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high quality information for decision making than traditional way. However, we
found that both effective recommendatory mechanism and the suitable atmos-
phere, which encourage users to contribute information actively, are critical for a
successful recommendatory service.
The reason for users to contribute themselves to proximal social intelligence
dependent on what user perceived from the provided e-service. The usefulness,
easy to use, and high quality information are essential factors that encourage users
participate in the service continuously. Appropriate recommendatory mechanism
could improve the information quality, but to facilitate the participation rely on
suitable design that focus on user's perception. Future research design should
strengthen the incentives for user to share information actively. In order to provide
some incentives or stimulus that increasing collaboration opportunities and facili-
tating the altruistic behaviors, some possible improvements are described below.
First, a suitable ranking system or point system should be established. Providing
the differentiation of information provider could honor the active information pro-
viders and create incentives for others continuously contribute themselves to the ser-
vice system. Second, some ad-hoc user group could be established according to the
user's need and their interests. By gathering more users participate in the service,
more unique information could be obtained and enrich the recommendatory capabil-
ity. When users establishing some emotional connections, they will be lock-in the
system and also urge other users to join the society. This will bring more valuable
heterogeneous information sources and personal experiences into the service system
to improve the information quality as well as the decision quality.
5 Conclusion and Future Directions
In web 2.0 era, collaboration with ubiquitous social intelligence becomes an im-
portant trend. The social networks contain abundant information for further utili-
zation, but altruism between unfamiliar strangers is need urgently. This chapter
focuses on user's basic needs such as perceptual feeling and their context, the
analysis extends to the content information as well as the context of users. By
highlight the proximity of each participant, we extend the user generated contents
(UGC) in social media for better utilization. The concepts of proximity have been
utilized for exploring both psychological and geographical incentives for users
within social networks to collaborate with others for mutual goals.
Personal experience could be various from different social context they encoun-
tered. We utilize these unique information sources to improve the recommenda-
tion quality for leisure entertainment industry. Both TF-IDF and CTD methods are
applied for extracting the knowledge from social network intelligence.
The collective social network intelligence benefit from user's proximity recog-
nition provides strong incentives for user to contribute themselves for mutual ad-
vantages. The social network on cyberspace also provide significant information
exchange rate. Utilizing the proximal social network intelligence on Internet envi-
ronments, leisure entertainment recommendatory service may transmit informa-
tion various ways. Through the external channel that permits effective information
spread and diffusion. Therefore, the participants with similar interest could be
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