Statistical relational learning with soft quantifiers

Quantification in statistical relational learning (SRL) is either existential or universal, however humans might be more inclined to express knowledge using soft quantifiers, such as ``most'' and ``a few''. In this paper, we define the syntax and semantics of PSL^Q, a new SRL framework that supports reasoning with soft quantifiers, and present its most probable explanation (MPE) inference algorithm. To the best of our knowledge, PSL^Q is the first SRL framework that combines soft quantifiers with first-order logic rules for modelling uncertain relational data. Our experimental results for link prediction in social trust networks demonstrate that the use of soft quantifiers not only allows for a natural and intuitive formulation of domain knowledge, but also improves the accuracy of inferred results

Building Affective Trust among Client-Organization Relationships on Social Media Accounts

The purpose of this thesis is to determine how consumers relay trust to their favorite brands on social media sites, define affective trust, and how consumers interact with their favorite brands on social media. In-depth interviews were conducted with 22 individuals living across the United States who were in different career fields and stages of life. The findings from this study revealed that not too many of the participants followed Starbucks on mainstream social media sites, but followed them on their propriety application. This application was talked about in multiple interviews where participants talked about the features of the application and how they trust that the content that Starbucks provides will be trustworthy and honored in each one of its cafes. The participants also recognized what trust is but each provided a different definition, which could be combined to allow for the creation of a universal understanding. Each participant was also asked to describe good and bad experiences that they have had with Starbucks or other brands to help them reflect on what can be done better and how trust was developed or broken. Based off of these findings, the researcher further discussed how trust and relationships are important to consumers and brands while making recommendations for the creation of a universal definition for affective trust and recommendations on how to manage a brand’s image and credibility among consumers based off of their interactions in stores or online

The Role of Social User and Social Feature on Recommendation Acceptance in Instagram in Indonesia

This study aims to identify the effect of social features and social users on recommendations acceptance on shopping activities in Instagram. This study uses quantitative approach to process 654 data collected using online questionnaire. The data were analyzed using CB-SEM method and AMOS 21 tools. The results of this study showed that social features and social users give moderating effect on the relationship between social recommendation, cognitive appraisal and affective appraisal. Meanwhile, affective and cognitive appraisal was found to affect purchase intention. The finding shows that the user giving recommendation and the features used to make recommendation can influence the level of recommendation acceptance

08171 Abstracts Collection -- Beyond the Finite: New Challenges in Verification and Semistructured Data

From 20.04. to 25.04.2008, the Dagstuhl Seminar 08171 ``Beyond the Finite: New Challenges in Verification and Semistructured Data\u27\u27 was held in the International Conference and Research Center (IBFI), Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Behavior of Shallot Farmers in Probolinggo Regency on The Use of Chemical Pesticides

This research was conducted based on the conditions for controlling shallot pests in Probolinggo Regency, which still uses chemical pesticides, far from sustainable agricultural practices. Therefore, this study aims as follows: 1. To identify the knowledge of shallot farmers on chemical pesticides; 2. Identify farmers' responses to pests and diseases of shallots; 3. Knowing the pattern of chemical pesticides used by farmers; 4. Develop academic plans on reducing the use of chemical pesticides. This research was a type of descriptive research; the research looked at the phenomena in the research location symbols in society that are considered essential. The approach used in the research was quantitative. In this study, observations were made first to see the initial conditions of the research location, then continued with direct observations of the research location area to collect data according to the results obtained from the intended respondents. The results showed that shallot farmers' knowledge level in Probolinggo Regency was still in the medium category. Meanwhile, the attitudes and actions of farmers in using chemical pesticides were included in the poor level

Trust-based recommendation systems: an axiomatic approach

ABSTRACT High-quality, personalized recommendations are a key feature in many online systems. Since these systems often have explicit knowledge of social network structures, the recommendations may incorporate this information. This paper focuses on networks which represent trust and recommendations which incorporate trust relationships. The goal of a trust-based recommendation system is to generate personalized recommendations from known opinions and trust relationships. In analogy to prior work on voting and ranking systems, we use the axiomatic approach from the theory of social choice. We develop an natural set of five axioms which we desire any recommendation system exhibit. Then we show that no system can simultaneously satisfy all these axioms. We also exhibit systems which satisfy any four of the five axioms. Next we consider ways of weakening the axioms, which can lead to a unique recommendation system based on random walks. We consider other recommendation systems (personal page rank, majority of majorities, and min cut) and search for alternative axiomatizations which uniquely characterize these systems. Finally, we determine which of these systems are incentive compatible. This is an important property for systems deployed in a monetized environment: groups of agents interested in manipulating recommendations to make others share their opinion have nothing to gain from lying about their votes or their trust links