Using topic information to improve non-exact keyword-based search for mobile applications

Considering the wide offer of mobile applications available nowadays, effective search engines are imperative for an user to find applications that provide a specific desired functionality. Retrieval approaches that leverage topic similarity between queries and applications have shown promising results in previous studies. However, the search engines used by most app stores are based on keyword-matching and boosting. In this paper, we explore means to include topic information in such approaches, in order to improve their ability to retrieve relevant applications for non-exact queries, without impairing their computational performance. More specifically, we create topic models specialized on application descriptions and explore how the most relevant terms for each topic covered by an application can be used to complement the information provided by its description. Our experiments show that, although these topic keywords are not able to provide all the information of the topic model, they provide a sufficiently informative summary of the top- ics covered by the descriptions, leading to improved performance.info:eu-repo/semantics/publishedVersio

Assessing Quality of Consumer Reviews in Mobile Application Markets: A Principal Component Analysis Approach

This study presents a simple, theory-based method for calculating a metric which reflects the quality of online consumer reviews in mobile application markets. Derived from prior online consumer review studies based on psychology, information quality, and economics literature, a metric for measuring online consumer review quality is developed. The metric is a weighted sum of three variables (Squared Star Rating, Log-transformed Word Count, and Sum of Squared Negative and Positive Sentiment), and weights for calculating the metric are estimated by using Principal Component Analysis (PCA) technique. Preliminary assessment of the proposed method shows that metrics computed by using the proposed method are positively correlated with helpfulness ranks of mobile application reviews in Google Play. However, PCA results show that one of the variables (i.e., sentiment) used for developing the metric did not load consistently on the first factor component. From the findings of the preliminary evaluation on the metric, limitations and future research directions of the proposed method are discussed

Give Away Your Digital Services: Leveraging Big Data to Capture Value

Consumers are getting used to receiving free services in many different fields, and the popularity of the mobile app industry is feeding this phenomenon. Historically, advertising—a typical two-sided market mechanism—is the primary method that companies relying on a free-to-consumers business model have used to appropriate value in digital environ- ments. But new strategies are needed to make free services sustainable and profitable in the long term. At the same time, companies are gathering a huge amount of data from consumers, especially through mobile apps, by leveraging the sensors embedded in smartphones; this data represents a powerful new source of value. Through a case study analysis, we show how leveraging a two-sided structure can enable companies to capture value from user-sourced data, enabling a sustainable free-to-consumers business model. In this model, users are more than eyeballs to be targeted with advertising; they become data providers, and companies may capture value by using that data to customize advertising messages, lever- age e-ethnography to improve their own core offer, serve as fodder for research, or create knowledge for third partie

ELECTRONIC WORD-OF-MOUTH: APPLICATIONS IN PRODUCT RECOMMENDATION AND CRISIS INFORMATION DISSEMINATION

Ph.DDOCTOR OF PHILOSOPH

A mobile app search engine

10.1007/s11036-012-0413-zMobile Networks and Applications18142-5