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Social recommendation has been recognized as a kind of e-commerce with large potential, but how social recommendations influence consumer decisions is still unclear. This paper aims to investigate how recommendations from different social ties influence consumers’ purchase intentions through both behavior and brain activity.

Utilizing behavioral (N = 70) and electroencephalogram (EEG) (N = 49) experiments, this study explored participants’ behavior and brain responses after being recommended by different social ties. The data were analyzed using statistical inference and event-related potential (ERP) analysis.

Behavioral results show that social tie strength positively impacts purchase intention, which can be fitted by a logarithmic model. Moreover, recommender-to-customer similarity and product affect mediate the effect of tie strength on purchase intention serially. EEG findings show that recommendations from weak tie strength elicit larger N100, N200 and P300 amplitudes than those from strong tie strength. These results imply that weak tie strength may motivate individuals to recruit more mental resources in social recommendation, including unconscious processing of consumer attention and conscious processing of cognitive conflict and negative emotion.

This study considers the effects of continuous social ties on purchase intention and models them mathematically, exploring the intrinsic mechanisms by which strong and weak ties influence purchase intentions through recommender-to-customer similarity and product affect, contributing to the applications of the stimulus-organism-response (SOR) model in the field of social recommendation. Furthermore, our study adopting EEG techniques bridges the gap of relying solely on self-report by providing an avenue to obtain relatively objective findings about the consumers’ early-occurred (unconscious) attentional responses and late-occurred (conscious) cognitive and emotional responses in purchase decisions.

Recommending suitable content for users of online health communities (OHCs) is critical for overcoming information overload problem and facilitate medical decision making, but remains not fully investigated. This study aims to provide a content recommendation approach to automatically match valuable health-related information for OHC members.

A framework of health-related content recommendation was proposed by leveraging rich social information in online communities. The authors constructed user influence relationship (UIR) utilizing users' interaction records, user profiles and user-generated content. The initial user rating matrix and the user post matching matrix were then created by analyzing text content of posts. Finally, the user rating matrix and the recommended content were generated for community members. Datasets were collected from an OHC to evaluate the effectiveness of the proposed approach.

The experimental results revealed that the proposed method statistically outperformed baseline models in content recommendation for users of OHCs.

The incorporation of social information can significantly enhance the performance of content recommendation in OHCs. The user post matching degree based on text analysis can improve the effectiveness of recommendation.

This study potentially contributes to the social support exchange and medical decision making of community members and the sustainable prosperity of OHCs.

This study proposes a novel social content recommendation method for online health consumers based on UIRs by leveraging social information in OHCs. The results indicate the significance of social information in content recommendation of healthcare social media.

Collaborative filtering based recommender systems (CF–RS) are widely used to recommend products based on consumers' preference similarity. Recommendations by CF–RS merely provide suggestions as “people who bought this also bought this” while, consumers are unaware about the source of these recommendations. By amalgamating CF–RS with consumers' social network information, e-commerce sites can offer recommendation from social networks of consumers. These social network embedded systems are known as social recommender systems (SRS). The extant literature has researched on the algorithms and implementation of these systems; however, SRS have not been understood from consumers' psychological perspective. This study aims to qualitatively explore consumers' motives to accept SRS in e-commerce websites.

This qualitative study is based on in-depth interviews of frequent online shoppers. SRS are currently not very widespread in the Indian e-commerce space; hence, a vignette was shown to respondents before they responded to the questions. Inductive qualitative content analysis method was used to analyse these interviews.

Three main themes (social-gratification, self-gratification and information-gratification) emerged from the analysis. Out of these, social-gratification acts as an enabler, while self-gratification along with some elements of information-gratification act as inhibitors towards acceptance of social recommendations. Based on these gratifications, we present a conceptual model on consumer's acceptance of social recommendations.

This study is an initial attempt to qualitatively understand consumers' attitudes and acceptance of social recommendations on e-commerce websites, which in itself is a fairly new phenomenon.

This study aims to examine the difference between personalized and non-personalized recommendations in influencing YouTube users’ video choices. In addition, whether men and women have a significant difference in using recommendations was compared and the predictors of recommendation video use frequency were explored.

A survey of 524 Saudi Arabia college students was conducted using computer-assisted self-administered interviews to collect their video recommendation sources and how likely they follow the recommendation from different sources.

Video links posted on social media used by the digital natives were found as the most effective form of recommendation shows that social approval is important in influencing trials. Recommendations can succeed in both personalized and non-personalized ways. Personalized recommendations as in YouTube recommended videos are almost the same as friends and family’s non-personalized posting of video links on social media in convincing people to watch the videos. Contrary to expectations, Saudi men college students are more likely to use recommendations than women students.

The use of a non-probability sample is a major limitation and self-reported frequency may result in over- or under-estimation of video use.

Marketers will realize that they may not need the personalized recommendation from the large site. They can use social media recommendations by the consumers’ friends and family. E-mail is the worst platform for a recommendation.

Recommendation is a credible source and can overcome the avoidance of advertising. Its influence on consumers will be increasing in years to come with the algorithmic recommendation and social media use.

This is the first study to compare the influence of different online recommendation sources and compare personalized and non-personalized recommendations. As recommendation is growing more and more important with algorithm development online, the study results have high reference values to marketers in Islamic countries and beyond.

Purpose – As researchers need partners to collaborate with, this study aims to provide author recommendation for academic researchers for potential collaboration, conference planning, and compilation of scientific working groups with the help of social information. Hereby the chapter analyzes and compares different similarity metrics in information and computer science.

Methodology/approach – The study uses data from the multidiscipline information services Web of Science and Scopus as well as the social bookmarking service CiteULike to measure author similarity and recommend researchers to unique target researchers. The similarity approach is based on author co-citation, bibliographic coupling of authors and collaborative filtering methods. The developed clusters and graphs are then evaluated by these target researchers.

Findings – The analysis shows, for example, that different methods for social recommendation complement each other and that the researchers evaluated user- and tag-based data from a social bookmarking system positively.

Research limitations/implications – The present study, providing author recommendation for six target physicists, is supposed to be a starting point for further approaches on social academic author recommendation.

Practical implications – The chapter investigates in recommendation methods and similarity algorithm models as basis for an implementation of a social recommendation system for researchers in academics and knowledge-intensive organizations.

Originality/value of chapter – The comparison of different similarity measurements and the user evaluation provide new insights into the construction of social data mining and the investigation of personalized recommendation.

The purpose of this paper is to explore the key factors that affect consumer redemption intention toward mobile coupons recommended in social network sites (SNS).

A research model that integrates recommendation trust, positive utilities, and negative utilities of coupon redemption is developed. With the important role of trust in social recommendation taken into consideration, the key drivers of recommendation trust were analyzed in the model. Data were collected from 210 users with mobile coupon recommendation experience in one of the largest SNS (i.e. WeChat) in China. The authors used partial least squares technique to analyze the model.

Recommendation trust and positive utilities (economic benefits and perceived enjoyment) positively affect the intention of mobile coupon redemption. Perceived risk, as a negative utility, negatively influences coupon redemption intention. In addition, swift trust (structure assurance, perceived similarity, trust propensity, and expertise of the recommender), knowledge-based trust (familiarity with the retailers), and emotion-based trust (social tie strength) are key drivers that promote recommendation trust.

While prior research investigated mobile coupon redemption behavior in which coupons were issued by merchants, limited research analyzed consumer responses toward mobile coupons in social recommendation. This study examines the effects of recommendation trust, positive utilities, and negative utilities on mobile coupon redemption in the context of social recommendation and recognizes the key drivers of recommendation trust.

Social recommender systems have recently gained increasing popularity. The purpose of this paper is to investigate the influences of informational factors on purchase intention in social recommender systems.

Specifically, this study validated the mediating effect of trust in recommendations and the perceived value between informational factors and consumers’ purchase intention.

The results confirm that recommendation persuasiveness was a strong predictor of trust in recommendations and perceived value. Recommendation completeness was positively related to trust in recommendations and perceived value as well. Trust in recommendations and perceived value was found to be strong drivers of purchase intention.

The author identifies two sets of informational factors, i.e. recommendation persuasiveness and recommendation completeness, which are relevant to consumer attitudes. The current study proved that informational factors on consumers’ purchase intention are fully mediated through trust in recommendations and perceived value.

Due to the rapidly increasing volume of users and products in virtual worlds, recommender systems are an important feature in virtual worlds; they can help solve information overload problems. Virtual world users are able to perform several actions that promote the enjoyment of their virtual life, including interacting with others, visiting virtual houses and shopping for virtual products. This study aims to concentrate on the following two important factors: the social neighbors’ influences and the virtual house bandwagon phenomenon, which affects users’ preferences during their virtual house visits and purchasing processes.

The authors determine social influence by considering the interactions between the target user and social circle neighbors. The degree of influence of the virtual house bandwagon effect is derived by analyzing the preferences of the virtual house hosts who have been visited by target users during their successive visits. A novel hybrid recommendation method is proposed herein to predict users’ preferences by combining the analyses of both factors.

The recommendation performance of the proposed method is evaluated by conducting experiments with a data set collected from a virtual world platform. The experimental results show that the proposed method outperforms the conventional recommendation methods, and they also exhibit the effectiveness of considering both the social influence and the virtual house bandwagon effect for making effective recommendations.

Existing studies on recommendation methods did not investigate the virtual house bandwagon effects that are unique to the virtual worlds. The novel idea of the virtual house bandwagon effect is proposed and analyzed for predicting users’ preferences. Moreover, a novel hybrid recommendation approach is proposed herein for generating virtual product recommendations. The proposed approach is able to improve the accuracy of preference predictions and enhance the innovative value of recommender systems for virtual worlds.

Online health communities (OHCs) are platforms that help health consumers to communicate with each other and obtain social support for better healthcare outcomes. However, it is usually difficult for community members to efficiently find appropriate peers for social support exchange due to the tremendous volume of users and their generated content. Most of the existing user recommendation systems fail to effectively utilize the rich social information in social media, which can lead to unsatisfactory recommendation performance. The purpose of this study is to propose a novel user recommendation method for OHCs to fill this research gap.

This study proposed a user recommendation method that utilized the adapted matrix factorization (MF) model. The implicit user behavior networks and the user influence relationship (UIR) network were constructed using the various social information found in OHCs, including user-generated content (UGC), user profiles and user interaction records. An experiment was conducted to evaluate the effectiveness of the proposed approach based on a dataset collected from a famous online health community.

The experimental results demonstrated that the proposed method outperformed all baseline models in user recommendation using the collected dataset. The incorporation of social information from OHCs can significantly improve the performance of the proposed recommender system.

This study can help users build valuable social connections efficiently, enhance communication among community members, and potentially contribute to the sustainable prosperity of OHCs.

This study introduces the construction of the UIR network in OHCs by integrating various social information. The conventional MF model is adapted by integrating the constructed UIR network for user recommendation.

With the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent. The purpose of this study is to incorporate topic optimization into collaborative filtering to enhance both the effectiveness and the efficiency of personalized recommendations for social tagging.

Combining the idea of optimization before service, this paper presents an approach that incorporates topic optimization into collaborative recommendations for social tagging. In the proposed approach, the recommendation process is divided into two phases of offline topic optimization and online recommendation service to achieve high-quality and efficient personalized recommendation services. In the offline phase, the tags' topic model is constructed and then used to optimize the latent preference of users and the latent affiliation of resources on topics.

Experimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations compared with the three baseline approaches. The proposed topic optimization–incorporated collaborative recommendation approach can achieve the improvement of both effectiveness and efficiency for the recommendation in social tagging.

With the support of the proposed approach, personalized recommendation in social tagging with high quality and efficiency can be achieved.