based Adversarial Optimisation for Personalised Top-N Item Recommendation
A Sequential-based Adversarial Optimisation (SAO) framework is proposed that effectively enhances the generalisation of sequential-based factorised approaches and demonstrates the effectiveness of the SAO framework in enhancing the performance of the state-of-the-art sequential- based factorised approach in terms of NDCG.
University of Glasgow at TREC 2015: Experiments with Terrier in Contextual Suggestion, Temporal Summarisation and Dynamic Domain Tracks
In TREC 2015, this work investigates the use of user-generated data in location-based social networks to suggest venues and examines features for event summarisation that explicitly model the entities involved in the events.
On Cross-Domain Transfer in Venue Recommendation
It is demonstrated that state-of-the-art cross-domain recommendation does not clearly contribute to the improvements of venue recommendation systems, and this result is validated on the latest sequential deep learning-based venue recommendation approach.