Marked Temporal Point Process (MTPP) -- the de-facto sequence model for continuous-time event sequences -- historically employed for modeling human-generated action sequences, lack awareness of external stimuli. In this study, we propose a novel framework developed over Transformer Hawkes Process (THP) to incorporate external stimuli in a domain-agnostic manner. Furthermore, we integrate personalization into our framework by employing language model-based representations of user and event descriptions, which is essential for modeling human-generated action sequences. Towards evaluating the efficacy, we put together a comprehensive benchmark comprising 5 datasets (2 novel additions, and 3 repurposed from existing open datasets) harvested from several domains, spanning education, e-commerce, online payment, and discussion forum. On average, we achieve 9.35% gain in type-prediction accuracy and 7.38% reduction in time-prediction RMSE across all datasets over SOTA MTPP baselines. We demonstrate the superior performance of our proposed model through extensive ablations and showcasing its ability to capture complex combinations of external stimuli in a synthetic set up.

Several traditional methods were tested on standard datasets to evaluate client emotions transmitted via internet portals. Customers, on the other hand, continue to have difficulty obtaining aspect-oriented viewpoints voiced by other customers, and the accuracy of the current model is insufficient. The suggested Aspect-Based Sentimental Analysis (ABSA) starts with pre-processing, which includes “stop word and punctuation removal, lower case conversion, and stemming.” Aspect extraction, which entails dividing the nouns and adjectives, as well as verbs and adverbs, is the following step. The weighted polarity features from the “Vader sentiment intensity analyzer, as well as the word2vector and Term Frequency-Inverse Document Frequency (TF-IDF)” are concatenated. OIDL stands for Optimized Integrated Deep Learning, which combines two types of deep learners. The first is the combination of concatenated features with “Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN),” while the second is the combination of concatenated features with RNN. The Improved Coyote Optimization Algorithm (ICOA) improves both deep learners, and the conclusion of sentiment analysis result is considered both models. Thus, the suggested model surpasses standard methodologies regarding precision and accuracy, according to the results of the experiments.

Rewards-based programs are popular within e-commerce online stores, with the goal of providing serendipitous incentives to delight customers. These rewards (or incentives) could be in the form of cashback, free-shipping or discount coupons on purchases within specific categories. The success of such programs relies on their ability to identify relevant rewards for customers, from a wide variety of incentives available on the online store. Estimating the likelihood of a customer redeeming an incentive is challenging due to 1) data sparsity: relatively rare occurrence of coupon redemptions as compared to issuances, and 2) delayed feedback: customers taking time to redeem, resulting in inaccurate model refresh, compounded by data drift due to new customers and coupons.

Graph-based semi-supervised learning (G-SSL) algorithms have witnessed rapid development and widespread usage across a variety of applications in recent years. However, the theoretical characterisation of the efficacy of such algorithms has remained an under-explored area. We introduce a novel algorithm for G-SSL, CSX, whose objective function extends those of Label Propagation and Expander, two popular G-SSL algorithms. We provide data-dependent generalisation error bounds for all three aforementioned algorithms when they are applied to graphs drawn from a partially labelled extension of a versatile latent space graph generative model. The bounds we obtain enable us to characterise the predictive performance as measured by accuracy in terms of homophily and label quantity. Building on this we develop a key notion of GLM-safety which enables us to compare G-SSL algorithms on the basis of the range of graphs on which they obtain a guaranteed accuracy. We show that the proposed algorithm CSX has a better GLM-safety profile than Label Propagation and Expander while achieving comparable or better accuracy on synthetic as well as real-world benchmark networks.

With the proliferation of learning scenarios with an abundance of instances, but limited amount of high-quality labels, semi-supervised learning algorithms came to prominence. Graph-based semi-supervised learning (G-SSL) algorithms, of which Label Propagation (LP) is a prominent example, are particularly well-suited for these problems. The premise of LP is the existence of homophily in the graph, but beyond that nothing is known about the efficacy of LP. In particular, there is no characterisation that connects the structural constraints, volume and quality of the labels to the accuracy of LP. In this work, we draw upon the notion of recovery from the literature on community detection, and provide guarantees on accuracy for partially-labelled graphs generated from the Partially-Labelled Stochastic Block Model (PLSBM). Extensive experiments performed on synthetic data verify the theoretical findings.

Recommending lifestyle articles is of immediate interest to the e-commerce industry and is beginning to attract research attention. Often followed strategies, such as recommending popular items are inadequate for this vertical because of two reasons. Firstly, users have their own personal preference over items, referred to as personal styles, which lead to the long-tail phenomenon. Secondly, each user displays multiple personas, each persona has a preference over items which could be dictated by a particular occasion, e.g. dressing for a party would be different from dressing to go to office. Recommendation in this vertical is crucially dependent on discovering styles for each of the multiple personas. There is no literature which addresses this problem.

While purchasing a product, consumers often rely on specifications as well as online reviews of the product for decision-making. While comparing, one often has in mind a specific aspect or a set of aspects which are of interest to them. Previous work has used comparative sentences, where two entities are compared directly in a single sentence by the review author, towards the comparison task. In this paper, we extend the existing model by incorporating the feature specifications of the products, which are easily available, and learn the importance to be associated with each of them. To test the validity of these product ranking measures, we comprehensively test it on a digital camera dataset from Amazon.com and the results show good empirical outperformance over the state-of-the-art baselines.

In a team formation problem, one is required to find a group of users that can match the requirements of a collaborative task. Example of such collaborative tasks abound, ranging from software product development to various participatory sensing tasks in knowledge creation. Due to the nature of the task, team members are often required to work on a co-operative basis. Previous studies [1, 2] have indicated that co-operation becomes effective in presence of social connections. Therefore, effective team selection requires the team members to be socially close as well as a division of the task among team members so that no user is overloaded by the assignment. In this work, we investigate how such teams can be formed on a social network.

Viral Marketing, the idea of exploiting social interactions of users to propagate awareness for products, has gained considerable focus in recent years. One of the key issues in this area is to select the best seeds that maximize the influence propagated in the social network. In this paper, we define the seed selection problem (called t-Influence Maximization, or t-IM) for multiple products. Specifically, given the social network and t products along with their seed requirements, we want to select seeds for each product that maximize the overall influence. As the seeds are typically sent promotional messages, to avoid spamming users, we put a hard constraint on the number of products for which any single user can be selected as a seed. In this paper, we design two efficient techniques for the t-IM problem, called Greedy and FairGreedy. The Greedy algorithm uses simple greedy hill climbing, but still results in a 1/3-approximation to the optimum. Our second technique, FairGreedy, allocates seeds with not only high overall influence (close to Greedy in practice), but also ensures fairness across the influence of different products. We also design efficient heuristics for estimating the influence of the selected seeds, that are crucial for running the seed selection on large social network graphs. Finally, using extensive simulations on real-life social graphs, we show the effectiveness and scalability of our techniques compared to existing and naive strategies.