Probabilistic latent semantic analysis

About: Probabilistic latent semantic analysis is a research topic. Over the lifetime, 2884 publications have been published within this topic receiving 198341 citations. The topic is also known as: PLSA.

Deep Semantic Mapping for Cross-Modal Retrieval

Abstract: Cross-Modal mapping plays an essential role in multimedia information retrieval systems. However, most of existing work paid much attention on learning mapping functions but neglected the exploration of high-level semantic representation of modalities. Inspired by recent success of deep learning, in this paper, deep CNN (convolutional neural networks) features and topic features are utilized as visual and textual semantic representation respectively. To investigate the highly non-linear semantic correlation between image and text, we propose a regularized deep neural network(RE-DNN) for semantic mapping across modalities. By imposing intra-modal regularization as supervised pre-training, we finally learn a joint model which captures both intra-modal and inter-modal relationships. Our approach is superior to previous work in follows: (1) it explores high-level semantic correlations, (2) it requires little prior knowledge for model training, (3) it is able to tackle modality missing problem. Extensive experiments on benchmark Wikipedia dataset show RE-DNN outperforms the state-of-the-art approaches in cross-modal retrieval.

Aspect-based latent factor model by integrating ratings and reviews for recommender system

Abstract: Recommender system has been recognized as a superior way for solving personal information overload problem. Rating, as an evaluation criteria revealing how much a customer likes a product, has been a foundation of recommender systems for a long period based on the popular latent factor models. However, review texts as the valuable user generated content have been neglected all the time. Recently, models integrating ratings and review texts as training sources have attracted a lot of attention, which may model review texts by topic model or its variants and then link latent factor vectors to topic distribution of review texts. For that, the integrated models need complicated optimization algorithms to fuse the heterogeneous sources, that may cause greater errors.In this work, we aim to propose a novel model, called Aspect-based Latent Factor Model (ALFM) to integrate ratings and review texts via latent factor model, in which by integrating rating matrix, user-review matrix and item-attribute matrix, the user latent factors and item latent factors with word latent factors can be derived. Our proposed model aggregates all review texts of the same user on the respective items and builds a user-review matrix by word frequencies. Similarly, an item's review is considered as all review texts of the same item collected from respective users. According to different information abstracted from review texts, we introduce two different kinds of item-attribute matrix to integrate the item-word frequencies and polarity scores of corresponding words. Experimental results on real-world data sets from amazon.com illustrate that our model can not only perform better than traditional models and art-of-state models on rating prediction task, but also accomplish cross-domain task through transferring word embedding.

Manifold Relevance Determination

Abstract: In this paper we present a fully Bayesian latent variable model which exploits conditional nonlinear (in)-dependence structures to learn an efficient latent representation. The latent space is factorized to represent shared and private information from multiple views of the data. In contrast to previous approaches, we introduce a relaxation to the discrete segmentation and allow for a "softly" shared latent space. Further, Bayesian techniques allow us to automatically estimate the dimensionality of the latent spaces. The model is capable of capturing structure underlying extremely high dimensional spaces. This is illustrated by modelling unprocessed images with tenths of thousands of pixels. This also allows us to directly generate novel images from the trained model by sampling from the discovered latent spaces. We also demonstrate the model by prediction of human pose in an ambiguous setting. Our Bayesian framework allows us to perform disambiguation in a principled manner by including latent space priors which incorporate the dynamic nature of the data.

Supervised latent semantic indexing for document categorization

Abstract: Latent semantic indexing (LSI) is a successful technology in information retrieval (IR) which attempts to explore the latent semantics implied by a query or a document through representing them in a dimension-reduced space. However, LSI is not optimal for document categorization tasks because it aims to find the most representative features for document representation rather than the most discriminative ones. In this paper, we propose supervised LSI (SLSI) which selects the most discriminative basis vectors using the training data iteratively. The extracted vectors are then used to project the documents into a reduced dimensional space for better classification. Experimental evaluations show that the SLSI approach leads to dramatic dimension reduction while achieving good classification results.

A Unified Approach to Personalization Based on Probabilistic Latent Semantic Models of Web Usage and Content

Abstract: Web usage mining techniques, such as clustering of user sessions, are often used to identify Web user access patterns. However, to understand the factors that lead to common navigational patterns, it is necessary to develop techniques that can automatically characterize users’ navigational tasks and intentions. Such a characterization must be based both on the common usage patterns, as well as on common semantic information associated with the visited Web resources. The integration of semantic content and usage patterns allows the system to make inferences based on the underlying reasons for which a user may or may not be interested in particular items. In this paper, we propose a unified framework based on Probabilistic Latent Semantic Analysis to create models of Web users, taking into account both the navigational usage data and the Web site content information. Our joint probabilistic model is based on a set of discovered latent factors that “explain” the underlying relationships among pageviews in terms of their common usage and their semantic relationships. Based on the discovered user models, we propose algorithms for characterizing Web user segments and to provide dynamic and personalized recommendations based on these segments. Our experiments, performed on real usage data, show that this approach can more accurately capture users’ access patterns and generate more effective recommendations, when compared to more traditional methods based on clustering.