Improving search personalisation with dynamic group formation

Vu, Thanh; Song, Dawei; Willis, Alistair; Tran, Son N. and Li, Jingfei (2014). Improving search personalisation with dynamic group formation. In: Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, ACM, pp. 951–954.

DOI: https://doi.org/10.1145/2600428.2609482

URL: http://dl.acm.org/citation.cfm?id=2600428.2609482

Abstract

Recent research has shown that the performance of search engines can be improved by enriching a user's personal profile with information about other users with shared interests. In the existing approaches, groups of similar users are often statically determined, e.g., based on the common documents that users clicked. However, these static grouping methods are query-independent and neglect the fact that users in a group may have different interests with respect to different topics. In this paper, we argue that common interest groups should be dynamically constructed in response to the user's input query. We propose a personalisation framework in which a user profile is enriched using information from other users dynamically grouped with respect to an input query. The experimental results on query logs from a major commercial web search engine demonstrate that our framework improves the performance of the web search engine and also achieves better performance than the static grouping method.

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