Group based Personalized Search by Integrating Search Behaviour and Friend Network

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 friend network naturally. Specifically, we propose a friend network enhanced personalized search model, which groups the user into multiple friend circles based on search behaviours and friend relations respectively. These two types of friend circles are complementary to construct a more comprehensive group profile for refining the personalization. Experimental results show the significant improvement of our model over existing personalized search models.