Topic
Latent Dirichlet allocation
About: Latent Dirichlet allocation is a research topic. Over the lifetime, 5351 publications have been published within this topic receiving 212555 citations. The topic is also known as: LDA.
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01 Jan 2013
TL;DR: A Topic and Location-aware probabilistic matrix factorization (TL-PMF) method is proposed for POI recommendation to consider both the extent to which a user interest matches the POI in terms of topic distribution and the word-of-mouth opinions of the POIs.
Abstract: The wide spread use of location based social networks (LBSNs) has enabled the opportunities for better location based services through Point-of-Interest (POI) recommendation. Indeed, the problem of POI recommendation is to provide personalized recommendations of places of interest. Unlike traditional recommendation tasks, POI recommendation is personalized, locationaware, and context depended. In light of this difference, this paper proposes a topic and location aware POI recommender system by exploiting associated textual and context information. Specifically, we first exploit an aggregated latent Dirichlet allocation (LDA) model to learn the interest topics of users and to infer the interest POIs by mining textual information associated with POIs. Then, a Topic and Location-aware probabilistic matrix factorization (TL-PMF) method is proposed for POI recommendation. A unique perspective of TL-PMF is to consider both the extent to which a user interest matches the POI in terms of topic distribution and the word-of-mouth opinions of the POIs. Finally, experiments on real-world LBSNs data show that the proposed recommendation method outperforms state-of-the-art probabilistic latent factor models with a significant margin. Also, we have studied the impact of personalized interest topics and word-of-mouth opinions on POI recommendations.
194 citations
30 Jun 2010
TL;DR: An empirical study to statistically analyze the equivalence of several traceability recovery methods based on Information Retrieval techniques shows that while JS, VSM, and LSI are almost equivalent, LDA is able to capture a dimension unique to the set of techniques which the authors considered.
Abstract: We present an empirical study to statistically analyze the equivalence of several traceability recovery methods based on Information Retrieval (IR) techniques. The analysis is based on Principal Component Analysis and on the analysis of the overlap of the set of candidate links provided by each method. The studied techniques are the Jensen-Shannon (JS) method, Vector Space Model (VSM), Latent Semantic Indexing (LSI), and Latent Dirichlet Allocation (LDA). The results show that while JS, VSM, and LSI are almost equivalent, LDA is able to capture a dimension unique to the set of techniques which we considered.
192 citations
16 Apr 2012
TL;DR: It is shown that for a dataset constructed from the Stackoverflow website, these topic models outperform other methods in retrieving a candidate set of best experts for a question and that the Segmented Topic Model gives consistently better performance compared to the Latent Dirichlet Allocation Model.
Abstract: Community Question Answering (CQA) websites provide a rapidly growing source of information in many areas. This rapid growth, while offering new opportunities, puts forward new challenges. In most CQA implementations there is little effort in directing new questions to the right group of experts. This means that experts are not provided with questions matching their expertise, and therefore new matching questions may be missed and not receive a proper answer. We focus on finding experts for a newly posted question. We investigate the suitability of two statistical topic models for solving this issue and compare these methods against more traditional Information Retrieval approaches. We show that for a dataset constructed from the Stackoverflow website, these topic models outperform other methods in retrieving a candidate set of best experts for a question. We also show that the Segmented Topic Model gives consistently better performance compared to the Latent Dirichlet Allocation Model.
192 citations
TL;DR: Data placement, pipeline processing, word bundling, and priority-based scheduling are proposed to improve scalability of LDA and significantly reduce the unparallelizable communication bottleneck and achieve good load balancing.
Abstract: Previous methods of distributed Gibbs sampling for LDA run into either memory or communication bottlenecks. To improve scalability, we propose four strategies: data placement, pipeline processing, word bundling, and priority-based scheduling. Experiments show that our strategies significantly reduce the unparallelizable communication bottleneck and achieve good load balancing, and hence improve scalability of LDA.
190 citations
19 Feb 2008
TL;DR: Preliminary results indicate that LDA is able to identify some of the domain topics and is a satisfactory starting point for further manual refinement of topics, and a human assisted approach based on LDA for extracting domain topics from source code is proposed.
Abstract: One of the difficulties in maintaining a large software system is the absence of documented business domain topics and correlation between these domain topics and source code. Without such a correlation, people without any prior application knowledge would find it hard to comprehend the functionality of the system. Latent Dirichlet Allocation (LDA), a statistical model, has emerged as a popular technique for discovering topics in large text document corpus. But its applicability in extracting business domain topics from source code has not been explored so far. This paper investigates LDA in the context of comprehending large software systems and proposes a human assisted approachbased on LDA for extracting domain topics from source code. This method has been applied on a number of open source and proprietary systems. Preliminary results indicate that LDA is able to identify some of the domain topics and isa satisfactory starting point for further manual refinement of topics
188 citations