相关论文: A Framework for App Store Optimization

The wide spread of mobile devices in the consumer market has posed a number of new issues in the design of internet applications and their user interfaces. In particular, applications need to adapt their interaction modalities to different…

Recommender systems are expected to be assistants that help human users find relevant information automatically without explicit queries. As recommender systems evolve, increasingly sophisticated learning techniques are applied and have…

Web-based application developers face a dizzying array of platforms, languages, frameworks and technical artifacts to choose from. We survey, classify, and compare technologies supporting Web application development. The classification is…

Personalizing user experience with high-quality recommendations based on user activity is vital for e-commerce platforms. This is particularly important in scenarios where the user's intent is not explicit, such as on the homepage.…

As foundation models have accumulated hundreds of millions of users, developers have begun to take steps to prevent harmful types of uses. One salient intervention that foundation model developers adopt is acceptable use policies: legally…

Recommender systems have emerged as a new weapon to help online firms to realize many of their strategic goals (e.g., to improve sales, revenue, customer experience etc.). However, many existing techniques commonly approach these goals by…

Nowadays, recommender systems and search engines play an integral role in fashion e-commerce. Still, many challenges lie ahead, and this study tries to tackle some. This article first suggests a content-based fashion recommender system that…

A scaleable modelling framework for the consumer intent within the setting of e-Commerce is presented. The methodology applies contextualisation through embeddings borrowed from Natural Language Processing. By considering the user session…

App developers aim to create apps that cater to the needs of different types of users. This development approach, also known as the "one-size-fits-all" strategy, involves combining various functionalities into one app. However, this…

One of the main challenges in recommender systems is data sparsity which leads to high variance. Several attempts have been made to improve the bias-variance trade-off using auxiliary information. In particular, document modeling-based…

Traditional recommender systems present a relatively static list of recommendations to a user where the feedback is typically limited to an accept/reject or a rating model. However, these simple modes of feedback may only provide limited…

We present a powerful general framework for designing data-dependent optimization algorithms, building upon and unifying recent techniques in adaptive regularization, optimistic gradient predictions, and problem-dependent randomization. We…

The increasing attraction of mobile apps has inspired researchers to analyze apps from different perspectives. As with any software product, apps have different attributes such as size, content maturity, rating, category, or number of…

Embedding models, which learn latent representations of users and items based on user-item interaction patterns, are a key component of recommendation systems. In many applications, contextual constraints need to be applied to refine…

We consider a setting where goods are allocated to agents by way of an allocation platform (e.g., a matching platform). An ``allocation facilitator'' aims to increase the overall utility/social-good of the allocation by encouraging (some of…

Context information brings new opportunities for efficient and effective applications and services on mobile devices. A wide range of research has exploited context dependency, i.e., the relations between context(s) and the outcome, to…

An increasing number of mobile applications share location-dependent information, from collaborative applications and social networks to location-based games. For such applications, peer-to-peer architectures where mobile devices share…

Online platforms such as YouTube, Instagram heavily rely on recommender systems to decide what content to present to users. Producers, in turn, often create content that is likely to be recommended to users and have users engage with it. To…

Open-source repositories provide wealth of information and are increasingly being used to build artificial intelligence (AI) based systems to solve problems in software engineering. Open-source repositories could be of varying quality…

Application autotuning is a promising path investigated in literature to improve computation efficiency. In this context, the end-users define high-level requirements and an autonomic manager is able to identify and seize optimization…