Interpersonal communication plays a key role in managing people's emotions, especially on digital platforms. Studies have shown that people use social media and consume online content to regulate their emotions and find support for rest and recovery. However, these platforms are not designed for emotion regulation, which limits their effectiveness in this regard. To address this issue, we propose an approach to enhance Interpersonal Emotion Regulation (IER) on online platforms through content recommendation. The objective is to empower users to regulate their emotions while actively or passively engaging in online platforms by crafting media content that aligns with IER strategies, particularly empathic responding. The proposed recommendation system is expected to blend system-initiated and user-initiated emotion regulation, paving the way for real-time IER practices on digital media platforms. To assess the efficacy of this approach, a mixed-method research design is used, including the analysis of text-based social media data and a user survey. Digital applications has served as facilitators in this process, given the widespread recognition of digital media applications for Digital Emotion Regulation (DER). The study collects 37.5K instances of user posts and interactions on Reddit over a year to design a Contextual Multi-Armed Bandits (CMAB) based recommendation system using features from user activity and preferences. The experimentation shows that the empathic recommendations generated by the proposed recommendation system are preferred by users over widely accepted ER strategies such as distraction and avoidance.

In this paper, we design a real-time question-answering system specifically targeted for helping sellers get relevant material/documentation they can share live with their customers or refer to during a call. Taking the Seismic content repository as a relatively large scale example of a diverse dataset of sales material, we demonstrate how LLM embeddings of sellers' queries can be matched with the relevant content. We achieve this by engineering prompts in an elaborate fashion that makes use of the rich set of meta-features available for documents and sellers. Using a bi-encoder with cross-encoder re-ranker architecture, we show how the solution returns the most relevant content recommendations in just a few seconds even for large datasets. Our recommender system is deployed as an AML endpoint for real-time inferencing and has been integrated into a Copilot interface that is now deployed in the production version of the Dynamics CRM, known as MSX, used daily by Microsoft sellers.

We investigate various prompting strategies for enhancing personalized recommendation performance with large language models (LLMs) through input augmentation. Our proposed approach, termed LLM-Rec, encompasses four distinct prompting strategies: (1) basic prompting, (2) recommendation-driven prompting, (3) engagement-guided prompting, and (4) recommendation-driven + engagement-guided prompting. Our empirical experiments show that incorporating the augmented input text generated by LLM leads to improved recommendation performance. Recommendation-driven and engagement-guided prompting strategies are found to elicit LLM's understanding of global and local item characteristics. This finding highlights the importance of leveraging diverse prompts and input augmentation techniques to enhance the recommendation capabilities with LLMs.

Social media marketing plays a vital role in promoting brand and product values to wide audiences. In order to boost their advertising revenues, global media buying platforms such as Facebook Ads constantly reduce the reach of branded organic posts, pushing brands to spend more on paid media ads. In order to run organic and paid social media marketing efficiently, it is necessary to understand the audience, tailoring the content to fit their interests and online behaviours, which is impossible to do manually at a large scale. At the same time, various personality type categorization schemes such as the Myers-Briggs Personality Type indicator make it possible to reveal the dependencies between personality traits and user content preferences on a wider scale by categorizing audience behaviours in a unified and structured manner. This problem is yet to be studied in depth by the research community, while the level of impact of different personality traits on content recommendation accuracy has not been widely utilised and comprehensively evaluated so far. Specifically, in this work we investigate the impact of human personality traits on the content recommendation model by applying a novel personality-driven multi-view content recommender system called Personality Content Marketing Recommender Engine, or PersiC. Our experimental results and real-world case study demonstrate not just PersiC's ability to perform efficient human personality-driven multi-view content recommendation, but also allow for actionable digital ad strategy recommendations, which when deployed are able to improve digital advertising efficiency by over 420% as compared to the original human-guided approach.

Optimove, a leading CRM marketing platform, announced it has acquired Graphyte, a real-time, cloud-based personalization platform optimizing the web and mobile experience for consumers. The announcement comes on the heels of Optimove's purchase of Kumulos, a leading provider of a personalized messaging platform for mobile applications. The combined purchase price of the two deals was not disclosed. The acquisitions help bolster Optimove as one of the most comprehensive CRM marketing platforms. Its AI-driven solutions autonomously determine the next-best-action for each customer, eliminating the need for marketers to manually map every customer journey.

Deployed on quality content platforms, the world's first knowledge dashboard leverages innovative technology to show users how much they know about the topics they care about CRUX, the developer of Knowledge Quantification technology used, is launching Knowledge Hub for deployment on quality content platforms and publisher sites. Knowledge Hub provides each user with a complete view of all the topics they are reading about – and how much of each topic they have already covered. The Knowledge Hub shows users in real time the impact of important content they have not yet read – and provides a personalized knowledge journey of the best articles to increase their knowledge. Knowledge Hub is the latest user experience based on CRUX's innovative knowledge quantification technology that measures each user's knowledge based on the content they consume. Deployed on quality publishers like NIKKEI, Sifted and The American Prospect, the technology is revolutionizing user engagement, retention and conversion.

Artificial intelligence (AI) has been invading our daily lives without us fully realizing it. When you wake up in the morning, you may ask Alexa to give you a run-down of your daily schedule. When you drive to work using Waze, the app is using a machine-learning algorithm to provide the best route for you. When you watch a movie or a show on Netflix or make a purchase on Amazon, the platforms use AI to make content or product recommendations for you. If Netflix can use AI to make content recommendations for us, businesses and enterprises can also use AI to make personalized content recommendations when prospects visit their websites.

Contracts are a common type of legal document that frequent in several day-to-day business workflows. However, there has been very limited NLP research in processing such documents, and even lesser in generating them. These contracts are made up of clauses, and the unique nature of these clauses calls for specific methods to understand and generate such documents. In this paper, we introduce the task of clause recommendation, asa first step to aid and accelerate the author-ing of contract documents. We propose a two-staged pipeline to first predict if a specific clause type is relevant to be added in a contract, and then recommend the top clauses for the given type based on the contract context. We pretrain BERT on an existing library of clauses with two additional tasks and use it for our prediction and recommendation. We experiment with classification methods and similarity-based heuristics for clause relevance prediction, and generation-based methods for clause recommendation, and evaluate the results from various methods on several clause types. We provide analyses on the results, and further outline the advantages and limitations of the various methods for this line of research.

According to a survey conducted by PwC, 72% of business leaders use AI for their business advantage. The Digital marketing world has been restructured immensely since the emergence of AI. It helps companies develop powerful digital strategies, optimizes campaigns, and improves return on investment. Teleflora, a floral company in the US, used AI marketing to build new customers' profiles and improve customer loyalty. Using these historical data, Teleflora used AI marketing to predict the future customer behavior of different audience segments.

According to a survey conducted by PwC, 72% of business leaders use AI for their business advantage. The Digital marketing world has been restructured immensely since the emergence of AI. It helps companies develop powerful digital strategies, optimizes campaigns, and improves return on investment. Teleflora, a floral company in the US, used AI marketing to build new customers' profiles and improve customer loyalty. Using these historical data, Teleflora used AI marketing to predict the future customer behavior of different audience segments.