Lien-Fa Lin

A social recommender mechanism for location-based group commerce

With the rapid growth of social media platforms, numerous group commerce websites, which exploit both the advantages of price discounts and experience value, have emerged. Moreover, the popularity of sophisticated mobile devices brings great commercial opportunities for local store to gain publicity. In this research, considering user preference, geographic convenience, and friends’ influence, a group-coupon recommender system is proposed for promoting location-sensitive products. The results of experiments conducted on Facebook indicate that the proposed mechanism could accurately recommend products and satisfactorily provide a companion list of to customers, significantly increasing willingness to purchase by taking advantage of the power of social influence.

Enhancing Targeted Advertising with Social Context Endorsement

The success of online advertising depends on the degree of customer acceptance and corresponding click-through rate (CTR). The coverage of traditional online advertising is wide, but the CTR of display advertisements delivered by firms is relatively lower. Whereas targeted advertising can deliver appropriate advertisements to customers based on their traits (disturbance avoidance), advertisements can leverage the power of social influence to improve the degree of acceptance of advertisements. In this paper, by utilizing the power of social influence and context embellishment, we propose a personalized social context endorsement mechanism to enhance the effectiveness of advertisements. According to the results of an experiment conducted on Facebook, the proposed mechanism outperforms other benchmark approaches to improving the CTR and user impressions. This research effectively incorporates the theories of preference similarity, social influence, and moral sentiments and provides advertising sponsors and social media providers with a powerful system to conduct successful advertising campaigns.

A recommender mechanism for social knowledge navigation in an online encyclopedia

In todays world, knowledge is important for constructing core competitive advantages for individuals and organizations. Recently, Web 2.0 applications and social media have provided a convenient medium for people to share knowledge over the Internet. However, the huge amount of created knowledge can also leads to the problem of information overload. This research proposes a social knowledge navigation mechanism that utilizes the techniques of relevant knowledge network construction, knowledge importance analysis, and knowledge concept ontology construction to generate a visualized recommendation of a knowledge map of sub-concept and knowledge of an article reading sequence for supporting learning activities related to a free online encyclopedia. The results of experiments conducted on Wikipedia show that the proposed mechanism can effectively recommend useful articles and improve a knowledge seekers learning effectiveness.

A social referral appraising mechanism for the e-marketplace

Abstract With the popularity of social media, in order to achieve word-of-mouth marketing, many marketers and individual sellers are actively engaged in achieving positive ratings in the e-marketplace. Although many seller review mechanisms have been developed, e-commerce market operators and consumers still face trust fraud issues. Trust is one of the major issues that confuse online purchase activities. In this paper, we develop a social referral mechanism to verify sellers based on the experiences of friends within a buyer’s social network. The proposed framework estimates the trust values of sellers from the perspective of the social network in order to stop buyers from making transactions with fraudulent sellers in the online marketplace.

A diffusion planning mechanism for social marketing

Abstract Social media is gaining importance as a component of marketing strategies. Many types of social media, such as social networking sites, blogospheres and micro-blogospheres, have been seeking business opportunities and establishing brand expression in the recent years. Online marketing information diffusion has become the critical business model of online social networks. However, most of the current marketing studies on discovering potential influences do not appropriately support them to diffuse advertisements. Therefore, marketing information may be lost during the diffusion process and cannot be sent to potential customers successfully. In this research, a diffusion path planning mechanism for advertisements is developed to help influencers to propagate marketing information and help marketers to evaluate possible rewards under different marketing strategies. Our experimental results show that the proposed mechanism can significantly improve the diffusion process of advertising messages and decrease marketing uncertainty.

Business modeling for online video services: download vs. streaming

Download and streaming are two major approaches for delivering online video/movie. In this paper, considering their differentiated characteristics, we analyze the pricing issues in a market where both types of content distribution are available. We examine the impact of various technological and market factors on developing the business strategies under the scenarios that these distribution channels are owned by the same provider or two independent firms. We further examine the loyalty fee development when the video content is independently owned by another content provider. Our results shows that while the quality of two types of video distribution has significant impact on determining the content loyalty fee in a competitive market, loyalty fee is only affected by the characteristic of steaming service.

Social Appraisal Support for Point-of-interest Visiting Decision-making

As neighborhood and city exploration is one of the major themes in many location-based social network services, it is vital to provide Point-of-Interest (POI) recommendations to users. With a fast growing number of mobile users and POIs within such services, it is important to adopt efficient techniques so as to provide precise POI recommendations. Owing to the strong participation of knowledgeable users, a social networking service can be seen as a large number of experts who support the decisions of online users. People make decisions based on their personal preferences, but they also rely strongly on the opinions of others, especially close friends or influential people. The “location” where the decision-making takes place is also equally important in affecting user behaviour. In this paper, we propose a social decision support mechanism that integrates the methodologies and techniques of social network analysis, geographic distance analysis, and local expert analysis to achieve social decision support for mobile users. We aim to discover the POIs to which a mobile user is likely to go. With this proposed mechanism, online users can efficiently reduce their decision-making processes and reduce the risk of visiting an unsuitable POI.

SEARCH LOCATION-DEPENDENT DATA IN BROADCASTING ENVIRONMENT

Abstract Location-based services (LBSs) provide the information based on location information as specified in a query. Queries that support LBS are called Location-Dependent Queries (LDQ). LDQ contain: range query, nearest neighbor (NN) query, k-nearest neighbor (KNN) query and reverse nearest neighbor (RNN) query. Examples of mobile LBSs include location-dependent information access (e.g., traffic reports and attractions) and nearest neighbor queries (e.g. fmding the nearest restaurant). While LDQ is well studied in the traditional wired, disk-based client-server environment, it has not yet been tackled in relation to a wireless broadcasting environrent. In this paper, the issues involved with organizing location-dependent data and answering LDQ queries on the air, aze investigated. The lineaz property of wireless broadcasting media and the power conservation requirement of mobile devices make the problem particularly interesting and challenging. An efficient data organization, called Jump Rdnn-Tree, and...