A Network-Aware Approach for Searching As-You-Type in Social Media (Extended Version)

TL;DR

This paper introduces a network-aware, incremental search algorithm for as-you-type social media queries, effectively combining social proximity and IR indexes to improve real-time search relevance and efficiency.

Contribution

It proposes a novel, memory-efficient, prefix-based retrieval algorithm that integrates social proximity into real-time search over social media, with proven effectiveness through extensive experiments.

Findings

Effective real-time social media search demonstrated in micro-blogging and review platforms

Algorithm exhibits anytime behavior, providing flexible response times

Significant improvements in search relevance and efficiency over baseline methods

Abstract

We present in this paper a novel approach for as-you-type top-$k$ keyword search over social media. We adopt a natural "network-aware" interpretation for information relevance, by which information produced by users who are closer to the seeker is considered more relevant. In practice, this query model poses new challenges for effectiveness and efficiency in online search, even when a complete query is given as input in one keystroke. This is mainly because it requires a joint exploration of the social space and classic IR indexes such as inverted lists. We describe a memory-efficient and incremental prefix-based retrieval algorithm, which also exhibits an anytime behavior, allowing to output the most likely answer within any chosen running-time limit. We evaluate it through extensive experiments for several applications and search scenarios, including searching for posts in micro-blogging (Twitter and Tumblr), as well as searching for businesses based on reviews in Yelp. They show that our solution is effective in answering real-time as-you-type searches over social media.