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	Browse All Reviews > Information Systems (H) > Information Storage And Retrieval (H.3) > Information Search And Retrieval (H.3.3) > Information Filtering (H.3.3...)     Options: All Media Types Journals Proceedings Div Books Whole Books Other Date Reviewed Title Author Publisher Published Date Descending Ascending     1-10 of 23 Reviews about "Information Filtering (H.3.3...)": Date Reviewed   A novel stock recommendation system using Guba sentiment analysis Sun Y., Fang M., Wang X. Personal and Ubiquitous Computing 22(3): 575-587, 2018.  Type: Article It’s pretty obvious that if you know what investors are thinking, you can predict what stocks they’ll buy and sell. Stock scams have operated for years on the complement of this thought, that is, attempt to change i... Sep 5 2018   Collaborative group-activity recommendation in location-based social networks Purushotham S., Kuo C., Shahabdeen J., Nachman L.  GeoCrowd 2014 (Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information, Dallas, TX, Nov 4, 2014) 8-15, 2014.  Type: Proceedings Group recommendation in location-based social networks (LBSNs) is an important but difficult task. Instead of aggregating individual data into groups, this paper proposes a collaborative group activity recommender (CGAR) system. The pr... Mar 9 2015   Opinions of people: factoring in privacy and trust Basu A., Vaidya J., Corena J., Kiyomoto S., Marsh S., Guo G., Zhang J., Miyake Y. ACM SIGAPP Applied Computing Review 14(3): 7-21, 2014.  Type: Article As online social networks have become part of daily life, social aware recommendation has progressively emerged, aiming at more sophisticated schemata to support automated recommendations.... Nov 20 2014   Russian web spam evolution: yandex experience Pevtsov S., Pevtsov S., Volkov S., Volkov S.  Proceedings of the 22nd International Conference on World Wide Web (Proceedings of the 22nd International World Wide Web Conference, Rio de Janeiro, Brazil, May 13-17, 2013) Comite Gestor da Internet no Brazil, 1137-1140, 2013.  Type: Proceedings “Web spam is defined as web pages that are created to manipulate search engines and deceive web users” [1]. The evolution of Russian web spam is the topic of this paper. The authors introduce detection approaches in... Sep 3 2014   An empirical comparison of social, collaborative filtering, and hybrid recommenders Bellogín A., Cantador I., Díez F., Castells P., Chavarriaga E. ACM Transactions on Intelligent Systems and Technology 4(1): 1-29, 2013.  Type: Article We are surrounded by recommender systems. We enjoy an abundance of choices ranging from information to anything you can imagine, including a spouse. The Amazon search tool is a well-known example. The authors study social, collaborativ... Jun 4 2014   Context ontologies for recommending from the social web Hurrell E., Smeaton A.  CaRR 2013 (Proceedings of the 3rd Workshop on Context-awareness in Retrieval and Recommendation, Rome, Italy, Feb 5, 2013) 26-32, 2013.  Type: Proceedings This paper uses data from Twitter on “follow” choices to explore the use of context for recommendation systems. Context here incorporates such things as usage patterns and rating behaviors, and factors that users ma... Aug 6 2013   Random walk based entity ranking on graph for multidimensional recommendation Lee S., Song S., Kahng M., Lee D., Lee S.  RecSys 11 (Proceedings of the 5th ACM Conference on Recommender Systems, Chicago, IL, Oct 23-27, 2011) 93-100, 2011.  Type: Proceedings While recommendation systems so far have been focused on improving accuracy, diversity, and serendipity, Lee et al. focus on flexibility and propose a graph-based recommendation system. Here, flexibility means the capability of handlin... Apr 17 2012   Real-time, location-aware collaborative filtering of Web content Sandholm T., Ung H.  CaRR 2011 (Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation, Palo Alto, CA, Feb 13, 2011) 14-18, 2011.  Type: Proceedings Sandholm and Ung report on Gloe, a Web content recommender system with a focus on mitigating popularity bias to intentionally increase recommendation diversity. The primary challenge is to provide real-time filtering of candidate recom... Jan 6 2012   Pattern recognition and classification for multivariate time series Spiegel S., Gaebler J., Lommatzsch A., De Luca E., Albayrak S.  SensorKDD 2011 (Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data, San Diego, CA, Aug 21, 2011) 34-42, 2011.  Type: Proceedings The continued advent of smart sensors in day-to-day electronic devices poses new challenges in machine learning and data mining. In spite of the available time series algorithm that predicts crimes [1], real-life data from smartphones,... Sep 16 2011   Improving recommender systems by incorporating social contextual information Ma H., Zhou T., Lyu M., King I. ACM Transactions on Information Systems 29(2): 1-23, 2011.  Type: Article Though the role of recommender systems has expanded in recent years, the data sparsity and cold-start problems still require solid solutions. This paper highlights how we can use social resources to overcome these problems, thus improv... Jun 13 2011       Display 510152550100 per page
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