This paper proposes a method that analyzes the location log data of multiple users to recommend locations to be visited. The method uses our new topic model, called Geo Topic Model, that can jointly estimate both the user's interests and activity area hosting the user's home, office and other personal places. By explicitly modeling geographical features of locations and users, the user's interests in other features of locations, which we call latent topics, can be inferred effectively. The topic interests estimated by our model 1) lead to high accuracy in predicting visit behavior as driven by personal interests, 2) make possible the generation of recommendations when the user is in an unfamiliar area (e.g. sightseeing), and 3) enable the recommender system to suggest an interpretable representation of the user profile that can be customized by the user. Experiments are conducted using real location logs of landmark and restaurant visits to evaluate the recommendation performance of the proposed method in terms of the accuracy of predicting visit selections. We also show that our model can estimate latent features of locations such as art, nature and atmosphere as latent topics, and describe each user's preference based on them.

We propose a classification model of tweet streams in Twitter, which are representative of document streams whose statistical properties will change over time. Our model solves several problems that hinder the classification of tweets; in particular, the problem that the probabilities of word occurrence change at different rates for different words. Our model switches between two probability estimates based on full and recent data for each word when detecting changes in word probability. This switching enables our model to achieve both accurate learning of stationary words and quick response to bursty words. We then explain how to implement our model by using a word suffix array, which is a full-text search index. Using the word suffix array allows our model to handle the temporal attributes of word n-grams effectively. Experiments on three tweet data sets demonstrate that our model offers statistically significant higher topic-classification accuracy than conventional temporally-aware classification models.

We present a method of document area identification that utilizes consecutive characters in the non-reading direction as search keys. We use this method to develop a prototype system called Kappan. It enables service providers and users to create hyperlinks in books without markers. Existing techniques generally require markers to be printed on the page if a hyperlink is to be created. We consider that utilizing the concept of the search index makes markers unnecessary. Kappan associates indexed text areas in a large number of books with supporting digital contents. The indexed text areas, freely defined by service providers or users, are identified by subjecting images of small areas of the printed page to OCR (Optical Character Recognition) and extracting from the text so recognized highly specific and efficient search keys. Traditional text indexing methods must extract long character sequences from the partial image in order to identify the area exactly given the sheer number of book pages. However, considering that the average OCR error rate is more than 20 percent if the partial image is captured by a camera-equipped cellular phone, it is highly probable that many characters would be misrecognized and area identification would thus fail. In contrast, our indexing method can extract area-specific clues using fewer characters that can identify the area exactly even when the partial image is small and the extracted text contains misrecognized characters. An experiment proves that our method can identify the exact area from more than one million areas with the high accuracy rates of 99 percent and 96 percent for OCR error rates of 0 percent and 22 percent, respectively.

The ability to create geotagged photos enables people to share their personal experiences as tourists at specific locations and times. Assuming that the collection of each photographer's geotagged photos is a sequence of visited locations, photo-sharing sites are important sources for gathering the location histories of tourists. By following their location sequences, we can find representative and diverse travel routes that link key landmarks. In this paper, we propose a travel route recommendation method that makes use of the photographers' histories as held by Flickr. Recommendations are performed by our photographer behavior model, which estimates the probability of a photographer visiting a landmark. We incorporate user preference and present location information into the probabilistic behavior model by combining topic models and Markov models. We demonstrate the effectiveness of the proposed method using a real-life dataset holding information from 71,718 photographers taken in the United States in terms of the prediction accuracy of travel behavior.

Most recommender algorithms produce types similar to those the active user has accessed before. This is because they measure user similarity only from the co-rating behaviors against items and compute recommendations by analyzing the items possessed by the users most similar to the active user. In this paper, we define item novelty as the smallest distance from the class the user accessed before to the class that includes target items over the taxonomy. Then, we try to accurately recommend highly novel items to the user. First, our method measures user similarity by employing items rated by users and a taxonomy of items. It can accurately identify many items that may suit the user. Second, it creates a graph whose nodes are users; weighted edges are set between users according to their similarity. It analyzes the user graph and extracts users that are related on the graph though the similarity between the active user and each of those users is not high. The users so extracted are likely to have highly novel items for the active user. An evaluation conducted on several datasets finds that our method accurately identifies items with higher novelty than previous methods.

We propose a hierarchical auto-tagging system, TagHats, to improve users' knowledge sharing. Our system assigns three different levels of tags to Q&A documents: category, theme, and keyword. Multiple category tags can organize a document according to multiple viewpoints, and multiple theme and keyword tags can identify what the document is about clearly. Moreover, these hierarchical tags will be helpful in organizing documents to support everyone because different users have different demands in terms of tag specificity. Our system consists of a hierarchical classification method for assigning category and theme tags, a new keyword extraction method that considers the structure of Q&A documents, and a new method for selecting theme tag candidates from each category. Experiments with the documents of Oshiete! goo demonstrate that our system is able to assign hierarchical tags to the documents appropriately and is capable of outperforming baseline methods significantly.

This paper proposes a new method for displaying large-scale tag clouds. We use a topographical image that helps users to grasp the relationship among tags intuitively as a background to the tag clouds. We apply this interface to a blog navigation system and show that the proposed method enables users to find the desired tags easily even if the tag clouds are very large, 5,000 and above tags. Our approach is also effective for understanding the overall structure of a large amount of tagged documents.

There are at least three barriers to utilizing blog tags in classification or navigation: 40% of entries are not (from our observations) tagged, there are many orthographic or synonymous tag variations, and not all tags are informative. We propose a method of multi-autotagging, based on k-NN, which is a case-based classification method. Our method also has the functions of merging tags with the same meaning and identifying informative tags. For realizing these functions, we propose the term weighting method named residual document frequency(RDF); it can score the similarity between tags. Experiments show the effectiveness of our methods. Our autotagging system is generic and can assign tag(s) to any text as well as blog entries although the training data is collected from the blogosophere.

Many existing rating systems for goods or services simply total the number of good and bad evaluations reported by the consumers when generating the ratings. These systems, however, make it difficult to create really reliable reports since there is no incentive for the evaluators to provide accurate or truthful reports. This paper proposes to introduce an index that represents the trust we can place on the evaluator. This index increases if the evaluator's past estimation of the reputation of goods or service is subsequently found to be true. An evaluator who earns a higher index score has more impact on the rating system. This mechanism generates fairer and more objective ratings.

Article Free Access Share on Distributed digital-ticket management for rights trading system Authors: Kazuo Matsuyama NTT Information Sharing Platform Laboratories, 1-1 Hikarinooka Yokosuka-Shi, Kanagawa 239-0847 Japan NTT Information Sharing Platform Laboratories, 1-1 Hikarinooka Yokosuka-Shi, Kanagawa 239-0847 JapanView Profile , Ko Fujimura NTT Information Sharing Platform Laboratories, 1-1 Hikarinooka Yokosuka-Shi, Kanagawa 239-0847 Japan NTT Information Sharing Platform Laboratories, 1-1 Hikarinooka Yokosuka-Shi, Kanagawa 239-0847 JapanView Profile Authors Info & Claims EC '99: Proceedings of the 1st ACM conference on Electronic commerceNovember 1999 Pages 110–118https://doi.org/10.1145/336992.337020Published:01 November 1999Publication History 21citation1,008DownloadsMetricsTotal Citations21Total Downloads1,008Last 12 Months52Last 6 weeks9 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF