Collaboratively Tracking Interests for User Clustering in Streams of Short Texts

Abstract

In this paper, we aim at tackling the problem of user clustering in the context of their published short text streams. Clustering users by short text streams is more challenging than in the case of long documents associated with them as it is difficult to track users' dynamic interests in streaming sparse data. To obtain better user clustering performance, we propose two user collaborative interest tracking models that aim at tracking changes of each user's dynamic topic distributions in collaboration with their followees' dynamic topic distributions, based both on the content of current short texts and the previously estimated distributions. Our models can be either short-term or long-term dependency topic models. Short-term dependency model collaboratively tracks users' interests based on users' topic distributions at the previous time period only, whereas long-term dependency model collaboratively tracks users' interests based on users' topic distributions at multiple time periods in the past. We also propose two collapsed Gibbs sampling algorithms for collaboratively inferring users' dynamic interests for their clustering in our short-term and long-term dependency topic models, respectively. We evaluate our proposed models via a benchmark dataset consisting of Twitter users and their tweets. Experimental results validate the effectiveness of our proposed models that integrate both users' and their collaborative interests for user clustering by short text streams.

Bayesian Deep Collaborative Matrix Factorization

“…Experimental results show that our BDCMF can effective infer latent factors of users and items, and the Bayesian posterior estimation is more robust than point estimation. In the future, we will utilize the proposed model to deal with other information retrieval task such as user profiling (Liang 2018;Liang et al 2018b;Liang, Yilmaz, and Kanoulas 2018) and social network analysis.…”

Collaborative, Dynamic and Diversified User Profiling

“…Modeling Interests over Time. We close follow the previous work in (Liang, Yilmaz, and Kanoulas 2018;Liang et al 2017a), and aim at inferring each user's dynamic interest distribution θ t,u = {θ t,u,z } Z z=1 and his collaborative interest distribution ψ t,u = {ψ t,u,z } Z z=1 at t in the context of streams of short documents in our CITM. We provide CITM's graphical representation in Fig.…”

“…Since the well-known topic models, PLSI (Probabilistic Latent Semantic Indexing) (Hofmann 1999) and LDA (Latent Dirichlet Allocation) (Blei, Ng, and Jordan 2003), were proposed, topic models with dynamics have been widely studied. These include Dynamic Topic Model (Blei and Lafferty 2006), Dynamic Mixture Model (Wei, Sun, and Wang 2007), Topic over Time (Wang and McCallum 2006), Topic Tracking Model (Iwata et al 2009), and more recently, dynamic Dirichlet multinomial mixture topic model (Liang et al 2017c), user expertise tracking topic model (Liang 2018) and user collaborative interest tracking topic model (Liang, Yilmaz, and Kanoulas 2018). To our knowledge, none of existing dynamic topic models has considered the problem of user profiling for short texts that utilizes collaborative information to infer topic distributions.…”

“…x is a Digamma function. Our derivations of the update rules for α t , β t and γ t in (6) are analogous to those in (Liang, Yilmaz, and Kanoulas 2018;Liang et al 2017c;2017b).…”

Dynamic Embeddings for User Profiling in Twitter

“…All of these models learn the evolution of latent topics over time. More recent dynamic topic models include dynamic User Clustering Topic model (UCT) [25,42], dynamic topic model for search result diversification [26], collaborative user clustering topic model for streams [28], and Dynamic Clustering Topic model (DCT) [24]. In this work, we take a different approach that pivots on neural embedding models while at the same time we compare our approach to the state-ofthe-art dynamic topic models for user profiling [25].…”