Serendipity in the city: User evaluations of urban recommender systems

Freedom and Constraint in Digital Environments: Implications for the Self

We evaluate how features of the digital environment free or constrain the self. Based on the current empirical literature, we argue that modern technological features, such as predictive algorithms and tracking tools, pose four potential obstacles to the freedom of the self: lack of privacy and anonymity, (dis)embodiment and entrenchment of social hierarchy, changes to memory and cognition, and behavioral reinforcement coupled with reduced randomness. Comparing these constraints on the self to the freedom promised by earlier digital environments suggests that digital reality can be designed in more freeing ways. We describe how people reassert personal agency in the face of the digital environment's constraints and provide avenues for future research regarding technology's influence on the self.

Designing for serendipity: a means or an end?

Purpose

This article aims to gain a better understanding of the reasons why serendipity is designed for in different kinds of environments. Understanding these design intents sheds light on the value such designs bring to designers, in contrast to the users of the environment. In this way, the article seeks to contribute to the literature on cultivating serendipity from a designers’ point of view.

Design/methodology/approach

An extensive review of the literature discussing designing for serendipity was conducted to elicit the different motivations to design for serendipity. Based on these findings and a thorough analysis, a typology of design intents for serendipity is presented.

Findings

The article puts forward four intents to design for serendipity: serendipity as an ideal, common good, mediator and feature. It also highlights that the current academic discourse puts a strong emphasis on two of them. It is argued that this academic abstraction could be problematic for how we deal with designs for serendipity, both in theory and practice.

Originality/value

The novelty of this article is that it addresses the question of why to design for serendipity from a designer’s point of view. By introducing the notion of directionality it opens up the opportunity to discuss serendipity from multiple perspectives, which contributes to gaining a firmer understanding of serendipity. It allows to more explicitly formulate the different functions of a design for serendipity and thereby expands our knowledge on the value of designing for serendipity. At the same time, it sheds light on the potential threats to designing for serendipity.

Anomaly Detection on Natural Language Processing to Improve Predictions on Tourist Preferences

Argumentation-based dialogue models have shown to be appropriate for decision contexts in which it is intended to overcome the lack of interaction between decision-makers, either because they are dispersed, they are too many, or they are simply not even known. However, to support decision processes with argumentation-based dialogue models, it is necessary to have knowledge of certain aspects that are specific to each decision-maker, such as preferences, interests, and limitations, among others. Failure to obtain this knowledge could ruin the model’s success. In this work, we sought to facilitate the information acquisition process by studying strategies to automatically predict the tourists’ preferences (ratings) in relation to points of interest based on their reviews. We explored different Machine Learning methods to predict users’ ratings. We used Natural Language Processing strategies to predict whether a review is positive or negative and the rating assigned by users on a scale of 1 to 5. We then applied supervised methods such as Logistic Regression, Random Forest, Decision Trees, K-Nearest Neighbors, and Recurrent Neural Networks to determine whether a tourist likes/dislikes a given point of interest. We also used a distinctive approach in this field through unsupervised techniques for anomaly detection problems. The goal was to improve the supervised model in identifying only those tourists who truly like or dislike a particular point of interest, in which the main objective is not to identify everyone, but fundamentally not to fail those who are identified in those conditions. The experiments carried out showed that the developed models could predict with high accuracy whether a review is positive or negative but have some difficulty in accurately predicting the rating assigned by users. Unsupervised method Local Outlier Factor improved the results, reducing Logistic Regression false positives with an associated cost of increasing false negatives.