Effective neural architectures for context-aware venue recommendation

Users in Location-Based Social Networks (LBSNs), such as Yelp and Foursquare, can search for interesting venues such as restaurants and museums to visit, or share their location with their friends by making an implicit feedback (e.g. checking in at venues they have visited). The users can also leave explicit feedback on the venues they have visited by providing rat- ings and/or comments. Such explicit and implicit feedback by the users provide rich infor- mation about both users and venues, and thus can be leveraged to study the users’ movement in urban cities, as well as enhance the quality of personalised venue recommendations. Un- like traditional recommendation systems (e.g. book and movie recommendation systems), making effective venue recommendations is more challenging because we need to take into account the users’ current context (e.g. time of the day, user’s current location as well as his recently visited venues). Two common techniques that are widely used in the literature for venue recommen- dation systems are Matrix Factorisation (MF) and Bayesian Personalised Ranking (BPR). MF is a popular Collaborative Filtering (CF) technique that can leverage the users’ explicit feedback (e.g. the numerical ratings) to predict the users’ ratings on the venues and hence relevant venues can be suggested to the users based on these predicted ratings. On the other hand, BPR is a pairwise ranking-based model that can leverage implicit feedback to generate effective top-K venue recommendations. In this thesis, based upon MF and BPR models, we aim to generate effective context-aware venue recommendation that a user may wish to visit based on the user’s historical explicit and implicit feedbacks, the user’s contextual informa- tion (e.g. the user’s current location and time of the day) and additional information (e.g. the geographical location of venues and users’ social relationships). To achieve this goal, we need to address the following challenges: namely (C1) modelling the users’ preferences and the characteristic of venues, (C2) capturing the complex structure of user-venue inter- actions in a Collaborative Filtering manner, (C3) modelling the users’ short-term (dynamic) preferences from the sequential order of user’s observed feedback as well as the contextual information associated with the successive feedback, (C4) generating accurate top-K venue recommendations based on the users’ preferences using a pairwise ranking-based model and (C5) appropriately sampling potential negative instances to train a ranking-based model. First, to address challenge C1, we leverage the users’ explicit feedback (e.g. their rat- ings and the textual content of the comments) and additional information (e.g. users’ social relationships) to effectively model the users’ preferences and the characteristics of venues. In particular, we propose a novel regularisation technique and a factorisation-based model that leverages the users’ explicit feedback and the additional information to improve the rat- ing prediction accuracy of the traditional MF model. Experiments conducted on a large scale rating dataset on LBSN demonstrate that the textual content of comments plays an important role in enhancing the accuracy of rating prediction. Second, we investigate how to leverage the users’ implicit feedback and additional in- formation such as the users’ social relationship and the geographical location of venues to improve the quality of top-K venue recommendations. We argue that the potential negative instances can be effectively sampled based on the social correlations between users and their friends as well as the geographical influences between the users’ and venues’ geographi- cal location. In particular, to address challenges C4 and C5, we propose a novel pairwise ranking-based framework for top-K venue recommendations that can incorporate multiple sources of additional information (e.g. the users’ social relationship and the geographical location of venues) to effectively sample the potential negative instances. Experimental re- sults on three large scale checkin and rating datasets from LBSNs demonstrate that the social correlations and the geographical influences play an important role to the quality of sampled negative instances and hence can improve the quality of top-K venue recommendations. Finally, to address challenges C2 and C3, we propose a framework for context-aware venue recommendations that exploits Deep Neural Network (DNN) models to effectively capture the complex structure of user-venue interactions and the users’ long-term (dynamic) preferences from their sequential order of checkins. In particular, within the framework, we propose a novel Recurrent Neural Network (RNN) architecture that can effectively in- corporate the contextual information associated with the successive implicit feedback (e.g. the time interval and the geographical distance between two successive checkins) to gener- ate high quality context-aware venue recommendations. Experimental results on three large scale checkin and rating datasets from LBSNs demonstrate the effectiveness and robustness of our proposed framework for context-aware venue recommendations. In particular, the results demonstrate that the sequential order of users’ implicit feedback can be leveraged to effectively improve the effectiveness of context-aware venue recommendation system. In addition, the time intervals and the geographical distances between two successive checkins play an important role in capturing the users’ short-term preferences

A Deep Recurrent Collaborative Filtering Framework for Venue Recommendation

Venue recommendation is an important application for Location-Based Social Networks (LBSNs), such as Yelp, and has been extensively studied in recent years. 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 tasks such as speech recognition, computer vision and natural language processing. Building upon this momentum, various approaches for recommendation have been proposed in the literature to enhance the effectiveness of MF-based approaches by exploiting neural network models such as: word embeddings to incorporate auxiliary information (e.g. textual content of comments); and Recurrent Neural Networks (RNN) to capture sequential properties of observed user-venue interactions. However, such approaches rely on the traditional inner product of the latent factors of users and venues to capture the concept of collaborative filtering, which may not be sufficient to capture the complex structure of user-venue interactions. In this paper, we propose 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. Our proposed framework consists of two components: namely Generalised Recurrent Matrix Factorisation (GRMF) and Multi-Level Recurrent Perceptron (MLRP) models. In particular, GRMF and MLRP learn to model complex structures of user-venue interactions using element-wise and dot products as well as the concatenation of latent factors. In addition, we propose a novel sequence-based negative sampling approach that accounts for the sequential properties of observed feedback and geographical location of venues to enhance the quality of venue suggestions, as well as alleviate the cold-start users problem. Experiments on three large checkin and rating datasets show the effectiveness of our proposed framework by outperforming various state-of-the-art approaches

Contextual Attention Recurrent Architecture for Context-aware Venue Recommendation

Venue recommendation systems aim to effectively rank a list of interesting venues users should visit based on their historical feedback (e.g. checkins). Such systems are increasingly deployed by Location-based Social Networks (LBSNs) such as Foursquare and Yelp to enhance their usefulness to users. Recently, various RNN architectures have been proposed to incorporate contextual information associated with the users' sequence of checkins (e.g. time of the day, location of venues) to effectively capture the users' dynamic preferences. However, these architectures assume that different types of contexts have an identical impact on the users' preferences, which may not hold in practice. For example, an ordinary context such as the time of the day reflects the user's current contextual preferences, whereas a transition context - such as a time interval from their last visited venue - indicates a transition effect from past behaviour to future behaviour. To address these challenges, we propose 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. Our proposed recurrent architecture consists of two types of gating mechanisms, namely 1) a contextual attention gate that controls the influence of the ordinary context on the users' contextual preferences and 2) a time- and geo-based gate that controls the influence of the hidden state from the previous checkin based on the transition context. Thorough experiments on three large checkin and rating datasets from commercial LBSNs demonstrate the effectiveness of our proposed CARA architecture by significantly outperforming many state-of-the-art RNN architectures and factorisation approaches

Regularising Factorised Models for Venue Recommendation using Friends and their Comments

Venue recommendation is an important capability of Location-Based Social Networks such as Yelp and Foursquare. Matrix Factorisation (MF) is a collaborative filtering-based approach that can effectively recommend venues that are relevant to the users' preferences, by training upon either implicit or explicit feedbacks (e.g. check-ins or venue ratings) that these users express about venues. However, MF suffers in that users may only have rated very few venues. To alleviate this problem, recent literature have leveraged additional sources of evidence, e.g. using users' social friendships to reduce the complexity of - or regularise - the MF model, or identifying similar venues based on 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). We propose a MF regularisation technique that seamlessly incorporates both social network information and textual comments, by exploiting word embeddings to estimate a semantic similarity of friends based on their explicit textual feedback, to regularise the complexity of the factorised model. Experiments on a large existing dataset demonstrate that our proposed regularisation model is promising, and can enhance the prediction accuracy of several state-of-the-art matrix factorisation-based approaches

Deep Collaborative Filtering Approaches for Context-Aware Venue Recommendation

In recent years, vast amounts of user-generated data have being created on Location-Based Social Networks (LBSNs) such as Yelp and Foursquare. Making effective personalised venue suggestions to users based on their preferences and surrounding context is a challenging task. Context-Aware Venue Recommendation (CAVR) is an emerging topic that has gained a lot of attention from researchers, where context can be the user's current location for example. Matrix Factorisation (MF) is one of the most popular collaborative filtering-based techniques, which can be used to predict a user's rating on venues by exploiting explicit feedback (e.g. users' ratings on venues). However, such explicit feedback may not be available, particularly for inactive users, while implicit feedback is easier to obtain from LBSNs as it does not require the users to explicitly express their satisfaction with the venues. In addition, the MF-based approaches usually suffer from the sparsity problem where users/venues have very few rating, hindering the prediction accuracy. Although previous works on user-venue rating prediction have proposed to alleviate the sparsity problem by leveraging user-generated data such as social information from LBSNs, research that investigates the usefulness of Deep Neural Network algorithms (DNN) in alleviating the sparsity problem for CAVR remains untouched or partially studied

Event Organization 101: Understanding Latent Factors of Event Popularity

The problem of understanding people's participation in real-world events has been a subject of active research and can offer valuable insights for human behavior analysis and event-related recommendation/advertisement. In this work, we study the latent factors for determining event popularity using large-scale datasets collected from the popular Meetup.com EBSN in three major cities around the world. We have conducted modeling analysis of four contextual factors (spatial, group, temporal, and semantic), and also developed a group-based social influence propagation network to model group-specific influences on events. By combining the Contextual features And Social Influence NetwOrk, our integrated prediction framework CASINO can capture the diverse influential factors of event participation and can be used by event organizers to predict/improve the popularity of their events. Evaluations demonstrate that our CASINO framework achieves high prediction accuracy with contributions from all the latent features we capture.Comment: International AAAI Conference on Web and Social Media (ICWSM) 2017 https://www.aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/1557