Followee Recommendation Research Articles

Integrating Users’ Contextual Engagements with Their General Preferences: An Interpretable Followee Recommendation Method

Users’ contextual engagements can affect their decisions about who to follow on online social networks because engaged (versus disengaged) users tend to seek more information about the interested topic and are more likely to follow relevant accounts successively. However, existing followee recommendation methods neglect to consider contextual engagement by only relying on users’ general preferences. In the light of the chronological characteristic of the user’s following behavior, we draw on the engagement theory and propose an interpretable algorithm, namely preference-engagement latent Dirichlet allocation (PE-LDA), which integrates users’ contextual engagements with their general preferences for followee recommendation. Specifically, we suggest that if engaged in the current interest, a user will be more likely to select a followee relevant to that interest. If not, the user tends to select a followee according to their general preference. To implement this framework, we extend the original LDA by (1) introducing an indicator to represent whether the user is engaged in the current interest or not and (2) allowing a potential dependency between a user’s successive interests to describe the condition of contextual engagement. We conduct extensive experiments using a real-world Twitter data set. Results demonstrate the superior performance of PE-LDA compared with several existing methods. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was supported by the National Natural Science Foundation of China [Grants 71702066, 71802192, 71832010, 72172112, and 72272152]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1284 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0172 ) at ( http://dx.doi.org/10.5281/zenodo.7460938 ).

Incorporating LDA with LSTM for followee recommendation on Twitter network

PurposeThe purpose of this study is to facilitate the task of finding appropriate information to read about, and searching for people who are in the same field of interest. Knowing that more people keep up with new streaming information on Twitter micro-blogging service. With the immense number of micro-posts shared via the follower/followee network graph, Twitter users find themselves in front of millions of tweets, which makes the task crucial.Design/methodology/approachIn this paper, a long short–term memory (LSTM) model that relies on the latent Dirichlet allocation (LDA) output vector for followee recommendation, the LDA model applied as a topic modeling strategy is proposed.FindingsThis study trains the model using a real-life data set extracted based on Twitter follower/followee architecture. It confirms the effectiveness and scalability of the proposed approach. The approach improves the state-of-the-art models average-LSTM and time-LSTM.Research limitations/implicationsThis study improves mainly the existing followee recommendation systems. Because, unlike previous studies, it applied a non-hand-crafted method which is the LSTM neural network with LDA model for topics extraction. The main limitation of this study is the cold-start users cannot be treated, also some active fake accounts may not be detected.Practical implicationsThe aim of this approach is to assist users seeking appropriate information to read about, by choosing appropriate profiles to follow.Social implicationsThis approach consolidates the social relationship between users in a microblogging platform by suggesting like-minded people to each other. Thus, finding users with the same interests will be easy without spending a lot of time seeking relevant users.Originality/valueInstead of classic recommendation models, the paper provides an efficient neural network searching method to make it easier to find appropriate users to follow. Therefore, affording an effective followee recommendation system.

Influence and performance of user similarity metrics in followee prediction

Followee recommendation is a problem rapidly gaining importance in Twitter as well as in other micro-blogging communities. Hence, understanding how users select whom to follow becomes crucial for designing accurate and personalised recommendation strategies. This work aims at shedding some light on how homophily drives the formation of user relationships by studying the influence of diverse recommendation factors on tie formation. The selected recommendation factors were studied considering multiple alternatives for assessing them in terms of user similarity. A data analysis comparing the similarity among Twitter users and their followees, regarding two commonly used followee recommendation factors (topology and content) was performed in the context of a followee recommendation task. This study is among the firsts to analyse the effect of the different criteria for followee recommendation in micro-blogging communities, and the importance of thoroughly analysing the different aspects of user relationships to define the concept of user similarity. The study showed how the choice of the different factors and assessment alternatives affects followee recommendation. It also verified the existence of certain patterns regarding friends and random users’ similarities, which can condition the adequacy of the available similarity metrics.

FRFB: Top‐k Followee Recommendation by exploring the Following Behaviors in social networks

SummaryAs social networks such as micro‐blogging sites rapidly grow, deciding whom to follow (followee recommendation) becomes a significantly important problem. Most existing works exclusively rely on two traditional factors: the proximity between two users in the network topology or the similarity of the user‐generated contents in the social network, disregarding the effect of users' following behaviors. The challenge of how to effectively combine these two factors remains largely open. Moreover, most research studies simply sort the scores to find top‐k users, which is time‐consuming, especially for large‐scale networks. In this paper, we propose the idea that “predict users' following behaviors by following behaviors themselves.” We consider a user's following to others as a normal process of dynamic and coherent behavior, and we model the potential propagation of the users' following behaviors. Furthermore, based on our previous research on top‐k selection problem, we propose an effective top‐k followee recommendation algorithm, called FRFB. FRFB has low complexity and high scalability and, moreover, good adaptability to real‐life dynamic social networks. We conduct extensive experiments, with two real social network data sets (Wiki and Twitter), which show that FRFB outperforms the well‐known topology‐based followee recommendation algorithms.

Multi-perspective User2Vec: Exploiting re-pin activity for user representation learning in content curation social network

Content curation social networks (CCSN) develop rapidly. Pinterest and Huaban are two typical CCSNs. Recently, there is active research on CCSNs. As a kind of content based social network, CCSNs involve not only the explicit social relations from user “following”, but also content-based social relations from re-pin paths and so on. In this paper, we propose a novel user representation learning algorithm, Multi-perspective User2Vec Representation (MUVR). It combines the two types of social relations to get the rich user sequences. Then the representation learning is implemented by using the skip-gram algorithm. Experimental results on Huaban.com demonstrate that the proposed algorithm can represent network well. It presents more competitive results in the followee recommendation, re-pinner recommendation and multi-label classification.

Hybrid followee recommendation in microblogging systems

Followee recommendation plays an important role in information sharing over microblogging plat-forms. Existing followee recommendation schemes adopt either content relevance or social information for followee ranking, suffering poor performance. Based on the observation that microblogging systems have dual roles of social network and news media platform, we propose a novel followee recommendation scheme that takes into account the information sources of both tweet contents and the social structures. We set up a linear weighted model to combine the two factors and further design a simulated annealing algorithm to automatically assign the weights of both factors in order to achieve an optimized combination of them. We conduct comprehensive experiments on real-world datasets collected from Sina Weibo, the largest microblogging system in China. The results demonstrate that our scheme provides a much more accurate followee recommendation for a user compared to existing schemes.

Exploring the role of personality traits in followee recommendation

Purpose– Followee recommendation is a problem rapidly gaining importance in Twitter as well as in other micro-blogging communities. To find interesting users to follow, most recommendation systems leverage different factors such as graph topology or user-generated content, among others. Those systems mostly disregard, however, the effect of psychological characteristics, such as personality, over the followee selection process. As personality is considered one of the primary factors that influence human behaviour, the purpose of this paper is to shed some light on the impact of personality traits on followee selection.Design/methodology/approach– The authors performed a data analysis comparing the similarity among Twitter users and their followees regarding personality traits. The authors analysed three different similarity measures. First, the authors computed an overall similarity considering the five personality traits or dimensions of the Five-Factor model as a whole. Second, the authors computed the dimension-to-dimension similarity considering each individual personality trait independently of each other. Third, the authors computed a cross-dimension similarity considering each personality dimension in relation to the others.Findings– This study showed that personality should be considered as a distinctive factor in the process of followee selection. However, personality dimensions should not be analysed as a whole as the overall personality similarity might not accurately assess the actual matching between individuals. Instead, the performed data analysis showed the existence of relations among the individual dimensions. Thus, the importance of considering each personality trait with respect to others is stated.Originality/value– This study is among the firsts to study the impact of personality, one of the primary factors that influence human behaviour and social relationships, in the selection of followees in micro-blogging communities.

An architecture and platform for developing distributed recommendation algorithms on large-scale social networks

The creation of new and better recommendation algorithms for social networks is currently receiving much attention owing to the increasing need for new tools to assist users. The volume of available social data as well as experimental datasets force recommendation algorithms to scale to many computers. Given that social networks can be modelled as graphs, a distributed graph-oriented support able to exploit computer clusters arises as a necessity. In this work, we propose an architecture, called Lightweight-Massive Graph Processing Architecture, which simplifies the design of graph-based recommendation algorithms on clusters of computers, and a Java implementation for this architecture composed of two parts: Graphly, an API offering operations to access graphs; and jLiME, a framework that supports the distribution of algorithm code and graph data. The motivation behind the creation of this architecture is to allow users to define recommendation algorithms through the API and then customize their execution using job distribution strategies, without modifying the original algorithm. Thus, algorithms can be programmed and evaluated without the burden of thinking about distribution and parallel concerns, while still supporting environment-level tuning of the distributed execution. To validate the proposal, the current implementation of the architecture was tested using a followee recommendation algorithm for Twitter as case study. These experiments illustrate the graph API, quantitatively evaluate different job distribution strategies w.r.t. recommendation time and resource usage, and demonstrate the importance of providing non-invasive tuning for recommendation algorithms.

Diffusion of “Following” Links in Microblogging Networks

When a “following” link is formed in a social network, will the link trigger the formation of other neighboring links? We study the diffusion phenomenon of the formation of “following” links by proposing a model to describe this link diffusion process. To estimate the diffusion strength between different links, we first conduct an analysis on the diffusion effect in 24 triadic structures and find evident patterns that facilitate the effect. We then learn the diffusion strength in different triadic structures by maximizing an objective function based on the proposed model. The learned diffusion strength is evaluated through the task of link prediction and utilized to improve the applications of follower maximization and followee recommendation, which are specific instances of influence maximization. Our experimental results reveal that incorporating diffusion patterns can indeed lead to statistically significant improvements over the performance of several alternative methods, which demonstrates the effect of the discovered patterns and diffusion model.

Top-[formula omitted] followee recommendation over microblogging systems by exploiting diverse information sources

Followee recommendation plays an important role in information sharing over microblogging platforms. We frame this problem as a top-k ranking in collaborative filtering (CF). The difficulty is that explicit user-to-user ratings are not available on microblogging systems. Thus existing CF schemes are not applicable to followee recommendation over microblogging systems. To solve this problem, in this paper, we propose a novel followee ranking scheme using a variation of the latent factor model, which leverages implicit users’ feedback including both tweet content and social relation information. To achieve good top-k recommendation, we introduce a rank-based criterion to latent factor model (LFM). The main obstacle for training the model parameters is the non-smoothness of the objective function of LFM, which makes traditional parameter optimization methods infeasible. To tackle with the problem, we further design a smooth version of the objective function. We conduct comprehensive experiments on a large-scale dataset collected from Sina Weibo, the most popular microblogging system in China and a real world experiment on the Amazon Mechanical Turk CrowdSourcing platform to evaluate the performance of our design. The results show that our scheme greatly outperforms existing schemes in terms of precision and top-k ranking by 46.8% and 32.8%, respectively.

The Followee Recommendation Algorithm Based on Microblog User Interest and Characteristic

Different from conventional social networks and e-commerce systems, microblog community is unique for its low user activity, data sparsity and dynamic of user’s interests. Because of these challenges, conventional recommendation algorithms cannot get a satisfactory performance in microblog community. This paper proposes a microblog followee recommendation algorithm based on user interest degree and attribute characteristics after analyzing the structure of microblog recommendation, and this algorithm is mainly on the basis of user cf recommendation algorithm to recommend new followee to user. Firstly, group users through calculating users interest degree to followee; Secondly, get users’ preferences value to followee with Bayesian algorithm when they are with different attribute characteristics; Finally, calculate target user’s nearest neighbor set using a new optimized similarity degree method to form the recommendation list. Experiments show that this proposed algorithm enhances the effectiveness and accuracy of the nearest neighbor set, and the recommendation quality has significantly improvement compared with previous algorithm.

Followee recommendation in microblog using matrix factorization model with structural regularization.

Microblog that provides us a new communication and information sharing platform has been growing exponentially since it emerged just a few years ago. To microblog users, recommending followees who can serve as high quality information sources is a competitive service. To address this problem, in this paper we propose a matrix factorization model with structural regularization to improve the accuracy of followee recommendation in microblog. More specifically, we adapt the matrix factorization model in traditional item recommender systems to followee recommendation in microblog and use structural regularization to exploit structure information of social network to constrain matrix factorization model. The experimental analysis on a real-world dataset shows that our proposed model is promising.

Followee recommendation based on text analysis of micro-blogging activity

Nowadays, more and more users keep up with news through information streams coming from real-time micro-blogging activity offered by services such as Twitter. In these sites, information is shared via a followers/followees social network structure in which a follower receives all the micro-blogs from his/her followees. Recent research efforts on understanding micro-blogging as a novel form of communication and news spreading medium have identified three different categories of users in these systems: information sources, information seekers and friends. As social networks grow in the number of registered users, finding relevant and reliable users to receive interesting information becomes essential. In this paper we propose a followee recommender system based on both the analysis of the content of micro-blogs to detect users' interests and in the exploration of the topology of the network to find candidate users for recommendation. Experimental evaluation was conducted in order to determine the impact of different profiling strategies based on the text analysis of micro-blogs as well as several factors that allows the identification of users acting as good information sources. We found that user-generated content available in the network is a rich source of information for profiling users and finding like-minded people.