22,919 research outputs found

    When and where do you want to hide? Recommendation of location privacy preferences with local differential privacy

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    In recent years, it has become easy to obtain location information quite precisely. However, the acquisition of such information has risks such as individual identification and leakage of sensitive information, so it is necessary to protect the privacy of location information. For this purpose, people should know their location privacy preferences, that is, whether or not he/she can release location information at each place and time. However, it is not easy for each user to make such decisions and it is troublesome to set the privacy preference at each time. Therefore, we propose a method to recommend location privacy preferences for decision making. Comparing to existing method, our method can improve the accuracy of recommendation by using matrix factorization and preserve privacy strictly by local differential privacy, whereas the existing method does not achieve formal privacy guarantee. In addition, we found the best granularity of a location privacy preference, that is, how to express the information in location privacy protection. To evaluate and verify the utility of our method, we have integrated two existing datasets to create a rich information in term of user number. From the results of the evaluation using this dataset, we confirmed that our method can predict location privacy preferences accurately and that it provides a suitable method to define the location privacy preference

    Probabilistic Personalized Recommendation Models For Heterogeneous Social Data

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    Content recommendation has risen to a new dimension with the advent of platforms like Twitter, Facebook, FriendFeed, Dailybooth, and Instagram. Although this uproar of data has provided us with a goldmine of real-world information, the problem of information overload has become a major barrier in developing predictive models. Therefore, the objective of this The- sis is to propose various recommendation, prediction and information retrieval models that are capable of leveraging such vast heterogeneous content. More specifically, this Thesis focuses on proposing models based on probabilistic generative frameworks for the following tasks: (a) recommending backers and projects in Kickstarter crowdfunding domain and (b) point of interest recommendation in Foursquare. Through comprehensive set of experiments over a variety of datasets, we show that our models are capable of providing practically useful results for recommendation and information retrieval tasks

    Top-k Route Search through Submodularity Modeling of Recurrent POI Features

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    We consider a practical top-k route search problem: given a collection of points of interest (POIs) with rated features and traveling costs between POIs, a user wants to find k routes from a source to a destination and limited in a cost budget, that maximally match her needs on feature preferences. One challenge is dealing with the personalized diversity requirement where users have various trade-off between quantity (the number of POIs with a specified feature) and variety (the coverage of specified features). Another challenge is the large scale of the POI map and the great many alternative routes to search. We model the personalized diversity requirement by the whole class of submodular functions, and present an optimal solution to the top-k route search problem through indices for retrieving relevant POIs in both feature and route spaces and various strategies for pruning the search space using user preferences and constraints. We also present promising heuristic solutions and evaluate all the solutions on real life data.Comment: 11 pages, 7 figures, 2 table

    Inter-temporal variations in the value of time

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    INTRODUCTION The objective of the research reported here is to examine how the value of time varies over time. A key factor in inter-temporal variations in the value of time is expected to be the impact of income growth, although changes in other socio-economic, demographic, attitudinal, employment and travel characteristics could also lead to variations in the value of time over time. The most widely held convention relating to the adjustment of recommended values of time over time is that they should be linked proportionately to growth in some measure of income. No consideration is given to possible changes in the value of time for other reasons. Even disregarding the latter issue, there is no reason from a theoretical standpoint why the income elasticity for private travel should be unity since it is a matter of personal preference how an individual or household allocates additional income to time savings. In contrast, the case for a close link between the value of time and income is much stronger for business travel. Official recommendations in Britain, as elsewhere, increase the value of non-work travel time over time in line with growth in income. DETR’s Transport Economics Note specifies that both work and non-work time values should be increased in line with real GDP per head. Beesley (1971) pointed out various sources of variation in the value of time over time and, on the basis of the uncertainty as to even the direction in which the values might vary, he argued for in favour of a zero trend value. The first British national value of time study (MVA et al., 1987) claimed that a constant real value of time was on theoretical grounds “equally logical and defensible” as the convention of linking the value of time to income growth. However, it was recognised that there did seem to have been an increase in the value of time over time. It was concluded that, “We do not feel able, therefore, in the absence of any specific work on this topic within our programme, and given the existence of plausible arguments in contrary directions, to come to any firm conclusions. The matter must remain on the agenda for further investigation”. A large amount of recent evidence, which we shall cover, is being taken to indicate that the income elasticity for the value of time spent in private travel is far less than unity. It is important that such a challenge to the widely used convention is tested against the widest body of evidence possible before any conclusions are drawn, particularly given the implications of amended recommendations for practical project evaluations. The aim of this paper is to review the existing evidence relevant to inter-temporal variations in the value of time and to present some fresh empirical evidence. The approach adopted here is threefold. Firstly, we examine the cross-sectional variations in the value of time with income apparent from a number of empirical studies, both British and from other countries, and we develop a model to explain cross-sectional income elasticities across British studies. Secondly, the opportunity exists of analysing two data sets obtained from the same SP design conducted in the same area but at different points in time. Finally, variations in values of time over time are analysed by means of ‘meta-analysis’ of a large data set of British empirical evidence. The structure of this report is as follows. Section 2 contains a discussion of various background issues relating to theoretical maters, methodology and previous findings. Section 3 reports analysis of cross-sectional variations in values of time with income whilst section 4 reports on joint analysis of two data sets collected in the first and second national value of time studies commissioned by the Department of Transport. Section 5 reports the findings of our meta-analysis of a large body of British evidence on the value of time. A discussion of the various findings is provided in section 6 and concluding remarks are provided in section 7. The final stage of the study will draw together this evidence to form recommendations concerning the value of time over time

    SMAP: A Novel Heterogeneous Information Framework for Scenario-based Optimal Model Assignment

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    The increasing maturity of big data applications has led to a proliferation of models targeting the same objectives within the same scenarios and datasets. However, selecting the most suitable model that considers model's features while taking specific requirements and constraints into account still poses a significant challenge. Existing methods have focused on worker-task assignments based on crowdsourcing, they neglect the scenario-dataset-model assignment problem. To address this challenge, a new problem named the Scenario-based Optimal Model Assignment (SOMA) problem is introduced and a novel framework entitled Scenario and Model Associative percepts (SMAP) is developed. SMAP is a heterogeneous information framework that can integrate various types of information to intelligently select a suitable dataset and allocate the optimal model for a specific scenario. To comprehensively evaluate models, a new score function that utilizes multi-head attention mechanisms is proposed. Moreover, a novel memory mechanism named the mnemonic center is developed to store the matched heterogeneous information and prevent duplicate matching. Six popular traffic scenarios are selected as study cases and extensive experiments are conducted on a dataset to verify the effectiveness and efficiency of SMAP and the score function

    Toward personalised and dynamic cultural routing: a three-level approach

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    This paper introduces the concept of “smart routing” as a recommender system for tourists that takes into account the dynamics of their personal user profiles. The concept relies on three levels of support: 1) programming the tour, i.e. selecting a set of relevant points of interests (POIs) to be included into the tour, 2) scheduling the tour, i.e. arranging the selected POIs into a sequence based on the cultural, recreational and situational value of each, and 3) determining the tour’s travel route, i.e. generating a set of trips between the POIs that the tourist needs to perform in order to complete the tour. The “smart routing” approach intends to enhance the experience of tourists in a number of ways. The first advantage is the system’s ability to reflect on the tourists’ dynamic preferences, for which an understanding of the influence of a tourist’s affective state and dynamic needs on the preferred activities is required. Next, it arranges the POIs together in a way that creates a storyline that the tourist will be interested to follow, which adds to the tour’s cultural value. Finally, the POIs are connected by a chain of multimodal trips that the tourist will have to make, also in accordance with the tourist’s preferences and dynamic needs. As a result, each tour can be personalised in a “smart” way, from the perspective of both the cultural and the overall experience of taking it. We present the building blocks of the “smart routing” concept in detail and describe the data categories involved. We also report on the current status of our activities with respect to the inclusion of a tourist’s affective state and dynamic needs into the preference measurement phase, as well as discuss relevant practical concerns in this regard
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