6 Conclusion
In this study, we have proposed an optimized process
recommendation model for optimizing information seeking
process. In our proposal, we defined a set of measures to
describe the patterns of users’ information access behaviors,
and then an optimized process recommendation
algorithm was used to extract and optimize the information
seeking process for target users from their reference user
group based on the similarity of behavior patterns.
Different from the traditional recommender systems that
provide the final results to users directly, our proposal
focused on recommending a seeking process to users,
because the seeking process can help users to apperceive
the knowledge contained in the recommended results
gradually. Furthermore, the individuality and commonness
are integrated in our approach, which could make the
recommendation results more suitable to target users.
Finally, based on our previously developed gradual adaptation
model, this approach gradually adapted to a target
user’s needs and information access behaviors so that it
benefits to improve the precision of recommendation.
As for future work, we will refine and improve optimized
process recommendation model and necessary
algorithms, fully implement the proposed system and
conduct experimental evaluation with users’ involvement.
6 ConclusionIn this study, we have proposed an optimized processrecommendation model for optimizing information seekingprocess. In our proposal, we defined a set of measures todescribe the patterns of users’ information access behaviors,and then an optimized process recommendationalgorithm was used to extract and optimize the informationseeking process for target users from their reference usergroup based on the similarity of behavior patterns.Different from the traditional recommender systems thatprovide the final results to users directly, our proposalfocused on recommending a seeking process to users,because the seeking process can help users to apperceivethe knowledge contained in the recommended resultsgradually. Furthermore, the individuality and commonnessare integrated in our approach, which could make therecommendation results more suitable to target users.Finally, based on our previously developed gradual adaptationmodel, this approach gradually adapted to a targetuser’s needs and information access behaviors so that itbenefits to improve the precision of recommendation.As for future work, we will refine and improve optimizedprocess recommendation model and necessaryalgorithms, fully implement the proposed system andconduct experimental evaluation with users’ involvement.
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