A survey of recommender systems in Twitter

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

Regression and Learning to Rank Aggregation for User Engagement Evaluation

User engagement refers to the amount of interaction an instance (e.g., tweet, news, and forum post) achieves. Ranking the items in social media websites based on the amount of user participation in them, can be used in different applications, such as recommender systems. In this paper, we consider a tweet containing a rating for a movie as an instance and focus on ranking the instances of each user based on their engagement, i.e., the total number of retweets and favorites it will gain. For this task, we define several features which can be extracted from the meta-data of each tweet. The features are partitioned into three categories: user-based, movie-based, and tweet-based. We show that in order to obtain good results, features from all categories should be considered. We exploit regression and learning to rank methods to rank the tweets and propose to aggregate the results of regression and learning to rank methods to achieve better performance. We have run our experiments on an extended version of MovieTweeting dataset provided by ACM RecSys Challenge 2014. The results show that learning to rank approach outperforms most of the regression models and the combination can improve the performance significantly.Comment: In Proceedings of the 2014 ACM Recommender Systems Challenge, RecSysChallenge '1

Retweeting beyond expectation: Inferring interestingness in Twitter

Online social networks such as Twitter have emerged as an important mechanism for individuals to share information and post user generated content. However, filtering interesting content from the large volume of messages received through Twitter places a significant cognitive burden on users. Motivated by this problem, we develop a new automated mechanism to detect personalised interestingness, and investigate this for Twitter. Instead of undertaking semantic content analysis and matching of tweets, our approach considers the human response to content, in terms of whether the content is sufficiently stimulating to get repeatedly chosen by users for forwarding (retweeting). This approach involves machine learning against features that are relevant to a particular user and their network, to obtain an expected level of retweeting for a user and a tweet. Tweets observed to be above this expected level are classified as interesting. We implement the approach in Twitter and evaluate it using comparative human tweet assessment in two forms: through aggregated assessment using Mechanical Turk, and through a web-based experiment for Twitter users. The results provide confidence that the approach is effective in identifying the more interesting tweets from a user’s timeline. This has important implications for reduction of cognitive burden: the results show that timelines can be considerably shortened while maintaining a high degree of confidence that more interesting tweets will be retained. In conclusion we discuss how the technique could be applied to mitigate possible filter bubble effects

It Is Not Just What We Say, But How We Say Them: LDA-based Behavior-Topic Model

Textual information exchanged among users on online social network platforms provides deep understanding into user-s ’ interest and behavioral patterns. However, unlike tradi-tional text-dominant settings such as offline publishing, one distinct feature for online social network is users ’ rich inter-actions with the textual content, which, unfortunately, has not yet been well incorporated in the existing topic modeling frameworks. In this paper, we propose an LDA-based behavior-topic mod-el (B-LDA) which jointly models user topic interests and be-havioral patterns. We focus the study of the model on online social network settings such as microblogs like Twitter where the textual content is relatively short but user interactions on them are rich. We conduct experiments on real Twitter data to demonstrate that the topics obtained by our model are both informative and insightful. As an application of our B-LDA model, we also propose a Twitter followee rec-ommendation algorithm combining B-LDA and LDA, which we show in a quantitative experiment outperforms LDA with a significant margin.

A Latent Factor Model for Board Recommendations in Pinterest

The past two years have seen the rise of a new online social network – Pinterest – which has grown more rapidly than any other social network (now reaching 70 million users). Pinterest is primarily organized around photos (or “pins”), where users reveal their interests via organizing pins into self-assigned categorical boards. However, one of the key challenges for new and existing users of Pinterest is to find boards of interest from the overall collection of 750 million boards. Hence, this thesis focuses on the problem of board recommendation in Pinterest towards identifying personalized, high-quality boards without requiring exhaustive search or browsing by the user. Board recommendation in Pinterest is challenging for a number of critical reasons: (i) Unlike community-oriented recommenders for movies, books, and other media, boards are highly personalized and not viewed or rated by many others. (ii) Many pins and boards lack descriptive text and other features that are typically used to power modern recommenders. (iii) Finally, evaluating the quality of a Pinterest board recommender is difficult, since there are no baseline nor ground truth recommendations of Pinterest to compare against. With these challenges in mind, this thesis proposes a new latent factor model for generating Pinterest board recommendations. To tackle the feature sparsity and personal boards challenges, the overall approach generates ratings for every user-board pair which is then fed to a latent factor model which factorizes the sparse matrix to give ratings for unrated user-board pairs and the top rated boards form the recommendation list. Two of the key components of the proposed latent factor model are the (i) definition of the universe of users around each target user for identifying candidate boards to recommend; and (ii) the approach for assigning implicit ratings to each user-board pair for this universe of users (as the basis of the latent factor model). For the first component, we investigate three universe types: a collection of randomly selected users, a collection of users in the target user's personal Pinterest network, and a collection of users who are “similar” to the target user. For the second component, we construct ratings via three approaches: a board-count method, a category-based method, and and LDA-based method. We investigate these design choices through a comprehensive set of experiments over a dataset of around 50,000 Pinterest users, 100 million pins, and around 570,000 boards

Retweeter ou ne pas retweeter

L'étude des caractéristiques contextuelles a été largement traitée en Recherche d'Information (RI), mais les applications concrètes sur de vrais flux de données ne sont pas très répandues. Dans cet article, notre problématique concerne la décision automatique de retweeter un message. En considérant le centre d'intérêt d'un utilisateur, nous proposons un modèle pour effectuer un filtrage automatique en temps-réel du flux Twitter en utilisant de multiples caractéristiques contextuelles. Le modèle sépare l'aspect contextuel du contenu du message en lui-même, tout en conservant une très grande vitesse d'exécution. Notre modèle a été évalué dans le cadre des tâches TREC Microblog 2015 et TREC Real-Time Summarization 2016. Les résultats montrent la grande efficience (temps de retweet) de notre modèle, et son efficacité sur les mesures de 2015. Ces résultats en termes d'efficacité n'ont cependant pas été confirmés sur 2016. Ceci nous a conduit à une analyse plus en détail des résultats (approche et cadre d'évaluation). Cette analyse a notamment montré un biais dans l'évaluation, biais que nous discutons à la fin de l'article

MARKETING DE INFLUENCERS EN REDES SOCIALES

El marketing de influencers es un fenómeno nuevo que las empresas necesitan entender. Instagram es una red social pensada para conectar a las diferentes personas del mundo que tienen gustos, deseos y pensamientos en común sobre algún tema en específico; (Veissi, 2017). Una persona con influencia en las redes sociales puede lograr un impacto sobre la reputación de una marca en la mente de un gran número de usuarios de forma inmediata, algo que hace pocos años resultaba inverosímil (Anzures, 2016); a ello se suma que los consumidores brindan mayor credibilidad a las opiniones y experiencias compartidas por otros usuarios en la red sobre un producto o marca que a la comunicación que emana directamente de la marca con un conocido fin comercial (Carricajo Blanco, 2015) En este estudio se analiza las características que influyen en la intención y finalmente en la compra de productos/servicios por parte de los consumidores; este estudio fue basado en la realización de 381 encuestas a personas de España y Colombia que usan sitios web de redes sociales como Instagram (Sánchez Torres et al., 2018). Si los influenciadores, de la mano deldiscurso publicitario, realmente comparten los valores de marca con las empresas y su sentir en el que hacer en la sociedad; permitirá que las personas sientan a través de los influencers (validadores de mensajes ) que el mensaje es real, que las marcas se preocupan por el consumidor, que hay una relación bidireccional y que finalmente el consumidor es escuchado y puede elegir (Erkan & Evans, 2016)

Marketing de influência nas redes sociais: determinantes dos influenciadores digitais na influência social e impacto na intenção de compra dos seguidores

Versão final (esta versão já contém as críticas e sugestões dos elementos do júri)O marketing 4.0 surge da necessidade dos Marketers por todo o mundo reinventarem as suas mensagens e estratégias para se adptarem e conseguirem persuadir um consumidor mais exigente em relação às marcas e produtos que consome, que agora, passa a transitar grande parte da sua jornada de compra para o mundo digital, principalmente na parte de pesquisa por soluções. As redes sociais são um dos palcos para o consumidor atual, um local onde este pode agora não só ouvir as mensagens das marcas como falar de volta com as mesmas, local onde passou a ser muito mais fácil encontrar pessoas com valores, interesses e hábitos similares aos seus, criando assim comunidades que interagem entre si e partilham informação com uma grande regularidade, requirindo agora para a sua decisão de compra uma validação social grande e uma conexão emocial e pessoal com as marcas. Surgiram desta necessidade de validação e conexão, os influenciadores digitais, que são pessoas com um grande grupo de seguidores e que funcionam como lideres de opinião para esse grupo de seguidores, influenciando assim os hábitos de consumo em deterimento de determinadas marcas. Optou-se pelo uso de um questionário online para recolher dados, que permitissem testar um modelo conceptual que pudesse dar resposta ao objetivo desta investigação que é entender quais os determinantes da influência social dos influenciadores digitais e como esta influencia a decisão de compra dos seus seguidores. Através da análise de regressão conseguiu-se constatar que as variáveis reputação, tendências e similaridade tem um efeito positivo e significativo na influência social dos influenciadores digitais, já a variável atratividade não é estatisticamente significativa e como tal o seu efeito na influência social não é significativo. Por fim a influência social demonstrou ter um impacto positivo e significativo na intenção de compra dos seguidores.Marketing 4.0 arises from the need for Marketers all over the world to reinvent their messages and strategies in order to adapt themselves and be able to persuade a more demanding consumer in relation to the brands and products they consume, who now, passes through much of their buying journey for the digital world, mainly in the search for solutions. Social media networks are one of the stages for the current consumer, a place where they can now not only hear the messages of the brands but also talk back to them, a place where it has become much easier to find people with similar values, interests and habits, thus creating communities that interact with each other and share information with great regularity, now requiring for the consumer purchase decision a great social validation and an emotional and personal connection with the brands. From this need for validation and connection, digital influencers, who are people with a large group of followers and who act as opinion leaders for this group of followers, thus influencing the consumption habits of certain brands. We opted for the use of an online questionnaire to collect data, which would allow us to test a conceptual model that could answer the objective of this investigation, which is to understand what are the determinants of the social influence of digital influencers and how it influences the purchase decision of their followers . Through regression analysis it was found that the variables reputation, trends and similarity have a positive and significant effect on the social influence of digital influencers, whereas the attractiveness variable is not statistically significant and as such its effect on social influence is not significant . Finally, social influence has shown to have a positive and significant impact on the purchase intention of followers