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Software-related platforms have enabled their users to collaboratively label software entities with topics. Tagging software repositories with relevant topics can be exploited for facilitating various downstream tasks. For instance, a correct and complete set of topics assigned to a repository can increase its visibility. Consequently, this improves the outcome of tasks such as browsing, searching, navigation, and organization of repositories. Unfortunately, assigned topics are usually highly noisy, and some repositories do not have well-assigned topics. Thus, there have been efforts on recommending topics for software projects, however, the semantic relationships among these topics have not been exploited so far. We propose two recommender models for tagging software projects that incorporate the semantic relationship among topics. Our approach has two main phases; (1) we first take a collaborative approach to curate a dataset of quality topics specifically for the domain of software engineering and development. We also enrich this data with the semantic relationships among these topics and encapsulate them in a knowledge graph we call SED-KGraph. Then, (2) we build two recommender systems; The first one operates only based on the list of original topics assigned to a repository and the relationships specified in our knowledge graph. The second predictive model, however, assumes there are no topics available for a repository, hence it proceeds to predict the relevant topics based on both textual information of a software project and SED-KGraph. We built SED-KGraph in a crowd-sourced project with 170 contributors from both academia and industry. The experiment results indicate that our solutions outperform baselines that neglect the semantic relationships among topics by at least 25% and 23% in terms of ASR and MAP metrics.

It's really important to discuss the benefits and drawbacks of artificial intelligence before it gets out of hand because this technology is improving and evolving at such a pace. As a computer science field, AI focuses on developing software and machines that mimic human thinking. Some artificial intelligence systems can analyze large amounts of data to learn from the past and enhance their performance without the input of programmers. AI is now widespread in both business and daily life. People interact with AI-powered virtual assistants or software daily to enhance their lives.

My reading so far, is that designing and implementing a real time automated recommendation is a probem that requires multiple objectives to solve and this is the need of the hour. . Such multi-objective system needs competing objectives of consumers, possible tensions between goals of different stakeholders, conflicts when optimizing for different time horizons, competing design choices at the UI level, as well as system-level and engineering-related considerations. Solution I am using the @kaggle competation is kind of enesemble modle RNN, Transformers and Linear Programming.

This research explores substitution of the fittest (SF), a technique designed to counteract the problem of disengagement in two-population competitive coevolutionary genetic algorithms. SF is domain-independent and requires no calibration. We first perform a controlled comparative evaluation of SF's ability to maintain engagement and discover optimal solutions in a minimal toy domain. Experimental results demonstrate that SF is able to maintain engagement better than other techniques in the literature. We then address the more complex real-world problem of evolving recommendations for health and well-being. We introduce a coevolutionary extension of EvoRecSys, a previously published evolutionary recommender system. We demonstrate that SF is able to maintain engagement better than other techniques in the literature, and the resultant recommendations using SF are higher quality and more diverse than those produced by EvoRecSys.

Our network is trained using triplets of songs such that two songs by the same In this paper, we explore the feasibility of using Triplet artist are embedded closer to one another than to networks, a variant of Siamese networks (Bromley et al., a third song by a different artist. We compare 1994), for content-based music recommendation. In this two models that are trained using different ways context, a Triplet network learns an embedding of an item of picking this third song: at random vs. based such that the item is close to other similar items and far on shared genre labels. Our experiments are conducted from dissimilar items in the embedding space. To train using songs from the Free Music Archive the network, we will consider songs by the same artist to and use standard audio features. The initial results be similar and songs by all other artists to be dissimilar.

Any organization needs to improve their products, services, and processes. In this context, engaging with customers and understanding their journey is essential. Organizations have leveraged various techniques and technologies to support customer engagement, from call centres to chatbots and virtual agents. Recently, these systems have used Machine Learning (ML) and Natural Language Processing (NLP) to analyze large volumes of customer feedback and engagement data. The goal is to understand customers in context and provide meaningful answers across various channels. Despite multiple advances in Conversational Artificial Intelligence (AI) and Recommender Systems (RS), it is still challenging to understand the intent behind customer questions during the customer journey. To address this challenge, in this paper, we study and analyze the recent work in Conversational Recommender Systems (CRS) in general and, more specifically, in chatbot-based CRS. We introduce a pipeline to contextualize the input utterances in conversations. We then take the next step towards leveraging reverse feature engineering to link the contextualized input and learning model to support intent recognition. Since performance evaluation is achieved based on different ML models, we use transformer base models to evaluate the proposed approach using a labelled dialogue dataset (MSDialogue) of question-answering interactions between information seekers and answer providers.

The world of mobile apps is changing, and it's all thanks to artificial intelligence (AI). An increasing number of companies are investing in AI technology that can be used to improve their products and services. But why should you care? What types of impacts could this have on your business? Let's explore some ways that AI will impact the development process for mobile apps: AI-powered personal assistants are becoming increasingly popular, and they can help with a range of tasks.

Artificial intelligence (AI) has undoubtedly changed many aspects of our lives, from how we work, meet people, discover information and even the careers we choose. The rate of change in society spurred through the developments in AI will almost certainly accelerate as it becomes more accessible to companies and to the general public. Its usefulness and level of "intelligence" directly correlate to the processing power of computer processors. What does this mean for you as an investor? AI is the leading supercycle in the technology sector and will have a profound impact on society.

Learning to rank is an effective recommendation approach since its introduction around 2010. Famous algorithms such as Bayesian Personalized Ranking and Collaborative Less is More Filtering have left deep impact in both academia and industry. However, most learning to rank approaches focus on improving technical accuracy metrics such as AUC, MRR and NDCG. Other evaluation metrics of recommender systems like fairness have been largely overlooked until in recent years. In this paper, we propose a new learning to rank algorithm named Pareto Pairwise Ranking. We are inspired by the idea of Bayesian Personalized Ranking and power law distribution. We show that our algorithm is competitive with other algorithms when evaluated on technical accuracy metrics. What is more important, in our experiment section we demonstrate that Pareto Pairwise Ranking is the most fair algorithm in comparison with 9 other contemporary algorithms.