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Influence maximization is a recent but well-studied problem which helps identify a small set of users that are most likely to “influence” the maximum number of users in a social network. The problem has attracted a lot of attention as it provides a way to improve marketing, branding, and product adoption. However, existing studies rarely consider the physical locations of the users, but location is an important factor in targeted marketing. In this paper, we propose and investigate the problem of influence maximization in location-aware social networks, or, more generally, Geo-social Influence Spanning Maximization. Given a query $q$ composed of a region $R$ , a regional acceptance rate $\rho$ , and an integer $k$ as a seed selection budget, our aim is to find the maximum geographic spanning regions (MGSR). We refer to this as the MGSR problem. Our approach differs from previous work as we focus more on identifying the maximum spanning geographical regions within a region $R$ , rather than just the number of activated users in the given network like the traditional influence maximization problem  [14] . Our research approach can be effectively used for online marketing campaigns that depend on the physical location of social users. To address the MGSR problem, we first prove NP-Hardness. Next, we present a greedy algorithm with a $1-1/e$ approximation ratio to solve the problem, and further improve the efficiency by developing an upper bounded pruning approach. Then, we propose the OIR -Tree index, which is a hybrid index combining ordered influential node lists with an R -tree. We show that our index based approach is significantly more efficient than the greedy algorithm and the upper bounded pruning algorithm, especially when $k$ is large. Finally, we evaluate the performance for all of the proposed approaches using three real datasets. [ABSTRACT FROM AUTHOR]

• The reasons for recommendations are extracted from the preference aspects. • The rating-based aspect contributions are matched with verbal reviews. • Graphs of aspect contributions in ratings provide a visual rationale. Word clouds show the opinions in reviews that explain a recommendation. Recommender systems based on collaborative filtering suggest items to users according to the similarity of items or similarity of preferences of other users. Latent factor models produce rather accurate predictions of user preferences, but the latency of the features extracted make it difficult to substantiate a recommendation to a user. The realisation that many aspects tend to exist in rating and social connections data, such as social influence or bias, led to the development of a component-based matrix factorisation approach in earlier work. The ability to quantify the contributions of a component to a recommendation opens up possibilities to identify reasons for recommendations which can be presented to the consumer receiving them. Reviews that accompany rating data can be analysed and correlated with latent factors to provide more detailed reasons for recommendations. This paper introduces a general comprehensive framework which supplements earlier work that models a users' preferences and makes recommendations by extracting reasons from the recommendation framework as well as the accompanying reviews. Both rating-based and review-based reasons for recommending an item are presented in a visual manner. These visualisations help the user understand the origins of the recommendations but also assist businesses in identifying properties in users and communities that open up opportunities for targeted marketing and customer management. The usefulness of the explanation tool is demonstrated with an example recommendation of four items for a user in the Yelp restaurants dataset. [ABSTRACT FROM AUTHOR]

Influential community search (ICS) on a graph finds a closely connected group of vertices having a dominance over other groups of vertices. The ICS has many applications in recommendations, event organization, and so on. In this paper, we introduce a new variant of ICS, namely keyword-aware influential community query (KICQ), that finds the communities with the highest influential scores and whose keywords match with the query terms (a set of keywords) and predicates (AND or OR). It is challenging to find such communities from a large network as the traditional pre-computation approach is not applicable with the change of query terms at every instance of the search. To solve this problem, we design two efficient algorithms: (i) a branch-and-bound approach that exploits the bounds computed from already explored communities to prune the search space, and (ii) a novel index based approach that hierarchically organizes sub-communities and keywords with associated bounds to quickly identify the desired communities. We propose a new influence measure for a community that considers both the cohesiveness and influence of the community and eliminates the need for specifying values of internal parameters of a network. We present detailed experiments and a case study to demonstrate the effectiveness and efficiency of the proposed approaches. • The query considers user preference (as keywords) for influential community search. • Considering the semantics of the keywords adds flexibility for the users. • The proposed influence measure eliminates the need for internal parameters as input. • A novel index is proposed to efficiently retrieve the most influential communities. • The datasets are shared publicly to help future works in this direction. [ABSTRACT FROM AUTHOR]

To meet the requirement of social influence analytics in various applications, the problem of influence maximization has been studied in recent years. The aim is to find a limited number of nodes (i.e., users) which can activate (i.e. influence) the maximum number of nodes in social networks. However, the community diversity of influenced users is largely ignored even though it has unique value in practice. For example, the higher community diversity reduces the risk of marketing campaigns as you should not put all your eggs in one basket; the diversity can also prolong the effect of a marketing campaign in the future promotion. Motivated by this observation, this paper investigates Community-diversified Influence Maximization (CDIM) problem to efficiently find k nodes such that, if a message is initiated and spread by the k nodes, the number as well as the community diversity of the activated nodes will be maximized at the end of propagation process. This work proposes a metric to measure the community-diversified influence and addresses a series of computational challenges. Two algorithms and an innovative CPSP-Tree index have been developed. This study also investigates the situation that community definition is not specified. The effectiveness and efficiency of the proposed solutions have been verified through extensive experimental studies on five real-world social network datasets. • A new problem, named community-diversified influence maximization (CDIM), is introduced in this paper. • A deliberately designed metric has been proposed in this paper to evaluate the CDIM. • Two algorithms (greedy and upper bound) are developed in this paper to solve the CDIM problem effectively. [ABSTRACT FROM AUTHOR]