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The mobile devices have become an essential item for many people in developed countries. Thanks to small software called apps, we can use these devices for a lot of tasks. This article proposes to use these mobile devices in combination with large databases of pollen in a region to track the health status of the patient and the detected levels of pollen to obtain possible relationships that result in the diagnosis of the user.

We have proposed an information filtering system using index word selection from a document set based on the topics included in a set of documents. This method narrows down the particularly characteristic words in a document set and the topics are obtained by Sparse Non-negative Matrix Factorization. In information filtering, a document is often represented with the vector in which the elements correspond to the weight of the index words, and the dimension of the vector becomes larger as the number of documents is increased. Therefore, it is possible that useless words as index words for the information filtering are included. In order to address the problem, the dimension needs to be reduced. Our proposal reduces the dimension by selecting index words based on the topics included in a document set. We have applied the Sparse Non-negative Matrix Factorization to the document set to obtain these topics. The filtering is carried out based on a centroid of the learning document set. The centroid is regarded as the user-s interest. In addition, the centroid is represented with a document vector whose elements consist of the weight of the selected index words. Using the English test collection MEDLINE, thus, we confirm the effectiveness of our proposal. Hence, our proposed selection can confirm the improvement of the recommendation accuracy from the other previous methods when selecting the appropriate number of index words. In addition, we discussed the selected index words by our proposal and we found our proposal was able to select the index words covered some minor topics included in the document set., {"references":["G.Salton, M.J.McGill: \"Introduction to Modern Information\nRetrieval\", McGraw-Hill Book Company, 1983.","P.O.Hoyer, \"Non-negative Matrix Factorization with Sparseness\nConstraints'\", Journal of Machine Learning Research, Vol. 5, pp.\n1457-1469, 2004.","D.Lee and H.Seung, \"Algorithms for non-negative matrix\nfactorization\", NIPS 2000, 2000.","D.Lee and H.Seung, \"Learning the parts of objects by\nnon-negative matrix factorization\", Nature, Vol. 401,\npp.788-791","S.Tsuge, M.Shishibori, S.Kuroiwa and K.Kita: \"Dimensionality\nReduction Using Non-negative Matrix Factorization for\nInformation Retrieval\", Natural Language Processing and\nKnowledge Engineering Mini Symposium, IEEE SYSTEMS,\nMAN, AND CYBERNETICS 2001 (NLPKE), pp.960-965,\n2001","E. P. Jiang: \"Information Retrieval and Filtering Using the\nRiemannian SVD\", Ph.D. Thesis, Dept. of Computer Science,\nThe University of Tennessee, Knoxville, TN, 1988.","S.Deerwester, T.Dumais, T.Landauer, W.Furnas and\nA.Harshman: \"Indexing by Latent Semantic Analysis\", Journal\nof the Society for Information Science, Vol.41, No.6,\npp.391-497","T.Kolenda and L.K.Hansen: \"Independent Components in Text\",\nAdvances in Independent Component Analysis, Springer-Verlag,\n2000.","T.Yokoi, H.Yanagimoto and S.Omatu: \"The Proposal for the\nWay to Recommend Information with ICA\", The Ninth Int.\nSynp. on Artificial Life and Robotics(AROB 9th '04), Proc. pp.\n694-697, 2004\n[10] Xu. W., Liu. X., Gong. Y.:\"Document Clustering Based On\nNon-negative Matrix Factorization\", Proceedings of SIGIR-03,\npp.267-273, 2003.\n[11] M.W. Berry, M. Browne, A.N. Langville, \"Algorithms and\nApplications for Approximate Nonnegative Matrix\nFactorization\", V.P. Pauca, and R.J. Plemmons, Computational\nStatistics & Data Analysis 52(1), pp. 155-173, 2007.\n[12] P.O.Hoyer, \"Nonnegative Sparse Coding\", Proc. IEEE\nWorkshop Neural Networks for Signal Processing, 2002\n[13] Xu.W., Liu. X., Gong. Y., \"Nonnegative Matrix Factorization for\nVisual Coding\", Proc. IEEE Int. Conf. Acoustics, Speech, and\nSignal Processing(ICASSP2003), 2003\n[14] Y.Matsuo and M.Ishizuka, \"Keyword Extraction from a Single\nDocument using Word Co-occurrence Statistical Information\",\nInt'l Journal on Artificial Intelligence Tools, Vol.13, No.1,\npp.157-169, 2004\n[15] Yukio Ohsawa, Nels E. Benson and Masahiko Yachida,\n\"KeyGraph: Automatic Indexing by Co-occurrence Graph based\non Building Construction Metaphor\", Proc. Advanced Digital\nLibrary Conference (IEEE ADL'98), pp.12-18 (1998)\n[16] J. Rocchio: \"Relevance Feedback in Information Retrieval\", The\nSMART Retrieval System Experiments in Automatic Document\nProcessing, pp313-323, 1971.\n[17] SMART stop-list\nftp://ftp.cs.cornell.edu/pub/smart/english.stop"]}