Future Works

There are several related issues, which could be further studied to facilitate the analysis process to best recommend the proper candidates for referrals.

We will check those issues from three different dimensions, the mechanism itself, the job seekers and the purpose of this application.

First, in order to enhance the recommendation mechanism, there are some strategies we could apply to do more accurate analysis and try to stimulate the real decision process. We may take the relationship between different companies into considered and constantly update those correlations by the latest news. For example, take TSMC, HP, and MediaTek for consideration. Though it looks like there are totally different companies, actually HP is the vendor for TSMC IT department and MediaTek is the client side that assigns TSMC to complete their weaver producing. The calculation model will be precise if we consider all of those complicated and dynamic relationship between different companies and the collaboration between those departments in the companies in the advanced version of this application.

Secondly, we could also free the limitation of online media in the further use. We could ask our users to provide more detailed descriptions in the offline interaction among his or her own social network and even the context or scenario happened in the interaction. After calculation the list of possible candidates, the user may provide the real interaction information about the relationship of that social link to further optimize the process based on the true situations in the real world. For example, candidate A may be the classmate who meets three days a week and candidate B just someone meet in the party once.

Thirdly, as we mentioned in the limitation part, we need more users to be involved and willing to provide the information about their own working, education experiences as well as

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their social interaction records. For the future, we could design mechanism to encourage more users engaged, like the membership and point system. For example, user can get some extra points while one of his or her friends decide to provide their information for the recommendation process.

Fourthly, we also focus on the distribution of users. From our experiment, we can see due to the users of Facebook, most users of our application are younger generation, like the student just graduated or the employers whose working experience is less than three years.

Maybe for the future we could pay more attention on the users of different ages, such as 40~50 year-old users and figure out some other ways to attract them.

Lastly, since we have already build on such powerful system, in the future we might expand the purpose of request for more widely use, such as finding experts or recommend male or female for date. Though the factors in the computation model may be vary with distinct application domain, the basic concept of social search is still the same. We might use this system as the base and develop more application.

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