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Knowledge Graph Embedding by Translating on Hyperplanes
TLDR
This paper proposes TransH which models a relation as a hyperplane together with a translation operation on it and can well preserve the above mapping properties of relations with almost the same model complexity of TransE. Expand
Cross-domain sentiment classification via spectral feature alignment
TLDR
This work develops a general solution to sentiment classification when the authors do not have any labels in a target domain but have some labeled data in a different domain, regarded as source domain and proposes a spectral feature alignment (SFA) algorithm to align domain-specific words from different domains into unified clusters, with the help of domain-independent words as a bridge. Expand
Scalable collaborative filtering using cluster-based smoothing
TLDR
In this paper, clusters generated from the training data provide the basis for data smoothing and neighborhood selection and show that the new proposed approach consistently outperforms other state-of-art collaborative filtering algorithms. Expand
Knowledge Graph and Text Jointly Embedding
TLDR
Large scale experiments on Freebase and a Wikipedia/NY Times corpus show that jointly embedding brings promising improvement in the accuracy of predicting facts, compared to separately embedding knowledge graphs and text. Expand
Document Summarization Using Conditional Random Fields
TLDR
A Conditional Random Fields (CRF) based framework is presented to keep the merits of the above two kinds of approaches while avoiding their disadvantages and can take the outcomes of previous methods as features and seamlessly integrate them. Expand
Learning to cluster web search results
TLDR
This paper reformalizes the clustering problem as a salient phrase ranking problem, and first extracts and ranks salient phrases as candidate cluster names, based on a regression model learned from human labeled training data. Expand
Support vector machines classification with a very large-scale taxonomy
TLDR
The first evaluation of Support Vector Machines in web-page classification over the full taxonomy of the Yahoo! categories found that the hierarchical use of SVMs is efficient enough for very large-scale classification; however, in terms of effectiveness, the performance of SVM over the Yahoo!. Directory is still far from satisfactory, which indicates that more substantial investigation is needed. Expand
How much can behavioral targeting help online advertising?
TLDR
This work is the first empirical study for BT on the click-through log of real world ads and draws three important conclusions: users who clicked the same ad will truly have similar behaviors on the Web, Click-Through Rate (CTR) of an ad can be averagely improved as high as 670% by properly segmenting users for behavioral targeted advertising in a sponsored search. Expand
Enhancing text clustering by leveraging Wikipedia semantics
TLDR
A way to build a concept thesaurus based on the semantic relations (synonym, hypernym, and associative relation) extracted from Wikipedia is proposed and a unified framework to leverage these semantic relations in order to enhance traditional content similarity measure for text clustering is developed. Expand
Demographic prediction based on user's browsing behavior
TLDR
This paper made a first approach to predict users' gender and age from their Web browsing behaviors, in which the Webpage view information is treated as a hidden variable to propagate demographic information between different users. Expand
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