Horizontal vs. Vertical Recommendation Zones Evaluation Using Behavior Tracking

Sulikowski, Piotr; Zdziebko, Tomasz

doi:10.3390/app11010056

Cited by 17 publications

(12 citation statements)

References 44 publications

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“…Therefore, in future research, having experiment participants experience an online service for a certain period of time and then presenting various variables or measurements will be a good way to improve the validity of the experiment. In addition, it will be interesting to apply techniques such as eye-tracking and implicit behavior tracking [7,8] to the concept of psychological ownership. For instance, "How would the layout of a website be attractive to users with a high sense of psychological possession?"…”

Section: Discussionmentioning

confidence: 99%

“…Interestingly, several recent studies deal with information expression or presentation that maximizes the effectiveness of recommendation systems by using electronic advanced research equipment such as an eye tracker [7]. For example, the study of Sulikowski and Zdziebko [8] suggested that the recommended area layout influences the effect of the recommendation. Specifically, the vertical recommendation area is more effective than the horizontal recommendation area.…”

Section: Introductionmentioning

confidence: 99%

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Effective Strategies for Contents Recommendation Based on Psychological Ownership of over the Top Services in Cyberspace

Seo

Park

2021

JTAER

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With the advanced development of IT, people are spending an increasing amount of time in the cyberspace and perceive psychological ownership of intangible objects (e.g., e-books, avatars, online movie streaming services), which they come to regard as “theirs”. This study focuses on users’ psychological ownership of OTT (over the top) services, which have recently received much attention, and investigates how service providers can present recommendation information more effectively when recommending content to users. This study, based on psychological ownership theory, specifically attempts to verify which method of recommending information is effective in correlation to the level of psychological ownership that a user feels about an online service. Additionally, this study presents this effect in terms of psychological distance, which we argue is the underlying mechanism of psychological ownership. Watcha, one of South Korea’s OTT services, was employed as the experimental subject in this study, and a scenario-based test was conducted. In conclusion, this study found that for users with high psychological ownership of online services, a recommendation information message based on objective and concrete information about a movie was more effective, whereas for users with low psychological ownership, abstractly expressed messages were more effective. Furthermore, by applying a moderated mediation model, this study confirmed that psychological distance mediated the results stated above.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effective Strategies for Contents Recommendation Based on Psychological Ownership of over the Top Services in Cyberspace

Seo

Park

2021

JTAER

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“…For example, previous studies investigated how to improve the effectiveness of recommendation systems by employing user-related knowledge, such as demographic information, in various industries, such as books, tours, and electronic products [25][26][27]. Moreover, information related to users' activities and their interactions with computer interfaces can be useful in determining user preferences and needs; these data can further improve the effectiveness of a recommendation system [28].…”

Section: Introductionmentioning

confidence: 99%

Unintended CSR Violation Caused by Online Recommendation

Yoon

Joo

2021

Sustainability

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This paper investigates whether online recommendation of products that exhibit corporate social responsibility (CSR) penalizes the purchase intention of non-CSR products. When consumers browse online retail stores and consider buying a particular product, online recommendation is made (e.g., “Customers who viewed this item also viewed”). This recommendation is often made between products of which attributes have a trade-off relationship (e.g., CSR vs. price). (A trade-off is where one thing increases, and another must decrease. A trade-off relationship between CSR and price suggests a pair of competing products are available: a more expensive, CSR product and an economical, non-CSR product.) We borrowed from the psychological literature of evaluability to hypothesize that when a CSR product is recommended, consumers would decrease their purchase intention of the economical product. However, when an economical product is recommended, consumers would maintain their purchase intention of the CSR product. We further hypothesized that this asymmetric effect would disappear when reinforcement information regarding the CSR is provided. Two carefully designed experiments conducted in China supported these hypotheses. Our findings contribute to the growing literature on online retailers by elucidating the psychological impact of online recommendations, which may influence manufacturers’ sales in an unexpected manner. The findings also indicate that online recommendations could be a potential source of channel conflict. While this study newly verifies the unintended CSR violation effect of online recommendations, future studies are required to expand our understanding of the CSR violation effect by investigating the effect under the trade-off relationship with other attributes of the product.

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“…With the development of data-mining algorithms, recommendation systems are used in information retrieval (e.g., Google and Baidu), news feeds (e.g., Toutiao and Google News), e-commerce [7] (e.g., Amazon, Taobao, and Alibaba), and social networks (e.g., Facebook, Tencent, and Twitter) have achieved great success in various fields, effectively alleviating the contradiction between information and users. Recommendation systems due to their multi-domain applicability are among the main topics of scientific interest in recent years [8].…”

Section: Introductionmentioning

confidence: 99%

A Top-N Movie Recommendation Framework Based on Deep Neural Network with Heterogeneous Modeling

Gong

Zhang

et al. 2021

Applied Sciences

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To provide more accurate and stable recommendations, it is necessary to combine display information with implicit information and to dig out potential information. Existing methods only consider explicit feedback information or implicit feedback information unilaterally and ignore the potential information of explicit feedback information and implicit feedback information, which is also crucial to the accuracy of the recommendation system. However, the traditional Heterogeneous Information Networks (HIN) recommendation ignores the attribute information in the meta-path and the interaction between the user and the item and, instead, only considers the linear characteristics of the user-object often ignoring its non-linear characteristics. Aiming at the potential information acquisition problem from assorted feedback, we propose a new top-N recommendation method MFDNN for Heterogeneous Information Networks (HINs). First, we consider explicit and implicit feedback information to determine the potential preferences of users and the potential features of the product. Then, matrix factorization (MF) and a deep neural network (DNN) are fused to learn independent feature embeddings through MF and DNN, and fully considering the linear and non-linear characteristics of the user-object. MFDNN was tested on several real data sets, such as Movie-Lens, and compared with benchmark experiments. MFDNN significantly improved the hit ratio (HR) and normalized discounted cumulative gain (NDCG). Further research showed that the meta-path bias had an excellent effect on the gain of potential information mining and the fusion of explicit and implicit information in the accuracy and stability of user interest classification.

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Horizontal vs. Vertical Recommendation Zones Evaluation Using Behavior Tracking

Cited by 17 publications

References 44 publications

Effective Strategies for Contents Recommendation Based on Psychological Ownership of over the Top Services in Cyberspace

Effective Strategies for Contents Recommendation Based on Psychological Ownership of over the Top Services in Cyberspace

Unintended CSR Violation Caused by Online Recommendation

A Top-N Movie Recommendation Framework Based on Deep Neural Network with Heterogeneous Modeling

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