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

Privacy-preserving distributed service recommendation based on locality-sensitive hashing

With the advent of IoT (Internet of Things) age, considerable web services are emerging rapidly in service communities, which places a heavy burden on the target users’ service selection decisions. In this situation, various techniques, e.g., collaborative filtering (i.e., CF) is introduced in service recommendation to alleviate the service selection burden. However, traditional CF-based service recommendation approaches often assume that the historical user-service quality data is centralized, while neglect the distributed recommendation situation. Generally, distributed service recommendation involves inevitable message communication among different parties and hence, brings challenging efficiency and privacy concerns. In view of this challenge, a novel privacy-preserving distributed service recommendation approach based on Locality-Sensitive Hashing (LSH), i.e., DistSRLSH is put forward in this paper. Through LSH, DistSRLSH can achieve a good tradeoff among service recommendation accuracy, privacy-preservation and efficiency in distributed environment. Finally, through a set of experiments deployed on WS-DREAM dataset, we validate the feasibility of our proposal in handling distributed service recommendation problems

Reliable Collaborative Filtering on Spatio-Temporal Privacy Data

Lots of multilayer information, such as the spatio-temporal privacy check-in data, is accumulated in the location-based social network (LBSN). When using the collaborative filtering algorithm for LBSN location recommendation, one of the core issues is how to improve recommendation performance by combining the traditional algorithm with the multilayer information. The existing approaches of collaborative filtering use only the sparse user-item rating matrix. It entails high computational complexity and inaccurate results. A novel collaborative filtering-based location recommendation algorithm called LGP-CF, which takes spatio-temporal privacy information into account, is proposed in this paper. By mining the users check-in behavior pattern, the dataset is segmented semantically to reduce the data size that needs to be computed. Then the clustering algorithm is used to obtain and narrow the set of similar users. User-location bipartite graph is modeled using the filtered similar user set. Then LGP-CF can quickly locate the location and trajectory of users through message propagation and aggregation over the graph. Through calculating users similarity by spatio-temporal privacy data on the graph, we can finally calculate the rating of recommendable locations. Experiments results on the physical clusters indicate that compared with the existing algorithms, the proposed LGP-CF algorithm can make recommendations more accurately

Itinerary Recommendation Algorithm in the Age of MEC

To provide fully immersive mobile experiences, next-generation touristic services will rely on the high bandwidth and low latency provided by the 5G networks and the Multi-access Edge Computing (MEC) paradigm. Recommendation algorithms, being integral part of travel planning systems, devise personalized tour itineraries for a user considering the popularity of the Points of Interest (POIs) of a city as well as the tourist preferences and constraints. However, in the context of next-generation touristic services, recommendation algorithms should also consider the applications (e.g., augmented reality) the tourist will consume in the POIs and the quality in which such applications will be delivered by the MEC infrastructure. In this paper, we address the joint problem of recommending personalized tour itineraries for tourists and efficiently allocating MEC resources for advanced touristic applications. We formulate an optimization problem that maximizes the itinerary score of individual tourists, while optimizing the resource allocation at the network edge. We then propose an exact algorithm that quickly solves the problem optimally considering instances of realistic size. Finally, we evaluate our algorithm using a real dataset extracted from Flickr. Results demonstrate gains up to 100% in the resource allocation and user experience in comparison with a state-of-the-art solution

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