Joint Geo-Spatial Preference and Pairwise Ranking for Point-of-Interest Recommendation

Recommending users with preferred point-of-interests (POIs) has become an important task for location-based social networks, which facilitates users' urban exploration by helping them filter out unattractive locations. Although the influence of geographical neighborhood has been studied in the rating prediction task (i.e. regression), few work have exploited it to develop a ranking-oriented objective function to improve top-N item recommendations. To solve this task, we conduct a manual inspection on real-world datasets, and find that each individual's traits are likely to cluster around multiple centers. Hence, we propose a co-pairwise ranking model based on the assumption that users prefer to assign higher ranks to the POIs near previously rated ones. The proposed method can learn preference ordering from non-observed rating pairs, and thus can alleviate the sparsity problem of matrix factorization. Evaluation on two publicly available datasets shows that our method performs significantly better than state-of-the-art techniques for the top-N item recommendation task

Context-Aware Personalized Point-of-Interest Recommendation System

The increasing volume of information has created overwhelming challenges to extract the relevant items manually. Fortunately, the online systems, such as e-commerce (e.g., Amazon), location-based social networks (LBSNs) (e.g., Facebook) among many others have the ability to track end users\u27 browsing and consumption experiences. Such explicit experiences (e.g., ratings) and many implicit contexts (e.g., social, spatial, temporal, and categorical) are useful in preference elicitation and recommendation. As an emerging branch of information filtering, the recommendation systems are already popular in many domains, such as movies (e.g., YouTube), music (e.g., Pandora), and Point-of-Interest (POI) (e.g., Yelp). The POI domain has many contextual challenges (e.g., spatial (preferences to a near place), social (e.g., friend\u27s influence), temporal (e.g., popularity at certain time), categorical (similar preferences to places with same category), locality of POI, etc.) that can be crucial for an efficient recommendation. The user reviews shared across different social networks provide granularity in users\u27 consumption experience. From the data mining and machine learning perspective, following three research directions are identified and considered relevant to an efficient context-aware POI recommendation, (1) incorporation of major contexts into a single model and a detailed analysis of the impact of those contexts, (2) exploitation of user activity and location influence to model hierarchical preferences, and (3) exploitation of user reviews to formulate the aspect opinion relation and to generate explanation for recommendation. This dissertation presents different machine learning and data mining-based solutions to address the above-mentioned research problems, including, (1) recommendation models inspired from contextualized ranking and matrix factorization that incorporate the major contexts and help in analysis of their importance, (2) hierarchical and matrix-factorization models that formulate users\u27 activity and POI influences on different localities that model hierarchical preferences and generate individual and sequence recommendations, and (3) graphical models inspired from natural language processing and neural networks to generate recommendations augmented with aspect-based explanations

Time Distribution based Diversified Point of Interest Recommendation

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Joint Geographical and Temporal Modeling based on Matrix Factorization for Point-of-Interest Recommendation

With the popularity of Location-based Social Networks, Point-of-Interest (POI) recommendation has become an important task, which learns the users' preferences and mobility patterns to recommend POIs. Previous studies show that incorporating contextual information such as geographical and temporal influences is necessary to improve POI recommendation by addressing the data sparsity problem. However, existing methods model the geographical influence based on the physical distance between POIs and users, while ignoring the temporal characteristics of such geographical influences. In this paper, we perform a study on the user mobility patterns where we find out that users' check-ins happen around several centers depending on their current temporal state. Next, we propose a spatio-temporal activity-centers algorithm to model users' behavior more accurately. Finally, we demonstrate the effectiveness of our proposed contextual model by incorporating it into the matrix factorization model under two different settings: i) static and ii) temporal. To show the effectiveness of our proposed method, which we refer to as STACP, we conduct experiments on two well-known real-world datasets acquired from Gowalla and Foursquare LBSNs. Experimental results show that the STACP model achieves a statistically significant performance improvement, compared to the state-of-the-art techniques. Also, we demonstrate the effectiveness of capturing geographical and temporal information for modeling users' activity centers and the importance of modeling them jointly.Comment: To be appear in ECIR 202

Where could we go? Recommendations for groups in location-based social networks

| openaire: EC/H2020/654024/EU//SoBigDataLocation-Based Social Networks (LBSNs) enable their users to share with their friends the places they go to and whom they go with. Additionally, they provide users with recommendations for Points of Interest (POI) they have not visited before. This functionality is of great importance for users of LBSNs, as it allows them to discover interesting places in populous cities that are not easy to explore. For this reason, previous research has focused on providing recommendations to LBSN users. Nevertheless, while most existing work focuses on recommendations for individual users, techniques to provide recommendations to groups of users are scarce. In this paper, we consider the problem of recommending a list of POIs to a group of users in the areas that the group frequents. Our data consist of activity on Swarm, a social networking app by Foursquare, and our results demonstrate that our proposed Geo-Group-Recommender (GGR), a class of hybrid recommender systems that combine the group geographical preferences using Kernel Density Estimation, category and location features and group check-ins outperform a large number of other recommender systems. Moreover, we find evidence that user preferences differ both in venue category and in location between individual and group activities. We also show that combining individual recommendations using group aggregation strategies is not as good as building a profile for a group. Our experiments show that (GGR) outperforms the baselines in terms of precision and recall at different cutoffs.Peer reviewe