Social Regularisation in a BPR-based Venue Recommendation Systems

No abstract available

Social Regularisation in a BPR-based Venue Recommendation Systems

No abstract available

Enhancing Graph Neural Networks for Recommender Systems

Recommender systems lie at the heart of many online services such as E-commerce, social media platforms and advertising. To keep users engaged and satisfied with the displayed items, recommender systems usually use the users' historical interactions containing their interests and purchase habits to make personalised recommendations. Recently, Graph Neural Networks (GNNs) have emerged as a technique that can effectively learn representations from structured graph data. By treating the traditional user-item interaction matrix as a bipartite graph, many existing graph-based recommender systems (GBRS) have been shown to achieve state-of-the-art performance when employing GNNs. However, the existing GBRS approaches still have several limitations, which prevent the GNNs from achieving their full potential. In this work, we propose to enhance the performance of the GBRS approaches along several research directions, namely leveraging additional items and users' side information, extending the existing undirected graphs to account for social influence among users, and enhancing their underlying optimisation criterion. In the following, we describe these proposed research directions

A contextual recurrent collaborative filtering framework for modelling sequences of venue checkins

Context-Aware Venue Recommendation (CAVR) systems aim to effectively generate a ranked list of interesting venues users should visit based on their historical feedback (e.g. checkins) and context (e.g. the time of the day or the user’s current location). Such systems are increasingly deployed by Location-based Social Networks (LBSNs) such as Foursquare and Yelp to enhance the satisfaction of the users. Matrix Factorisation (MF) is a popular Collaborative Filtering (CF) technique that can suggest relevant venues to users based on an assumption that similar users are likely to visit similar venues. In recent years, deep neural networks have been successfully applied to recommendation systems. Indeed, various approaches have been previously proposed in the literature to enhance the effectiveness of MF-based approaches by exploiting Recurrent Neural Networks (RNN) models to capture the sequential properties of observed checkins. Moreover, recently, several RNN architectures have been proposed to incorporate contextual information associated with the users’ sequence of checkins (for instance, the time interval or the geographical distance between two successive checkins) to effectively capture such short-term preferences of users. In this work, we propose a Contextual Recurrent Collaborative Filtering framework (CRCF) that leverages the users’ preferred context and the contextual information associated with the users’ sequence of checkins in order to model the users’ short-term preferences for CAVR. In particular, the CRCF framework is built upon two state-of-the-art approaches: namely Deep Recurrent Collaborative Filtering framework (DRCF) and Contextual Attention Recurrent Architecture (CARA). Thorough experiments on three large checkin and rating datasets from commercial LBSNs demonstrate the effectiveness and robustness of our proposed CRCF framework by significantly outperforming various state-of-the-art matrix factorisation approaches. In particular, the CRCF framework significantly improves NDCG@10 by 5–20% over the state-of-the-art DRCF framework (Manotumruksa, Macdonald, and Ounis, 2017a) and the CARA architecture (Manotumruksa, Macdonald, and Ounis, 2018) across the three datasets. Furthermore, the CRCF framework is less significantly risky than both the DRCF framework and the CARA architecture across the three datasets