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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.


Papers
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Proceedings ArticleDOI
12 Oct 2013
TL;DR: A probabilistic latent semantic indexing model for pairwise learning (Pairwise PLSI), which assumes a set of users' latent preferences between pairs of items, is proposed.
Abstract: Many online systems present a list of recommendations and infer user interests implicitly from clicks or other contextual actions. For modeling user feedback in such settings, a common approach is to consider items acted upon to be relevant to the user, and irrelevant otherwise. However, clicking some but not others conveys an implicit ordering of the presented items. Pairwise learning, which leverages such implicit ordering between a pair of items, has been successful in areas such as search ranking. In this work, we study whether pairwise learning can improve community recommendation. We first present two novel pairwise models adapted from logistic regression. Both offline and online experiments in a large real-world setting show that incorporating pairwise learning improves the recommendation performance. However, the improvement is only slight. We find that users' preferences regarding the kinds of communities they like can differ greatly, which adversely affect the effectiveness of features derived from pairwise comparisons. We therefore propose a probabilistic latent semantic indexing model for pairwise learning (Pairwise PLSI), which assumes a set of users' latent preferences between pairs of items. Our experiments show favorable results for the Pairwise PLSI model and point to the potential of using pairwise learning for community recommendation.

24 citations

Proceedings ArticleDOI
27 Oct 2013
TL;DR: This paper proposes a principled and scalable approach for integrating of latent semantic information into a learning-to-rank model, by combining compact representation of semantic similarity, achieved by using a modified algorithm for tensor factorization, with explicit entity information.
Abstract: Entity ranking has become increasingly important, both for retrieving structured entities and for use in general web search applications. The most common format for linked data, RDF graphs, provide extensive semantic structure via predicate links. While the semantic information is potentially valuable for effective search, the resulting adjacency matrices are often sparse, which introduces challenges for representation and ranking. In this paper, we propose a principled and scalable approach for integrating of latent semantic information into a learning-to-rank model, by combining compact representation of semantic similarity, achieved by using a modified algorithm for tensor factorization, with explicit entity information. Our experiments show that the resulting ranking model scales well to the graphs with millions of entities, and outperforms the state-of-the-art baseline on realistic Yahoo! SemSearch Challenge data sets.

24 citations

Journal ArticleDOI
TL;DR: This paper proposes a novel hierarchical video event detection model, which deliberately unifies the processes of underlying semantics discovery and event modeling from video data and devise an effective model to automatically uncover video semantics by hierarchically capturing latent static-visual concepts in frame-level and latent activity concepts in segment-level.
Abstract: Semantic information is important for video event detection. How to automatically discover, model, and utilize semantic information to facilitate video event detection has been a challenging problem. In this paper, we propose a novel hierarchical video event detection model, which deliberately unifies the processes of underlying semantics discovery and event modeling from video data. Specially, different from most of the approaches based on manually pre-defined concepts, we devise an effective model to automatically uncover video semantics by hierarchically capturing latent static-visual concepts in frame-level and latent activity concepts (i.e., temporal sequence relationships of static-visual concepts) in segment-level. The unified model not only enables a discriminative and descriptive representation for videos, but also alleviates error propagation problem from video representation to event modeling existing in previous methods. A max-margin framework is employed to learn the model. Extensive experiments on four challenging video event datasets, i.e., MED11, CCV, UQE50, and FCVID, have been conducted to demonstrate the effectiveness of the proposed method.

24 citations

Proceedings Article
07 Aug 2002
TL;DR: This work introduces an algorithm for discovering partitions of observed variables such that members of a class share only a single latent common cause and requires no prior knowledge of the number of latent variables, and does not depend on the mathematical form of the relationships among the latent variables.
Abstract: Observed associations in a database may be due in whole or part to variations in unrecorded ("latent") variables. Identifying such variables and their causal relationships with one another is a principal goal in many scientific and practical domains. Previous work shows that, given a partition of observed variables such that members of a class share only a single latent common cause, standard search algorithms for causal Bayes nets can infer structural relations between latent variables. We introduce an algorithm for discovering such partitions when they exist. Uniquely among available procedures, the algorithm is (asymptotically) correct under standard assumptions in causal Bayes net search algorithms, requires no prior knowledge of the number of latent variables, and does not depend on the mathematical form of the relationships among the latent variables. We evaluate the algorithm on a variety of simulated data sets.

24 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202319
202277
202114
202036
201927
201858