Group based Personalized Search by Integrating Search Behaviour and Friend Network
Yujia Zhou, Zhicheng Dou, Bingzheng Wei, Ruobing Xievand Ji-Rong Wen

TL;DR
This paper introduces a neural network and friend network enhanced personalized search model that combines semantic user similarity and social connections to improve search result relevance, especially for users with limited historical data.
Contribution
It proposes a novel integration of semantic user similarity and friend network data into personalized search, enhancing group profiling and result re-ranking.
Findings
Significant improvement over existing models.
Effective grouping of users into friend circles.
Enhanced personalization for users with limited history.
Abstract
The key to personalized search is to build the user profile based on historical behaviour. To deal with the users who lack historical data, group based personalized models were proposed to incorporate the profiles of similar users when re-ranking the results. However, similar users are mostly found based on simple lexical or topical similarity in search behaviours. In this paper, we propose a neural network enhanced method to highlight similar users in semantic space. Furthermore, we argue that the behaviour-based similar users are still insufficient to understand a new query when user's historical activities are limited. To tackle this issue, we introduce the friend network into personalized search to determine the closeness between users in another way. Since the friendship is often formed based on similar background or interest, there are plenty of personalized signals hidden in the…
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Taxonomy
TopicsInformation Retrieval and Search Behavior · Recommender Systems and Techniques · Expert finding and Q&A systems
