Influence maximization is the problem of finding influential users, or nodes, in a graph so as to maximize the spread of information. It has many applications in advertising and marketing on social networks. In this article, we study a highly generic version of influence maximization, one of optimizing influence campaigns by sequentially selecting “spread seeds” from a set of influencers , a small subset of the node population, under the hypothesis that, in a given campaign, previously activated nodes remain persistently active. This problem is in particular relevant for an important form of online marketing, known as influencer marketing , in which the marketers target a sub-population of influential people, instead of the entire base of potential buyers. Importantly, we make no assumptions on the underlying diffusion model, and we work in a setting where neither a diffusion network nor historical activation data are available. We call this problem online influencer marketing with persistence (in short, OIMP). We first discuss motivating scenarios and present our general approach. We introduce an estimator on the influencers’ remaining potential – the expected number of nodes that can still be reached from a given influencer – and justify its strength to rapidly estimate the desired value, relying on real data gathered from Twitter. We then describe a novel algorithm, GT-UCB, relying on probabilistic upper confidence bounds on the remaining potential. We show that our approach leads to high-quality spreads on both simulated and real datasets. Importantly, it is orders of magnitude faster than state-of-the-art influence maximization methods, making it possible to deal with large-scale online scenarios.

In this paper, we study a highly generic version of influence maximization (IM), one of optimizing influence campaigns by sequentially selecting "spread seeds" from a set of candidates, a small subset of the node population, under the hypothesis that, in a given campaign, previously activated nodes remain "persistently" active throughout and thus do not yield further rewards. We call this problem online influence maximization with persistence. We introduce an estimator on the candidates' missing mass - the expected number of nodes that can still be reached from a given seed candidate - and justify its strength to rapidly estimate the desired value. We then describe a novel algorithm, GT-UCB, relying on upper confidence bounds on the missing mass. We show that our approach leads to high-quality spreads on classic IM datasets, even though it makes almost no assumptions on the diffusion medium. Importantly, it is orders of magnitude faster than state-of-the-art IM methods.

Modern search applications feature real-time as-you-type query search. In its elementary form, the problem consists in retrieving a set of k search results, that is, performing a search with a given prefix, and showing the top-ranked results. In this article, we focus on as-you-type keyword search over social media, that is, data published by users who are interconnected through a social network. We adopt a “network-aware” interpretation for information relevance, by which information produced by users who are closer to the user issuing a request is considered more relevant. This query model raises new challenges for effectiveness and efficiency in online search, even when the intent of the user is fully specified, as a complete query given as input in one keystroke. This is mainly because it requires a joint exploration of the social space and traditional IR indexes, such as inverted lists. We describe a memory-efficient and incremental prefix-based retrieval algorithm, which also exhibits an anytime behavior, allowing output of the most likely answer within any chosen runtime limit. We evaluate our approach through extensive experiments for several applications and search scenarios. We consider searching for posts in microblogging (Twitter and Tumblr), for businesses (Yelp), as well as for movies (Amazon) based on reviews. We also conduct a series of experiments comparing our algorithm with baselines using state-of-the-art techniques and measuring the improvements brought by several key optimizations. They show that our solution is effective in answering real-time as-you-type searches over social media.

Sequentially learning to place items in multi-position displays or lists is a task that can be cast into the multiple-play semi-bandit setting. However, a major concern in this context is when the system cannot decide whether the user feedback for each item is actually exploitable. Indeed, much of the content may have been simply ignored by the user. The present work proposes to exploit available information regarding the display position bias under the so-called Position-based click model (PBM). We first discuss how this model differs from the Cascade model and its variants considered in several recent works on multiple-play bandits. We then provide a novel regret lower bound for this model as well as computationally efficient algorithms that display good empirical and theoretical performance.

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.