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Register a new userQuery Scheduling in the Presence of Complex User Profiles
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Added by
Haggai Roitman
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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Advances in Web technology enable personalization proxies that assist users in satisfying their complex information monitoring and aggregation needs through the repeated querying of multiple volatile data sources. Such proxies face a scalability challenge when trying to maximize the number of clients served while at the same time fully satisfying clients complex user profiles. In this work we use an abstraction of complex execution intervals (CEIs) constructed over simple execution intervals (EIs) represents user profiles and use existing offline approximation as a baseline for maximizing completeness of capturing CEIs. We present three heuristic solutions for the online problem of query scheduling to satisfy complex user profiles. The first only considers properties of individual EIs while the other two exploit properties of all EIs in the CEI. We use an extensive set of experiments on real traces and synthetic data to show that heuristics that exploit knowledge of the CEIs dominate across multiple parameter settings.
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Recent research has shown the usefulness of using collective user interaction data (e.g., query logs) to recommend query modification suggestions for Intranet search. However, most of the query suggestion approaches for Intranet search follow an one size fits all strategy, whereby different users who submit an identical query would get the same query suggestion list. This is problematic, as even with the same query, different users may have different topics of interest, which may change over time in response to the users interaction with the system. We address the problem by proposing a personalised query suggestion framework for Intranet search. For each search session, we construct two temporal user profiles: a click user profile using the users clicked documents and a query user profile using the users submitted queries. We then use the two profiles to re-rank the non-personalised query suggestion list returned by a state-of-the-art query suggestion method for Intranet search. Experimental results on a large-scale query logs collection show that our personalised framework significantly improves the quality of suggested queries.
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