Personalization Approaches for Ranking: A Review and Research Experiments

Personalization Approaches for Ranking: A Review and Research Experiments

Madhuri A. Potey (Savitribai Phule Pune University, Pune, India) and Pradeep K. Sinha (College of Engineering, Pune, India)
Copyright: © 2017 |Pages: 16
DOI: 10.4018/IJIRR.2017010101
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Abstract

Search engine technologies are evolving to satisfy the user's ever increasing information need; but are yet to achieve perfection especially in ranking. With the exponential growth in the available information on the internet; ranking has become vital for satisfactory search experience. User satisfaction can be ensured to some extent by personalizing the search results based on user preferences which can be explicitly stated or learned from user's search behavior. Machine learning algorithms which predict user preference from the available information related to the user are extensively experimented for personalization. Among several studies undertaken for re-ranking the documents, many focus on the user. Such approaches create user model to capture the search context and behavior. This study attempts to analyze the research trends in user model based personalization and discuss experimental results in personalized information retrieval area. The authors experimented to extend the state of the art in the specific areas of personalization.
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Personalization Approaches At Querying Stage

Query logs record user’s search queries and related actions; which is an important source to learn user’s search behavior implicitly. Web Search technology may collect information about user in general and then identify groups of user to improvise ranking. Users may explicitly specify interests, demographics or cognitive characteristics. One disadvantage of such explicit feedback is that it is difficult to collect and maintain it for temporal personalization. The user models can also be built implicitly based on content the user has clicked on or the web history of interaction. These models are suitable for personalization using query adaptations or query modification where query is improvised to better represent the information need based on the user model. Short queries can be augmented with additional words to minimize the vocabulary problem, such as polysemy and synonymy, which are prevalent in keyword-based search. Alternatively, if the query retrieves a smaller number of resources than expected, it is possible to expand it using words or phrases with a similar meaning or applying some other statistical relations to the set of relevant documents [Micarelli et al.2007]. The major advantage of query modification approach is that the amount of work required to retrieve the results is the same as in the non-personalized search. On the other hand, query modification approach is less likely to affect the result lists as it does not affect all stages of ranking process.

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