Modelling User Preferences using Word Embeddings for Context-Aware Venue Recommendation

Venue recommendation aims to assist users by making personalised suggestions of venues to visit, building upon data available from location-based social networks (LBSNs) such as Foursquare. A particular challenge for this task is context-aware venue recommendation (CAVR), which additionally takes the surrounding context of the user (e.g. the user’s location and the time of day) into account in order to provide more relevant venue suggestions. To address the challenges of CAVR, we describe two approaches that exploit word embedding techniques to infer the vector-space representations of venues, users’ existing preferences, and users’ contextual preferences. Our evaluation upon the test collection of the TREC 2015 Contextual Suggestion track demonstrates that we 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 TREC 2015 systems

Modelling User Preferences using Word Embeddings for Context-Aware Venue Recommendation

Venue recommendation aims to assist users by making personalised suggestions of venues to visit, building upon data available from location-based social networks (LBSNs) such as Foursquare. A particular challenge for this task is context-aware venue recommendation (CAVR), which additionally takes the surrounding context of the user (e.g. the user’s location and the time of day) into account in order to provide more relevant venue suggestions. To address the challenges of CAVR, we describe two approaches that exploit word embedding techniques to infer the vector-space representations of venues, users’ existing preferences, and users’ contextual preferences. Our evaluation upon the test collection of the TREC 2015 Contextual Suggestion track demonstrates that we 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 TREC 2015 systems

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

Suggestion contextuelle composite

International audienceLa suggestion contextuelle consiste à recommander à un utilisateur un ensemble de lieux d'activités adaptés à ses préférences et à son contexte. La plupart des approches existantes considèrent uniquement ces deux caractéristiques pour constituer leur liste de suggestions. Cependant, les recherches en systèmes de recommandation ont récemment souligné l'importance de la diversité des suggestions. Cet article présente un modèle novateur de suggestion contextuelle inspiré de la recherche composite qui consiste à regrouper les suggestions en différentes grappes thématiquement cohésives. L'évaluation réalisée dans le cadre de la piste Contextual Suggestion de TREC 2013 et 2014 montre que notre approche est compétitive et permet d'améliorer la diversité des suggestions sans dégrader leur pertinence

Experiments with a Venue-Centric Model for Personalisedand Time-Aware Venue Suggestion

No abstract available

On the Importance of Venue-Dependent Features for Learning to Rank Contextual Suggestions

Suggesting venues to a user in a given geographic context is an emerging task that is currently attracting a lot of attention. Existing studies in the literature consist of approaches that rank candidate venues based on different features of the venues and the user, which either focus on modeling the preferences of the user or the quality of the venue. However, while providing insightful results and conclusions, none of these studies have explored the relative effectiveness of these different features. In this paper, we explore a variety of user-dependent and venue-dependent features and apply state-of-the-art learning to rank approaches to the problem of contextual suggestion in order to find what makes a venue relevant for a given context. Using the test collection of the TREC 2013 Contextual Suggestion track, we perform a number of experiments to evaluate our approach. Our results suggest that a learning to rank technique can significantly outperform a Language Modelling baseline that models the positive and negative preferences of the user. Moreover, despite the fact that the contextual suggestion task is a personalisation task (i.e. providing the user with personalised suggestions of venues), we surprisingly find that user-dependent features are less effective than venue-dependent features for estimating the relevance of a suggestion

Recommending Structured Objects: Paths and Sets

Recommender systems have been widely adopted in industry to help people find the most appropriate items to purchase or consume from the increasingly large collection of available resources (e.g., books, songs and movies). Conventional recommendation techniques follow the approach of ``ranking all possible options and pick the top'', which can work effectively for single item recommendation but fall short when the item in question has internal structures. For example, a travel trajectory with a sequence of points-of-interest or a music playlist with a set of songs. Such structured objects pose critical challenges to recommender systems due to the intractability of ranking all possible candidates. This thesis study the problem of recommending structured objects, in particular, the recommendation of path (a sequence of unique elements) and set (a collection of distinct elements). We study the problem of recommending travel trajectories in a city, which is a typical instance of path recommendation. We propose methods that combine learning to rank and route planning techniques for efficient trajectory recommendation. Another contribution of this thesis is to develop the structured recommendation approach for path recommendation by substantially modifying the loss function, the learning and inference procedures of structured support vector machines. A novel application of path decoding techniques helps us achieve efficient learning and recommendation. Additionally, we investigate the problem of recommending a set of songs to form a playlist as an example of the set recommendation problem. We propose to jointly learn user representations by employing the multi-task learning paradigm, and a key result of equivalence between bipartite ranking and binary classification enables efficient learning of our set recommendation method. Extensive evaluations on real world datasets demonstrate the effectiveness of our proposed approaches for path and set recommendation