Personal Web Revisitation by Context and Content Keywords with Relevance Feedback
Getting back to previously viewed web pages is a common yet uneasy task for users due to the large volume of personally accessed information on the web. This paper leverages human’s natural recall process of using episodic and semantic memory cues to facilitate recall, and presents a personal web revisitation technique called WebPage prev through context and content keywords. Underlying techniques for context and content memories’ acquisition, storage, decay, and utilization for page re-finding are discussed. A relevance feedback mechanism is also involved to tailor to individual’s memory strength and revisitation habits. Our 6-month user study shows that: (1) Compared with the existing web revisitation tool Memento, History List Searching method, and Search Engine method, the proposed WebPage prev delivers the best re-finding quality in finding rate (92.10%), average F1-measure (0.4318) and average rank error (0.3145). (2) Our dynamic management of context and content memories including decay and reinforcement strategy can mimic users’ retrieval and recall mechanism. With relevance feedback, the finding rate of WebPagePrev increases by 9.82%, average F1-measure increases by 47.09%, and average rank error decreases by 19.44% compared to stable memory management strategy. Among time, location, and activity context factors in WebPagePrev, activity is the best recall cue, and context+content based re-finding delivers the best performance, compared to context based re-finding and content based re-finding.
Reference IEEE paper:
“Personal Web Revisitation by Context and Content Keywords with Relevance Feedback”, IEEE Transactions on Knowledge and Data Engineering, 2017.
Unique ID -SBI1042
Domain – DATA MINING