0000000000007003
AUTHOR
Denis Kotkov
Volkswagen Emission Crisis : Managing Stakeholder Relations on the Web
Organizations establish their own profiles at social media sites to publish pertinent information to customers and other stakeholders. During a long and severe crisis, multiple issues may emerge in media interaction. Positive responses and prompt interaction from the official account of e.g. a car manufacturer creates clarity and reduces anxiety among stakeholders. This research targets the Volkswagen 2015 emission scandal that became public on Sept. 18, 2015. We report its main phases over time based on public web information. To better understand the online interaction and reactions of the company, we scrutinized what information was published on VW’s official web sites, Facebook, and Twi…
Challenges of Serendipity in Recommender Systems
Most recommender systems suggest items similar to a user profile, which results in boring recommendations limited by user preferences indicated in the system. To overcome this problem, recommender systems should suggest serendipitous items, which is a challenging task, as it is unclear what makes items serendipitous to a user and how to measure serendipity. The concept is difficult to investigate, as serendipity includes an emotional dimension and serendipitous encounters are very rare. In this paper, we discuss mentioned challenges, review definitions of serendipity and serendipity-oriented evaluation metrics. The goal of the paper is to guide and inspire future efforts on serendipity in r…
How does serendipity affect diversity in recommender systems? A serendipity-oriented greedy algorithm
Most recommender systems suggest items that are popular among all users and similar to items a user usually consumes. As a result, the user receives recommendations that she/he is already familiar with or would find anyway, leading to low satisfaction. To overcome this problem, a recommender system should suggest novel, relevant and unexpected i.e., serendipitous items. In this paper, we propose a serendipity-oriented, reranking algorithm called a serendipity-oriented greedy (SOG) algorithm, which improves serendipity of recommendations through feature diversification and helps overcome the overspecialization problem. To evaluate our algorithm, we employed the only publicly available datase…
Gaming Bot Detection: A Systematic Literature Review
In online games, some players employ programs (bots) that allow them to bypass game routines and effortlessly gain virtual resources. This practice leads to negative effects, such as reduced revenue for the game development companies and unfair treatment for ordinary players. Bot detection methods act as a counter measure for such players. This paper presents a systematic literature review of bot detection in online games. We mainly focus on games that allow resource accumulation for players between game sessions. For this, we summarize the existing literature, list categories of games ignored by the scientific community, review publicly available datasets, present the taxonomy of detection…
Samsung and Volkswagen Crisis Communication in Facebook and Twitter : A Comparative Study
Since September 2015 at least two major crises have emerged where major industrial companies producing consumer products have been involved. In September 2015 diesel cars manufactured by Volkswagen turned out to be equipped with cheating software that caused NO2 and other emission values to be reduced to acceptable levels while tested from the real, unacceptable values in normal use. In August 2016 reports began to appear that the battery of a new smart phone produced by Samsung, Galaxy Note7, could begin to burn, or even explode, while the device was on. In Nov. 2016 also 34 washing machine models were reported to have caused damages due to disintegration. In all cases, the companies have …
Online Stakeholder Interaction of Some Airlines in the Light of Situational Crisis Communication Theory
Part 2: Digital Marketing and Customer Relationship Management; International audience; The purpose of this paper is to explore the participation of main actors in Facebook. The engagement shows different degrees of participation that directly affect the brand image and reputation. This research applies Situational Crisis Communication Theory (SCCT) to interaction in the social media. It provides possibilities for decision makers to monitor diverse messages online, understand stakeholder concerns and reply to them adequately, which is especially important in crisis situations. Seven airline organizations were selected for a comparative analysis concerning their online discussions. The verif…
A survey of serendipity in recommender systems
We summarize most efforts on serendipity in recommender systems.We compare definitions of serendipity in recommender systems.We classify the state-of-the-art serendipity-oriented recommendation algorithms.We review methods to assess serendipity in recommender systems.We provide the future directions of serendipity in recommender systems. Recommender systems use past behaviors of users to suggest items. Most tend to offer items similar to the items that a target user has indicated as interesting. As a result, users become bored with obvious suggestions that they might have already discovered. To improve user satisfaction, recommender systems should offer serendipitous suggestions: items not …
Improving Serendipity and Accuracy in Cross-Domain Recommender Systems
Cross-domain recommender systems use information from source domains to improve recommendations in a target domain, where the term domain refers to a set of items that share attributes and/or user ratings. Most works on this topic focus on accuracy but disregard other properties of recommender systems. In this paper, we attempt to improve serendipity and accuracy in the target domain with datasets from source domains. Due to the lack of publicly available datasets, we collect datasets from two domains related to music, involving user ratings and item attributes. We then conduct experiments using collaborative filtering and content-based filtering approaches for the purpose of validation. Ac…
Cross-Domain Recommendations with Overlapping Items
In recent years, there has been an increasing interest in cross-domain recommender systems. However, most existing works focus on the situation when only users or users and items overlap in different domains. In this paper, we investigate whether the source domain can boost the recommendation performance in the target domain when only items overlap. Due to the lack of publicly available datasets, we collect a dataset from two domains related to music, involving both the users’ rating scores and the description of the items. We then conduct experiments using collaborative filtering and content-based filtering approaches for validation purpose. According to our experimental results, the sourc…
The Issue Arena of a Corporate Social Responsibility Crisis : The Volkswagen Case in Twitter
This paper explores the online debate in a corporate social responsibility crisis, where multiple actors communicate through social media, each representing different interests and views pertaining to the crisis. The study utilizes Twitter data relating to the recent case of the falsified Volkswagen diesel emissions that became public in 2015. To better understand the online interaction, use is made of issue arena theory and insights on CSR crises. The focus is on capturing the issue as it evolved over time, the actors and sentiments expressed, and the responses of the organization. The findings show that after the case became public, the emissions issue received massive attention in Twitte…
Investigating serendipity in recommender systems based on real user feedback
Over the past several years, research in recommender systems has emphasized the importance of serendipity, but there is still no consensus on the definition of this concept and whether serendipitous items should be recommended is still not a well-addressed question. According to the most common definition, serendipity consists of three components: relevance, novelty and unexpectedness, where each component has multiple variations. In this paper, we looked at eight different definitions of serendipity and asked users how they perceived them in the context of movie recommendations. We surveyed 475 users of the movie recommender system, MovieLens regarding 2146 movies in total and compared tho…
Cross-Social Network Collaborative Recommendation
Online social networks have become an essential part of our daily life, and an increasing number of users are using multiple online social networks simultaneously. We hypothesize that the integration of data from multiple social networks could boost the performance of recommender systems. In our study, we perform cross-social network collaborative recommendation and show that fusing multi-source data enables us to achieve higher recommendation performance as compared to various single-source baselines.
A Serendipity-Oriented Greedy Algorithm for Recommendations
Most recommender systems suggest items to a user that are popular among all users and similar to items the user usually consumes. As a result, a user receives recommendations that she/he is already familiar with or would find anyway, leading to low satisfaction. To overcome this problem, a recommender system should suggest novel, relevant and unexpected, i.e. serendipitous items. In this paper, we propose a serendipity-oriented algorithm, which improves serendipity through feature diversification and helps overcome the overspecialization problem. To evaluate our algorithm and compare it with others, we employ a serendipity metric that captures each component of serendipity, unlike the most …
Recommending Serendipitous Items using Transfer Learning
Most recommender algorithms are designed to suggest relevant items, but suggesting these items does not always result in user satisfaction. Therefore, the efforts in recommender systems recently shifted towards serendipity, but generating serendipitous recommendations is difficult due to the lack of training data. To the best of our knowledge, there are many large datasets containing relevance scores (relevance oriented) and only one publicly available dataset containing a relatively small number of serendipity scores (serendipity oriented). This limits the learning capabilities of serendipity oriented algorithms. Therefore, in the absence of any known deep learning algorithms for recommend…