A Conceptual Framework for Enhancing Product Search with Product Information from Reviews

Product search today is limited, as users can only search and filter for a restricted set of product features, e.g. 15” and 1TB hard disk when searching for a laptop. The often decision- critical aspects of a product are however hidden in user reviews (“noisy fan” or “bright display”) and are not available until a product has been found. This paper proposes a conceptual framework for the integration of product aspects, that have been mined and derived from consumer reviews, into the product search. The framework structures the challenges that arise in four major fields and gives an overview of existing research for each one of them: Data challenges, user experience challenges, purchase process challenges and business challenges. It may inform researchers from various disciplines to perform target-oriented research as well as practitioners what to consider when building up such an enriched product search

Managing Temporal Dynamics of Filter Bubbles

Filter bubbles have attracted much attention in recent years in terms of their impact on society. Whereas it is commonly agreed that filter bubbles should be managed, the question is still how. We draw a picture of filter bubbles as dynamic, slowly changing constructs that underlie temporal dynamics and that are constantly influenced by both machine and human. Anchored in a research setting with a major public broadcaster, we follow a design science approach on how to design the temporal dynamics in filter bubbles and how to design users' influence over time. We qualitatively evaluate our approach with a smartphone app for personalized radio and found that the adjustability of filter bubbles leads to a better co-creation of information flows between information broadcaster and listener

Consumers’ Need for Negative Product Information Before Reading Reviews

Negative product-related information is crucial to consumers in purchase decisions. Consumers perceive negative information stronger than positive, and next to a stronger perception, consumers also have a high demand for negative product aspects, as these show the problem areas of a product and can help avoid losses. But negative product-related information is not available in the product search process until the customer reads reviews at a very late phase of the decision process. Even though we know about a bias in perception of negative information, little is known about the exact need for negative product-related information during the search process. We examine the need for negative product-related information throughout the purchase-decision process for different product types. Insights about the need for negative product-related information can inform ecommerce platform providers how to design a better product search on their site

AUTOMATED KEYWORD GENERATION IN THE PUBLIC RADIO SECTOR USING WORD EMBEDDINGS

Public broadcasters find themselves in a difficult situation when it comes to digital offers. In more and more use cases, metadata is needed, e.g. to allow radio editors to search for content pieces, to set up content-based recommendation services, to allow users to browse by categories or tags, or to optimize content for search engines. They are in need of proper metadata to manage digital products and to offer new and timely services. Public broadcasters often have their own taxonomy of keywords at hand. The manual distilling of metadata in particular in form of keywords may however become a bottleneck in operation, whereas automatic keyword generation does not always provide the desired accuracy and also requires continuous human effort for training classifiers and controlling the accuracy. Building upon more recent techniques of word embedding we present a novel approach to assign keywords from a taxonomy to documents on the basis of distributed representation of words and documents that does not require annotation by human experts and evaluate it with a large dataset of a German nation-wide broadcaster. Preliminary results are promising that keywords can be automatically generated in an unsupervised way in the public radio sector

Digital Transformation of Radio Broadcasting: An Exploratory Analysis of Challenges and Solutions for New Digital Radio Services

Like other media industries before, radio broadcasting is increasingly facing competition from new media platforms and changing consumer expectations. Many broadcasters are experimenting with possible solutions and are changing their production processes. While this is necessary, research is needed to capture the whole phenomenon of digital transformation of radio broadcasting. We conducted exploratory qualitative content analysis on talks of radio practitioner to identify current challenges, possible solutions, and specific aesthetics that shape current and future radio experience. We conceptualize the case of digital transformation of radio from the perspective of service-dominant logic and digital service innovation and discuss relevant areas of service innovation. We thus offer orientation for practitioners and contribute to a rather new, yet fruitful area of research for the information systems discipline

Making Filter Bubbles Understandable

Recommender systems tend to create filter bubbles and, as a consequence, lower diversity exposure, often with the user not being aware of it. The biased preselection of content by recommender systems has called for approaches to deal with exposure diversity, such as giving users control over their filter bubble. We analyze how to make filter bubbles understandable and controllable by using interactive word clouds, following the idea of building trust in the system. On the basis of several prototypes, we performed explorative research on how to design word clouds for the controllability of filter bubbles. Our findings can inform designers of interactive filter bubbles in personalized offers of broadcasters, publishers, and media houses

Processing Patient Information Leaflets with Embeddings

As of 2021, more than 100,000 drugs are approved in Germany, 35,000 of which are non-prescriptive over-the-counter drugs. While proven information from medical studies is given in patient information leaflets, patients are often lost when trying to determine which drugs are compatible with their needs or which alternatives are suitable. We show that representing patient information leaflets as dense vectors allows us to extract more valuable medical information than is explicitly stated in the leaflets. Without any explicit insertion of medical knowledge, our embeddings capture concepts of generics, even with respect to the dosage form. Furthermore, the embeddings allow patients to identify drug clusters based on their treatment area and offer suitable alternatives based on analogical reasoning. The carved-out information may not only help patients to explore alternative drugs but also serve pharmacists and patients as a new way to search for drugs tailored to dietary, allergic, or medical needs