Many real-world applications are associated with collection of events with timestamps, known as time-to-event data. Earthquake occurrences, social networks, and user activity logs can be represented as a sequence of discrete events observed in continuous time. Temporal point process serves as an essential tool for modeling such time-to-event data in continuous time space. Despite having massive amounts of event sequence data from various domains like social media, healthcare etc., real world application of temporal point process faces two major challenges: 1) it is not generalizable to predict events from unseen event sequences in dynamic environment 2) they are not capable of thriving in continually evolving environment with minimal supervision while retaining previously learnt knowledge. To tackle these issues, we propose HyperHawkes, a hypernetwork based temporal point process framework which is capable of modeling time of event occurrence for unseen sequences and consequently, zero-shot learning for time-to-event modeling. We also develop a hypernetwork based continually learning temporal point process for continuous modeling of time-to-event sequences with minimal forgetting. HyperHawkes augments the temporal point process with zero-shot modeling and continual learning capabilities. We demonstrate the application of the proposed framework through our experiments on real-world datasets. Our results show the efficacy of the proposed approach in terms of predicting future events under zero-shot regime for unseen event sequences. We also show that the proposed model is able to learn the time-to-event sequences continually while retaining information from previous event sequences, mitigating catastrophic forgetting in neural temporal point process.

Semantic characterization of the Point-of-Interest (POI) plays an important role for modeling location-based social networks and various related applications like POI recommendation, link prediction etc. However, semantic categories are not available for many POIs which makes this characterization difficult. Semantic annotation aims to predict such missing categories of POIs. Existing approaches learn a representation of POIs using graph neural networks to predict semantic categories. However, LBSNs involve complex and higher order mobility dynamics. These higher order relations can be captured effectively by employing hypergraphs. Moreover, visits to POIs can be attributed to various reasons like temporal characteristics, spatial context etc. Hence, we propose a Multi-view Hypergraph Convolution Network (Multi-HGCN) where we learn POI representations by considering multiple hypergraphs across multiple views of the data. We build a comprehensive model to learn the POI representation capturing temporal, spatial and trajectory-based patterns among POIs by employing hypergraphs. We use hypergraph convolution to learn better POI representation by using spectral properties of hypergraph. Experiments conducted on three real-world datasets show that the proposed approach outperforms the state-of-the-art approaches.

The task of generating incorrect options for multiple-choice questions is termed as distractor generation problem. The task requires high cognitive skills and is extremely challenging to automate. Existing neural approaches for the task leverage encoder-decoder architecture to generate long distractors. However, in this process two critical points are ignored - firstly, many methods use Jaccard similarity over a pool of candidate distractors to sample the distractors. This often makes the generated distractors too obvious or not relevant to the question context. Secondly, some approaches did not consider the answer in the model, which caused the generated distractors to be either answer-revealing or semantically equivalent to the answer.

The prevalence of location-based social networks (LBSNs) has eased the understanding of human mobility patterns. However, categories which act as semantic characterization of the location, might be missing for some check-ins and can adversely affect modelling the mobility dynamics of users. At the same time, mobility patterns provide cues on the missing semantic categories. In this paper, we simultaneously address the problem of semantic annotation of locations and location adoption dynamics of users. We propose our model HAP-SAP, a latent spatio-temporal multivariate Hawkes process, which considers latent semantic category influences, and temporal and spatial mobility patterns of users. The inferred semantic categories can supplement our model on predicting the next check-in events by users. Our experiments on real datasets demonstrate the effectiveness of the proposed model for the semantic annotation and location adoption modelling tasks.

Word embedding techniques in literature are mostly based on Bag of Words models where words that co-occur with each other are considered to be related. However, it is not necessary for similar or related words to occur in the same context window. In this paper, we propose a new approach to combine different types of resources for training word embeddings. The lexical resources used in this work are Dependency Parse Tree and WordNet. Apart from the co-occurrence information, the use of these additional resources helps us in including the semantic and syntactic information from the text in learning the word representations. The learned representations are evaluated on multiple evaluation tasks like Semantic Textual Similarity, Word Similarity. Results of the experimental analyses highlight the usefulness of the proposed methodology.

Micro-blogging sites provide a wealth of resources during disaster events in the form of short texts. Correct classification of these text data into various actionable classes can be of great help in shaping the means to rescue people in disaster-affected places. The process of classification of these text data poses a challenging problem because the texts are usually short and very noisy and finding good features that can distinguish these texts into different classes is time consuming, tedious and often requires a lot of domain knowledge. We propose a deep learning based model to classify tweets into different actionable classes such as resource need and availability, activities of various NGO etc. Our model requires no domain knowledge and can be used in any disaster scenario with little to no modification.

There has been a massive rise in the use of Community Question and Answering (CQA) forums to get solutions to various technical and non-technical queries. One common problem faced in CQA is the small number of experts, which leaves many questions unanswered. This paper addresses the challenging problem of predicting the best answerer for a new question and thereby recommending the best expert for the same. Although there are work in the literature that aim to find possible answerers for questions posted in CQA, very few algorithms exist for finding the best answerer whose answer will satisfy the information need of the original Poster. For finding answerers, existing approaches mostly use features based on content and tags associated with the questions. There are few approaches that additionally consider the users' history. In this paper, we propose an approach that considers a comprehensive set of features including but not limited to text representation, tag based similarity as well as multiple user-based features that target users' availability, agility as well as expertise for predicting the best answerer for a given question. We also include features that give incentives to users who answer less but more important questions over those who answer a lot of questions of less importance. A learning to rank algorithm is used to find the weight of each feature. Experiments conducted on a real dataset from Stack Exchange show the efficacy of the proposed method in terms of multiple evaluation metrics for accuracy, robustness and real time performance.

With more and more news sources publishing news articles online, news recommendation systems are becoming increasingly popular in recent days. However, if a news recommender shows to the user many news articles that cover same story or talk about same persons or events, then the recommendations might become monotonous for the user. A possible way to handle this scenario is to diversify the recommended news articles so that the articles in the recommendation list are not too similar to each other. Existing research works in literature discuss how to use user-specific relevance of news articles and diversification to produce the final recommendations. To be able to compute user-specific relevance of articles, several recommender systems suggest that users have a login, or they set cookies to track page visits of the users. Often users are unsure whether the information collected via these means will be used for the sole purpose of news recommendation. Privacy of the identity and page access patterns is a concern for many users. In this work, we consider the problem where diverse recommendations are to be given for users, but no past data about users is collected or stored by the system. We use only article popularity to estimate the relevance. We identify few aspects of news articles on which diversification can be performed. These aspects are incorporated in an optimization framework. An approximation algorithm is used to generate the recommendations. Experimental analysis shows that in scenarios where no individual user data is stored or accessible by the recommender system, diversification results in higher click rates and increase in the number of satisfied users. Performances of the multiple diversification aspects and approaches are also analyzed.

In the Recommendation Problem, it is often important to find a set of items similar to a particular item or a group of items. This problem of finding similar items for the recommendation task may also be viewed as a link prediction problem in a network, where the items can be treated as the nodes. The strength of the edge connecting two items represents the similarity between the items. In this context, a central challenge is to suitably define an appropriate dissimilarity function between the items. For content based recommender systems, the dissimilarity function should take into account the individual attributes of the items. The same attribute may have different importances in different parts of the underlying network. We focus on the problem of learning a suitable dissimilarity function between items and address it by formulating it as a constrained optimization problem which captures the local weightages of the attributes in different regions of the graph. The constraints are imposed in such a way that the non-connected nodes show higher value of dissimilarity than the connected nodes. The local tuning of the weights learns the optimal value of weights in various parts of the network: from the portions having rich graph information to the portions having only content information. Detailed experimentation shows the superiority of the proposed algorithm over the Adamic Adar metric as well as logistic regression methodology.

Collaborative filtering is a widely used technique for rating prediction in recommender systems. Memory based collaborative filtering algorithms assign weights to the users to capture similarities between them. The weighted average of similar users' ratings for the test item is output as prediction. We propose a memory based algorithm that is markedly different from the existing approaches. We use preference relations instead of absolute ratings for similarity calculations, as preference relations between items are generally more consistent than ratings across like-minded users. Each user's ratings are viewed as a preference graph. Similarity weights are learned using an iterative method motivated by online learning. These weights are used to create an aggregate preference graph. Ratings are inferred to maximally agree with this aggregate graph. The use of preference relations allows the rating of an item to be influenced by other items, which is not the case in the weighted-average approaches of the existing techniques. This is very effective when the data is sparse, specially for the items rated by few users. Our experiments show that the our method outperforms other methods in the sparse regions. However, for dense regions, sometimes our results are comparable to the competing approaches, and sometimes worse.