The Moderating Role of Consumption Type

학위논문(석사) -- 서울대학교대학원 : 경영대학 경영학과, 2021.8. 박기완.While previous literature on algorithm aversion and appreciation have directed their attention to comparing consumers’perceptions of AI recommendation agents against human agents, seldom were consumers’ perceptions of different AI recommendation systems, despite their various techniques and proliferation in the real world, compared against each other. In such context, this study investigates how consumers’ evaluations of online platform brands differ by the AI recommendation systems - personalized versus non-personalized - accentuated in brand messages. This study posits that the type of product sold in the online platform brand will influence the evaluations of different AI recommendation systems emphasized in brand messages. For hedonic consumption with multiple ideal points of preference, consumers would prefer to take recommendations from personalized AI recommendations which would meet their own specific ideal points over the non-personalized. Contrarily, for utilitarian consumption that manifest high consensus in evaluation, there would be no difference in evaluations between personalized and non-personalized recommendation systems. This study further investigates the psychological mechanism of this effect: AI recommendation usefulness. Together, these results provide insights for online shopping platform brands in adopting effective AI recommendation systems for their product category and generating attractive brand messages regarding the recommendation system.최근 마케팅 연구에서는 특정 소비 영역에서 인간 추천 대비 인공지능 추천에 대한 소비자들의 반감을 나타내는 알고리즘 적대감(Algorithm Aversion) 현상이 밝혀졌다. 그러나 인공지능 추천 시스템의 유형별 소비자 인식을 살펴본 연구는 아직까지 드문 상황이다. 이에 본 연구는 브랜드 메시지가 소구하는 추천 시스템 유형(개인화 추천 vs. 비개인화 추천)에 대한 소비자 인식이 소비 유형(쾌락 소비 vs. 유용 소비)별로 다르다는 것을 입증한다. 이상 점(ideal point)이 다양한 쾌락 소비의 경우, 소비자들은 자신의 고유한 이상 점을 만족시키는 상품 및 서비스를 추천해줄 개인화 추천 시스템을 선호할 것이다. 반면 제품에 대한 평가 일치도가 높은 유용 소비의 경우, 개인화 추천 시스템과 상품 인기도에 따른 추천을 제공하는 비개인화 추천 시스템 간 소비자 선호도에는 차이가 없을 것이다. 이러한 효과는 인지된 인공지능 추천 시스템 유용성이라는 심리적 기재에 의해 매개될 것이다. 종합적으로, 본 연구 결과는 브랜드가 취급하는 품목이나 개인의 소비 동기에 따라 개인화 대 비개인화 추천 시스템에 대한 선호가 달라진다는 것을 입증, 브랜드 매니저들이 브랜드에 적합한 추천 시스템을 도입하고 효과적인 브랜드 메시지를 구상하는 데 유용한 시사점을 제공할 것이다.Chapter 1. Introduction 1 Chapter 2. Theorhetical Background 3 2.1. Perceptions on AI Algorithm 3 2.2. Recommendation System Techniques 5 2.3. Product Types and Preference Heterogeneity 6 2.4. Anticipated Usefulness of AI Recommendation 7 Chapter 3. Research Model and Hypothesis 8 Chapter 4. Pilot Study 9 4.1. Participants and Design 9 4.2. Stimuli and Procedure 9 4.3. Measures 11 4.4. Results 12 4.5. Discussion 14 Chapter 5. Main Study 15 5.1. Participants and Design 15 5.2. Stimuli and Procedure 15 5.3. Measures 17 5.4. Results 19 5.5. Dissussion 22 Chapter 6. General Discussion 23 Bibliography 27 Appendix 32 국문초록 33석

Analysis of collaborative filtering algorithms

Recommender System is a subclass of information filtering system which predicts the rating given to an item by any user. Collaborative filtering is a key technique in recommender systems. This technique predicts the user rating of an item by collaboration of other users who have similar interests with this user. Collaborative filtering approaches can be categorized as Memory based, Model-based and Hybrid approaches. Memory-based approach can be further classified as Item-based and User-based recommendations. Pearson correlation scheme belongs to user-based scheme and Slope one family of algorithms belong to item-based scheme. Slope one family consists of Normal, Weighted and Bipolar slope one algorithms. Algorithms belonging to model-based approach are Singular value decomposition, Regularized Singular value decomposition and Probabilistic Matrix Factorization. In hybrid approach combination of memory-based and model-based approaches are used for making recommendations. In this thesis we made an attempt to analyze various algorithms in Memory-based and Model-based approaches. In model based algorithms, we analyzed Singular Value Decomposition (SVD) and Regularized Singular Value Decomposition (RSVD). By taking three different dataset sizes, we observed that RSVD outperforms SVD for all three dataset sizes. In memory based algorithms, we analyzed Pearson correlation scheme which takes the correlation between user vectors as similarity measure and Slope one family of algorithms. In slope one algorithms, we proposed an improvement to the existing scheme for determining Threshold value of Bipolar slope one algorithm. We used median and average of min-max ratings which outperforms the existing user average scheme. Finally, we made an analysis of all these algorithms and concluded that RSVD outperforms rest of the algorithms in terms of accuracy of predictions

The constrained median : a way to incorporate side information in the assessment of food samples

A classical problem in the field of food science concerns the consensus evaluation of food samples. Typically, several panelists are asked to provide scores describing the perceived quality of the samples, and subsequently, the overall (consensus) scores are determined. Unfortunately, gathering a large number of panelists is a challenging and very expensive way of collecting information. Interestingly, side information about the samples is often available. This paper describes a method that exploits such information with the aim of improving the assessment of the quality of multiple samples. The proposed method is illustrated by discussing an experiment on raw Atlantic salmon (Salmo salar), where the evolution of the overall score of each salmon sample is studied. The influence of incorporating knowledge of storage days, results of a clustering analysis, and information from additionally performed sensory evaluation tests is discussed. We provide guidelines for incorporating different types of information and discuss their benefits and potential risks

Empirical Findings On Persuasiveness Of Recommender Systems For Customer Decision Support In Electronic Commerce

More and more companies are making online presence by opening online stores and providing customers with company and products information but the overwhelming amount of information also creates information overload for the customers. Customers feel frustrated when given too many choices while companies face the problem of turning browsers into actual buyers. Online recommender systems have been adopted to facilitate customer product search and provide personalized recommendation in the market place. The study will compare the persuasiveness of different online recommender systems and the factors influencing customer preferences. Review of the literature does show that online recommender systems provide customers with more choices, less effort, and better accuracy. Recommender systems using different technologies have been compared for their accuracy and effectiveness. Studies have also compared online recommender systems with human recommendations 4 and recommendations from expert systems. The focus of the comparison in this study is on the recommender systems using different methods to solicit product preference and develop recommendation message. Different from the technology adoption and acceptance models, the persuasive theory used in the study is a new perspective to look at the end user issues in information systems. This study will also evaluate the impact of product complexity and product involvement on recommendation persuasiveness. The goal of the research is to explore whether there are differences in the persuasiveness of recommendation given by different recommender systems as well as the underlying reasons for the differences. Results of this research may help online store designers and ecommerce participants in selecting online recommender systems so as to improve their products target and advertisement efficiency and effectiveness

Towards Reliable Online Feedback : The Impact of User Preference and Visual Cues in Rating Scales and User Ratings

With the rise of dependency on online shopping and service providers, consumer ratings and reviews help users decide between good and bad options. Reliable and useful ratings can ensure better consumer service, product sales, brand management. Any underlying bias or external factors affecting users emotional stability can corrupt the credibility of user feedback. Prior studies suggest that the visual representation and design elements provided with a rating scale can affect the user's responses, specially if the rating scales have visual labels that generate an emotional response in users. Since there are a number of rating scale designs used in online e-commerce sites and recommender systems, it is also important that users get a say in which rating scale they are comfortable in using. Online marketplace still does not provide a platform to consider user's own choice in this matter. This preferential choice of scales can make users more involved in the rating process and help get the best response from them. Earlier research have already proved that users have specific personalized preferences when it comes to using rating scales to give feedback online. Further emphasis on how this preference and visual cues together can elicit more reliable online feedback mechanism is required in this area. This thesis aims to investigate whether the preference of users in rating scales influences the reliability and authenticity of user's ratings. It also explores the user's reaction to certain visual cues in rating scales, and how user's preferences of rating scale are influenced by such visual elements. A within-subject study ($n$ = 187) was conducted to collect user ratings of popular products with six different rating scale designs, using two types of visual icons (stars and emojis) and colour-metaphors (using a warm-cool and a traffic-light metaphors). Statistical analysis from the survey shows that users prefer the scale with most visually informative design (traffic-light metaphor colours with emoji icons). It also shows that users tend to give their true ratings on scales they prefer most, rather than the scale design they are most familiar with. The rating score analysis also demonstrates a positive shift and better consistency in the ratings given on more visually rich scales. Based on these results, it can be concluded that user involvement is desirable in selecting the rating scale designs, and meaningful visual cues can contribute in getting more accurate (truthful) rating scores from users. The proposed approach of user preference based rating system has novelty because I elicited the user's own opinion on what their accurate or ``true" rating is; rather than only relying on analysing the data received from the rating scores. This work can offer insights for online rating scale designs to improve the rating decision quality of users and help online business platforms obtain more credible feedback from customers which can significantly improve their services and user satisfaction

A Connotative Space for Supporting Movie Affective Recommendation

The problem of relating media content to users’affective responses is here addressed. Previous work suggests that a direct mapping of audio-visual properties into emotion categories elicited by films is rather difficult, due to the high variability of individual reactions. To reduce the gap between the objective level of video features and the subjective sphere of emotions, we propose to shift the representation towards the connotative properties of movies, in a space inter-subjectively shared among users. Consequently, the connotative space allows to define, relate and compare affective descriptions of film videos on equal footing. An extensive test involving a significant number of users watching famous movie scenes, suggests that the connotative space can be related to affective categories of a single user. We apply this finding to reach high performance in meeting user’s emotional preferences