The point-of-interest (POI) recommender exploits check-in information from location-based social networks (LBSNs) to recommend POIs that match user preferences. The preferences of users vary with region/locality, consumption type, and co-consumers. For instance, the places preferred for friends may be different from the places preferred for family, and the places preferred in one locality may be different from the places in another locality. These dynamic preferences can be crucial for an efficient recommender system. A locality consists of different preference trends, such as "known for food and recreational sites." In real-world, different sets of users might be attracted toward different preference trends, and some users' preferences might overlap across multiple preference sets. Hence, an efficient aggregation of locality preferences is essential for the recommender systems. Many existing studies simply group items by their category and use simple collaborative filtering (CF) for recommendations, however, such techniques cannot efficiently handle the aggregated locality preferences. We propose a hierarchical recommendation model termed Hierarchical Contextual Location Recommendation System (HiRecS) that formulates users' preferences as a hierarchical structure and models the locality trend using aggregated hierarchy. For a locality, the root of hierarchy contains preferred k items from a set of visitors, and the subsequent levels contain preference wise subsets of those items. We also present a hierarchy aggregation technique to aggregate the hierarchical preferences from a similar set of users. The aggregated hierarchy is then contextually exploited for POI sequence recommendation. The core contributions of this article are: 1) it formulates the locality trends as hierarchical structures, presents a hierarchy aggregation technique, and models the personalized POI preferences with the aggregated hierarchy; 2) it exploits the aggregated popular trends to generate contextual POI sequence recommendation; and 3) it extensively evaluates the proposed model with two real-world data sets and demonstrates a significant performance gain of 0.006-5.91 on diversity metrics, 0.0349-17.51 on displacement metrics, and 0.114-0.289 on normalized discounted cumulative gain (NDCG) metrics, when compared to several baselines and relevant studies.

The location-based social networks (LBSN) (e.g., Facebook, etc.) have been explored in the past decade for Point-of-Interest (POI) recommendation. Many of the existing systems focus on recommending a single location or a list which might not be contextually coherent. In this paper, we propose a model termed CLoSe (Contextualized Location Sequence Recommender) that generates contextually coherent POI sequences relevant to user preferences. The POI sequence recommenders are helpful in many day-to-day activities, for e.g., itinerary planning, etc. To the best of our knowledge, this paper is the first to formulate contextual POI sequence recommendation by exploiting Recurrent Neural Network (RNN). We incorporate check-in contexts to the hidden layer and global context to the hidden and output layers of RNN. We also demonstrate the efficiency of extended Long-short term memory (LSTM) in sequence generation. The main contributions of this paper are: (i) it exploits multi-context, personalized user preferences to formulate contextual POI sequence generation, (ii) it presents contextual extensions of RNN and LSTM that incorporate different contexts applicable to a POI and POI sequence, and (iii) it demonstrates significant performance gain of proposed model on pair-F1 and NDCG metrics when evaluated with two real-world datasets.

Adversarial machine learning in the context of image processing and related applications has received a large amount of attention. However, adversarial machine learning, especially adversarial deep learning, in the context of malware detection has received much less attention despite its apparent importance. In this paper, we present a framework for enhancing the robustness of Deep Neural Networks (DNNs) against adversarial malware samples, dubbed Hashing Transformation Deep Neural Networks} (HashTran-DNN). The core idea is to use hash functions with a certain locality-preserving property to transform samples to enhance the robustness of DNNs in malware classification. The framework further uses a Denoising Auto-Encoder (DAE) regularizer to reconstruct the hash representations of samples, making the resulting DNN classifiers capable of attaining the locality information in the latent space. We experiment with two concrete instantiations of the HashTran-DNN framework to classify Android malware. Experimental results show that four known attacks can render standard DNNs useless in classifying Android malware, that known defenses can at most defend three of the four attacks, and that HashTran-DNN can effectively defend against all of the four attacks.

The revolution of World Wide Web (WWW) and smart-phone technologies have been the key-factor behind remarkable success of social networks. With the ease of availability of check-in data, the location-based social networks (LBSN) (e.g., Facebook1, etc.) have been heavily explored in the past decade for Point-of-Interest (POI) recommendation. Though many POI recommenders have been defined, most of them have focused on recommending a single location or an arbitrary list that is not contextually coherent. It has been cumbersome to rely on such systems when one needs a contextually coherent list of locations, that can be used for various day-to-day activities, for e.g., itinerary planning. This paper proposes a model termed as CAPS (Context-Aware Personalized POI Sequence Recommender System) that generates contextually coherent POI sequences relevant to user preferences. To the best of our knowledge, CAPS is the first attempt to formulate the contextual POI sequence modeling by extending Recurrent Neural Network (RNN) and its variants. CAPS extends RNN by incorporating multiple contexts to the hidden layer and by incorporating global context (sequence features) to the hidden layers and the output layer. It extends the variants of RNN (e.g., Long-short term memory (LSTM)) by incorporating multiple contexts and global features in the gate update relations. The major contributions of this paper are: (i) it models the contextual POI sequence problem by incorporating personalized user preferences through multiple constraints (e.g., categorical, social, temporal, etc.), (ii) it extends RNN to incorporate the contexts of individual item and that of the whole sequence. It also extends the gated functionality of variants of RNN to incorporate the multiple contexts, and (iii) it evaluates the proposed models against two real-world data sets.

The evolution of World Wide Web (WWW) and the smart-phone technologies have revolutionized our daily life. This has facilitated the emergence of many useful systems, such as Location-based Social Networks (LBSN) which have provisioned many factors that are crucial for selection of Point-of-Interests (POI). Some of the major factors are: (i) the location attributes, such as geo-coordinates, category, and check-in time, (ii) the user attributes, such as, comments, tips, reviews, and ratings made to the locations, and (iii) other information, such as the distance of the POI from user’s house/office, social tie between users, and so forth. Careful selection of such factors can have significant impact on the efficiency of POI recommendation. In this paper, we define and analyze the fusion of different major aspects in POI recommendation. Such a fusion and analysis is barely explored by other researchers. The major contributions of this paper are: (i) it analyzes the role of different aspects (e.g., check-in frequency, social, temporal, spatial, and categorical) in the location recommendation, (ii) it proposes two fused models—a ranking-based, and a matrix factorization-based, that incorporate all the major aspects into a single recommendation model, and (iii) it evaluates the proposed models against two real-world datasets.

The Point-of-Interest (POI) preference of a user varies by locality, item type, and the co-visitors, e.g., user 1 and user 2 can have closest preference on food items but not on historic sites, etc. A locality can have different preference trends (e.g., popular for food, recreation, etc.) and a user's preference can span across multiple such trends. A good recommender should also exploit the aggregated locality preference trends. Most of the existing studies group items by category or global user preferences which might not be relevant for locality-based aggregated preferences. We propose HiCaPS ( Hi erarchical C ontextual P OI S equence Recommender) that formulates user preferences as hierarchical structure and presents a hierarchy aggregation technique for POI recommendation. The top level of locality hierarchy contains preferred k items from a set of users and the subsequent levels contain preference wise subsets. The core contributions of this paper are: (i) it formulates user preferences as a preference hierarchy, presents a technique to aggregate preference hierarchies of a similar users, and models the target users' preference in terms of aggregated trend in a locality, (ii) it contextually exploits the aggregated trend to generate personalized POI sequences, and (iii) it extensively evaluates the proposed model with two real-world datasets and demonstrates performance gain (0.03 - 0.28 on pair F-score, 0.006 - 5.91 on diversity, 0.0349 - 17.51 on displacement, and 0.114 - 0.289 on NDCG) over baseline models.

The Location-Based Social Networks (LBSN) (e.g., Facebook, etc.) have many attributes (e.g., ratings, reviews, etc.) that play a crucial role for the Point-of-Interest (POI) recommendations. Unlike ratings, the reviews can help users to elaborate their consumption experience in terms of relevant factors of interest (aspects). Though some of the existing systems have exploited user reviews, most of them are less transparent and non-interpretable (as they conceal the reason behind recommendation). These reasons have motivated us towards explainable and interpretable recommendation. To the best of our knowledge, only a few of the researchers have exploited user reviews to incorporate the sentiment and opinions on different aspects for personalized and explainable POI recommendation. This paper proposes a model termed as ReEL (Review aware Explanation of Location Recommendation) which models the review-aspect correlation by exploiting deep neural network, formulates user-aspect bipartite relation as a bipartite graph, and models the explainable recommendation by using dense subgraph extraction and ranking-based techniques. The major contributions of this paper are: (i) it models users and POIs using the aspects posted on user reviews, and it provisions incorporation of multiple contexts (e.g., categorical, spatial, etc.) in POI recommendation, (ii) it formulates preference of users' on aspects as a bipartite relation, represents it as a location-aspect bipartite graph, and models the explainable recommendation with the notion of ordered dense subgraph extraction using bipartite cores, shingles, and ranking-based techniques, and (iii) it extensively evaluates the proposed models using three real-world datasets and demonstrates an improvement of 5.8% to 29.5% on F-score metric, when compared to the relevant studies.

In large scale and complex IT service environments, a problematic incident is logged as a ticket and contains the ticket summary (system status and problem description). The system administrators log the step-wise resolution description when such tickets are resolved. The repeating service events are most likely resolved by inferring similar historical tickets. With the availability of reasonably large ticket datasets, we can have an automated system to recommend the best matching resolution for a given ticket summary.In this paper, we first identify the challenges in real-world ticket analysis and develop an integrated framework to efficiently handle those challenges. The framework first quantifies the quality of ticket resolutions using a regression model built on carefully designed features. The tickets, along with their quality scores obtained from the resolution quality quantification, are then used to train a deep neural network ranking model that outputs the matching scores of ticket summary and resolution pairs. This ranking model allows us to leverage the resolution quality in historical tickets when recommending resolutions for an incoming incident ticket. In addition, the feature vectors derived from the deep neural ranking model can be effectively used in other ticket analysis tasks, such as ticket classification and clustering. The proposed framework is extensively evaluated with a large real-world dataset.

The Location-Based Social Networks (LBSN) (e.g., Facebook) have many factors (for instance, ratings, check-in time, etc.) that play a crucial role for the Point-of-Interest (POI) recommendations. Unlike ratings, the reviews can help users to elaborate their opinion and share the extent of consumption experience in terms of the relevant factors of interest (aspects). Though some of the existing recommendation systems have been using the user reviews, most of them are less transparent and non-interpretable. These reasons have induced considerable attention towards explainable and interpretable recommendation. To the best of our knowledge, this is the first paper to exploit the user reviews to incorporate the sentiment and opinions on different aspects for the personalized and explainable POI recommendation. In this paper, we propose a model termed as PERS (Personalized Explainable POI Recommender System) which models the review-aspect category correlation by exploiting deep neural network, formulates the user-aspect category bipartite relation as a bipartite graph, and models the explainable recommendation using bipartite core-based and ranking-based methods. The major contributions of this paper are: (i) it models users and locations based on the aspects posted by user via reviews, (ii) it exploits a deep neural network to model the review-aspect category correlation, (iii) it provisions the incorporation of multiple contexts (e.g., categorical, spatial, etc.) in the POI recommendation model, (iv) it formulates the preference of users' on aspect category as a bipartite relation, represents it as a location-aspect category bipartite graph, and models the explainable recommendation with the notion of ordered dense subgraph extraction using bipartite core-based and ranking-based approaches, and (v) it evaluates the generated recommendation with three real-world datasets.

The evolution of the World Wide Web (WWW) and the smart-phone technologies have played a key role in the revolution of our daily life. The location-based social networks (LBSN) have emerged and facilitated the users to share the check-in information and multimedia contents. The Point of Interest (POI) recommendation system uses the check-in information to predict the most potential check-in locations. The different aspects of the check-in information, for instance, the geographical distance, the category, and the temporal popularity of a POI; and the temporal check-in trends, and the social (friendship) information of a user play a crucial role in an efficient recommendation. In this paper, we propose a fused recommendation model termed MAPS (Multi Aspect Personalized POI Recommender System) which will be the first in our knowledge to fuse the categorical, the temporal, the social and the spatial aspects in a single model. The major contribution of this paper are: (i) it realizes the problem as a graph of location nodes with constraints on the category and the distance aspects (i.e. the edge between two locations is constrained by a threshold distance and the category of the locations), (ii) it proposes a multi-aspect fused POI recommendation model, and (iii) it extensively evaluates the model with two real-world data sets.

The maturity of the smartphone and the World Wide Web (WWW) technologies have driven many social network applications which have facilitated people to share text and multimedia contents. The social networks that facilitate users to share the check-in (location visit) information are known as the location-based social networks (LBSN)s and provide various information for a recommendation problem that span beyond the user-location ratings, and comments. For instance, the time of the check-in, the category of the POI, the distance of POI from the user's home, the user's friends' visit to that place, and so forth. It's worthwhile to explore and efficiently integrate such information for the desired purpose. A Point of Interest (POI) recommendation system uses a user's historical check-in information from LBSNs and recommends the list of places that are potential for future visits. Many of the existing POI recommendation systems have focused on either of the temporal (time of the check-in), the geographical/spatial (distance between check-in locations), or the social (friendship, and trust based) aspects. Incorporation of all the major aspects (the categorical, the geographical, the social, and the temporal) of check-ins into a single model is barely explored by other studies. In this paper, we propose a fused model termed GeoTeCS (Geographical Temporal Categorical and Social) for personalized location recommendation. GeoTeCS uses the matrix factorization technique and an extension of the Multi-center Gaussian Model (MGM) to model the users' historical check-in behavior by fusing the major check-in aspects. The contributions of this paper are: (i) it proposes a matrix factorization based location recommender that incorporates all the major aspects-the categorical, the geographical, the social, and the temporal aspects into a single model and (ii) it extensively evaluates the proposed model against two real-world datasets - the Gowalla, and the Weeplaces, to illustrate its effectiveness.

We combined a spoken dialog system that we developed to deliver brief health interventions with the fully autonomous humanoid robot (NAO). The dialog system is based on a framework facilitating Markov decision processes (MDP). It is optimized using reinforcement learning (RL) algorithms with data we collected from real user interactions. The system begins to learn optimal dialog strategies for initiative selection and for the type of confirmations that it uses during theinteraction. The health intervention, delivered by a 3D character instead of the NAO, has already been evaluated, with positive results in terms of task completion, ease of use, and future intention to use the system. The current spoken dialog system for the humanoid robot is a novelty and exists so far as a proof ofconcept.