Matrix Factorisation with Word Embeddings for Rating Prediction on Location-Based Social Networks

With vast amounts of data being created on location-based social networks (LBSNs) such as Yelp and Foursquare, making effective personalised suggestions to users is an essential functionality. Matrix Factorisation (MF) is a collaborative filtering-based approach that is widely used to generate suggestions relevant to user’s preferences. In this paper, we address the problem of predicting the rating that users give to venues they visit. Previous works have proposed MF-based approaches that consider auxiliary information (e.g. social information and users’ comments on venues) to improve the accuracy of rating predictions. Such approaches leverage the users’ friends’ preferences, extracted from either ratings or comments, to regularise the complexity of MF-based models and to avoid over-fitting. However, social information may not be available, e.g. due to privacy concerns. To overcome this limitation, in this paper, we propose 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. Experiments conducted on a large dataset of LBSN ratings demonstrate the effectiveness of our proposed approach compared to various state-of-the-art rating prediction 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

Report on the Information Retrieval Festival (IRFest2017)

The Information Retrieval Festival took place in April 2017 in Glasgow. The focus of the workshop was to bring together IR researchers from the various Scottish universities and beyond in order to facilitate more awareness, increased interaction and reflection on the status of the field and its future. The program included an industry session, research talks, demos and posters as well as two keynotes. The first keynote was delivered by Prof. Jaana Kekalenien, who provided a historical, critical reflection of realism in Interactive Information Retrieval Experimentation, while the second keynote was delivered by Prof. Maarten de Rijke, who argued for more Artificial Intelligence usage in IR solutions and deployments. The workshop was followed by a "Tour de Scotland" where delegates were taken from Glasgow to Aberdeen for the European Conference in Information Retrieval (ECIR 2017

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

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 Personalised Ranking Framework with Multiple Sampling Criteria for Venue Recommendation

Recommending a ranked list of interesting venues to users based on their preferences has become a key functionality in Location-Based Social Networks (LBSNs) such as Yelp and Gowalla. Bayesian Personalised Ranking (BPR) is a popular pairwise recommendation technique that is used to generate the ranked list of venues of interest to a user, by leveraging the user's implicit feedback such as their check-ins as instances of positive feedback, while randomly sampling other venues as negative instances. To alleviate the sparsity that affects the usefulness of recommendations by BPR for users with few check-ins, various approaches have been proposed in the literature to incorporate additional sources of information such as the social links between users, the textual content of comments, as well as the geographical location of the venues. However, such approaches can only readily leverage one source of additional information for negative sampling. Instead, we propose a novel Personalised Ranking Framework with Multiple sampling Criteria (PRFMC) that leverages both geographical influence and social correlation to enhance the effectiveness of BPR. In particular, we apply a multi-centre Gaussian model and a power-law distribution method, to capture geographical influence and social correlation when sampling negative venues, respectively. Finally, we conduct comprehensive experiments using three large-scale datasets from the Yelp, Gowalla and Brightkite LBSNs. The experimental results demonstrate the effectiveness of fusing both geographical influence and social correlation in our proposed PRFMC framework and its superiority in comparison to BPR-based and other similar ranking approaches. Indeed, our PRFMC approach attains a 37% improvement in MRR over a recently proposed approach that identifies negative venues only from social links

Leveraging Node Attributes for Incomplete Relational Data

Relational data are usually highly incomplete in practice, which inspires us to leverage side information to improve the performance of community detection and link prediction. This paper presents a Bayesian probabilistic approach that incorporates various kinds of node attributes encoded in binary form in relational models with Poisson likelihood. Our method works flexibly with both directed and undirected relational networks. The inference can be done by efficient Gibbs sampling which leverages sparsity of both networks and node attributes. Extensive experiments show that our models achieve the state-of-the-art link prediction results, especially with highly incomplete relational data.Comment: Appearing in ICML 201