Persuasive Online-Selling in Quality and Taste Domains

Zanker, Markus; Bricman, Marcel; Gordea, Sergiu; Jannach, Dietmar; Jessenitschnig, Markus

doi:10.1007/11823865_6

Cited by 33 publications

(14 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Persuasion is generally affected by various parameters, such as perceived system credibility, recommendation accuracy and quality of data, perceived system competence, and usability [4,9,44]. On the other hand, satisfaction is also influenced by complex parameters such as recommendation accuracy, system transparency, previous trust relation with the system, and usability [14,29].…”

Section: Study Hypothesesmentioning

confidence: 99%

See 1 more Smart Citation

The effects of recommendations’ presentation on persuasion and satisfaction in a movie recommender system

2010

View full text Add to dashboard Cite

The explosive growth of Internet applications and content, during the last decade, has revealed an increasing need for information filtering and recommendation. Most research in the area of recommendation systems has focused on designing and implementing efficient algorithms that provide accurate recommendations. However, the selection of appropriate recommendation content and the presentation of information are equally important in creating successful recommender applications. This paper addresses issues related to the presentation of recommendations in the movies domain. The current work reviews previous research approaches and popular recommender systems, and focuses on user persuasion and satisfaction. In our experiments, we compare different presentation methods in terms of recommendations' organization in a list (i.e. top N-items list and structured overview) and recommendation modality (i.e. simple text, combination of text and image, and combination of text and video). The most efficient presentation methods, regarding user persuasion and satisfaction, proved to be the ''structured overview'' and the ''text and video'' interfaces, while a strong positive correlation was also found between user satisfaction and persuasion in all experimental conditions.

show abstract

Section: Study Hypothesesmentioning

confidence: 99%

“…to persuade them). This compound measure evaluates the quality of the utilized information as it was studied in previous research [9,26,44]. It refers to the content structure, accessibility, usability, timeliness, and sufficiency (e.g.…”

Section: Experimental Conditions and Measuresmentioning

confidence: 99%

The effects of recommendations’ presentation on persuasion and satisfaction in a movie recommender system

2010

View full text Add to dashboard Cite

show abstract

“…Unfortunately, measuring such long-term effects is difficult even when it is possible to conduct A/B tests. One of the few works in that direction are the ones by [22] and [23], who observed that the online recommender systems guided customers to a different part of the product spectrum.…”

Section: Popularity Reinforcementmentioning

confidence: 99%

What Recommenders Recommend – An Analysis of Accuracy, Popularity, and Sales Diversity Effects

Jannach

Lerche

Gedikli

et al. 2013

User Modeling, Adaptation, and Personalization

Self Cite

View full text Add to dashboard Cite

Abstract. In academic studies, the evaluation of recommender system (RS) algorithms is often limited to offline experimental designs based on historical data sets and metrics from the fields of Machine Learning or Information Retrieval. In real-world settings, however, other businessoriented metrics such as click-through-rates, customer retention or effects on the sales spectrum might be the true evaluation criteria for RS effectiveness. In this paper, we compare different RS algorithms with respect to their tendency of focusing on certain parts of the product spectrum. Our first analysis on different data sets shows that some algorithmswhile able to generate highly accurate predictions -concentrate their top 10 recommendations on a very small fraction of the product catalog or have a strong bias to recommending only relatively popular items than others. We see our work as a further step toward multiple-metric offline evaluation and to help service providers make better-informed decisions when looking for a recommendation strategy that is in line with the overall goals of the recommendation service.

show abstract

“…The present work now tries to build bridges between recommendation systems that are mainly driven by codified knowledge such as [5,2] or [6] and majorly learning and data-driven approaches. Algorithms for multi-criteria recommender systems typically learn predictive models by exploiting the different rating dimensions without exploiting any domain specific knowledge [7,8,9].…”

Section: Introductionmentioning

confidence: 99%

Multi-criteria Ratings for Recommender Systems: An Empirical Analysis in the Tourism Domain

Fuchs

Zanker

2012

Lecture Notes in Business Information Processing

Self Cite

View full text Add to dashboard Cite

Abstract. Most recommendation systems require some form of user feedback such as ratings in order to make personalized propositions of items. Typically ratings are unidimensional in the sense of consisting of a scalar value that represents the user's appreciation for the rated item. Multi-criteria ratings allow users to express more differentiated opinions by allowing separate ratings for different aspects or dimensions of an item. Recent approaches of multi-criteria recommender systems are able to exploit this multifaceted user feedback and make personalized propositions that are more accurate than recommendations based on unidimensional rating data. However, most proposed multi-criteria recommendation algorithms simply exploit the fact that a richer feature space allows building more accurate predictive models without considering the semantics and available domain expertise. This paper contributes on the latter aspects by analyzing multi-criteria ratings from the major etourism platform, TripAdvisor, and structuring raters' overall satisfaction with the help of a Penalty-Reward Contrast analysis. We identify that several a-priori user segments significantly differ in the way overall satisfaction can be explained by multi-criteria rating dimensions. This finding has implications for practical algorithm development that needs to consider different user segments.

show abstract

Persuasive Online-Selling in Quality and Taste Domains

Cited by 33 publications

References 22 publications

The effects of recommendations’ presentation on persuasion and satisfaction in a movie recommender system

The effects of recommendations’ presentation on persuasion and satisfaction in a movie recommender system

What Recommenders Recommend – An Analysis of Accuracy, Popularity, and Sales Diversity Effects

Multi-criteria Ratings for Recommender Systems: An Empirical Analysis in the Tourism Domain

Contact Info

Product

Resources

About