Predicting User Locations and Trajectories

Location-based services usually recommend new locations based on the user’s current location or a given destination. However, human mobility involves to a large extent routine behavior and visits to already visited locations. In this paper, we show how daily and weekly routines can be modeled with basic prediction techniques. We compare the methods based on their performance, entropy and correlation measures. Further, we discuss how location prediction for everyday activities can be used for personalization techniques, such as timely or delayed recommendations.

Authors: Eelco Herder, Patrick Siehndel and Ricardo Kawase

PDF: herder-umap2014

Beyond the Usual Suspects: Context-Aware Revisitation Support

Ricardo Kawase @HT2011

A considerable amount of our activities on the Web involves revisits to pages or sites. Reasons for revisiting include active monitoring of content, verification of information, regular use of online services, and reoccurring tasks. Browsers support for revisitation is mainly focused on frequently and recently visited pages. In this paper we present a dynamic browser toolbar that provides recommendations beyond these usual suspects, balancing diversity and relevance. The recommendation method used is a combination of ranking and propagation methods. Experimental outcomes show that this algorithm performs significantly better than the baseline method. Further experiments address the question whether it is more appropriate to recommend specific pages or rather (portal pages of) Web sites. We conducted two user studies with a dynamic toolbar that relies on our recommendation algorithm. In this context, the outcomes confirm that users appreciate and use the contextual recommendations provided by the toolbar.

Venue: HT2011

Authors: Ricardo Kawase, George Papadakis, Eelco Herder and Wolfgang Nejdl

Award:  Engelbart Best Paper Award (HT2011)

PDF: kawase-ht2011

A Layered Approach to Revisitation Prediction

Session chair Daniel Schwabe and presenter Ricardo Kawase @ICWE2011

Web browser users return to Web pages for various reasons. Apart from pages visited due to backtracking, they typically have a number of favorite/important pages that they monitor or tasks that reoccur on an infrequent basis. In this paper, we introduce the architecture of a system that facilitates revisitations through the effective prediction of the next page request. It consists of three layers, each dealing with a specific aspect of revisitation patterns: the first one estimates the value of each page by balancing the recency and the frequency of its requests; the second one captures the contextual regularities in users’ navigational activity in order to promote related pages, and the third one dynamically adapts the page associations of the second layer to the constant drift in the interests of users. For each layer, we introduce several methods, and evaluate them over a large, real-world dataset. The outcomes of our experimental evaluation suggest a significant improvement over other methods typically used in this context.

Venue: ICWE2011

Authors:  George Papadakis, Ricardo Kawase, Eelco Herder and Claudia Niederée

PDF: kawase-icwe2011

Supporting revisitation with contextual suggestions

Web browsers provide only little support for users to revisit pages that they do not visit very often. We developed a browser toolbar that reminds users of visited pages related to the page that they currently viewing. The recommendation method combines ranking with propagation methods. A user evaluation shows that on average 22.7% of the revisits were triggered by the toolbar, a considerable change on the participants’ revisitation routines. In this paper we discuss the value of the recommendations and the implications derived from the evaluation.

Venue: JCDL2011

Authors: Ricardo Kawase, George Papadakis and Eelco Herder

PDF: kawase-jcdl2011b