Personalized Point of Interest recommendation is very helpful for satisfying users' needs at new places. In this article, we propose a tag embedding based method Recommendation Of Interest. We model the relationship between tags corresponding to The provides representative corresponds in way that related will be closer. Interest-based on and also users (user profile) rated by them. finally, ran...

This document describes the participation of Vienna University of Technology in the TREC Clinical Decision Support Track 2014. Four different search models are investigated, as well as different strategies to index the corpus and to extract the most relevant information from the topics. Our results conclude that BM25 and Vector Space Model had similar performance for P@10 and inferred NDCG.

In this paper we, Team IRLabDAIICT, describe our participation in the ShARe/CLEF ehealth 2014 task 3: Information Retrieval for addressing questions related to patients health based on clinical reports. We submitted a total of six runs out of the seven in this years task. In our approach we focus on examining the relevance between the documents and user generated query by conducting experiments...

For many internet users, searching for health advice online is the first step in seeking treatment. We present a Learning to Rank system that uses a novel set of syntactic and semantic features to improve consumer health search. Our approach was evaluated on the 2016 CLEF eHealth dataset, outperforming the best method by 26.6% in NDCG@10.

Drugs with similar side-effect profiles may share similar therapeutic properties through related mechanisms of action. In this study, a drug-drug network was constructed based on the similarities between their clinical side effects. The indications of a drug may be inferred by the enriched FDA-approved functions of its neighbouring drugs in the network. We systematically screened new indication...

This paper describes the ITNLP system participated in the Short Text Conversation (STC Chinese subtask) of the NTCIR12. We employed a Logistic Regression Model combined with pattern-based matching features to solve the STC problem. Deep learning methods were also tried in our experiment. Out of the 44 submitted runs our best performance run ITNLP-C-R3 ranked 8(Mean nDCG@1),12(Mean P+) and 9(Mea...

This paper describes our subtopic mining system for the NTCIR-9 INTENT task. We propose a method that mines subtopics for each topic only using the given Chinese query log. Our method finds possible subtopics and estimates scores of them based on interest and clearness. In the Chinese subtopic mining, our best values of D#-nDCG were 0.3823 for l = 10, 0.4413 for l = 20 and 0.4241 for l = 30.

Overfitting in a deep neural network leads to low recommendation precision and high loss. To mitigate these issues network-based algorithm, we propose LG-DropEdge, joint light graph convolutional network, the DropEdge. First, reduce cost of data storage calculation, initialize user item embedding layer algorithm. Then, obtain high-order interaction relationships optimize representation, enrich ...