Where to Go on Your Next Trip? Optimizing Travel Destinations Based on User Preferences

Recommendation based on user preferences is a common task for e-commerce websites. New recommendation algorithms are often evaluated by offline comparison to baseline algorithms such as recommending random or the most popular items. Here, we investigate how these algorithms themselves perform and compare to the operational production system in large scale online experiments in a real-world application. Specifically, we focus on recommending travel destinations at Booking.com, a major online travel site, to users searching for their preferred vacation activities. To build ranking models we use multi-criteria rating data provided by previous users after their stay at a destination. We implement three methods and compare them to the current baseline in Booking.com: random, most popular, and Naive Bayes. Our general conclusion is that, in an online A/B test with live users, our Naive-Bayes based ranker increased user engagement significantly over the current online system.Comment: 6 pages, 2 figures in SIGIR 2015, SIRIP Symposium on IR in Practic

Clinical and immunological studies in patients with an increased serum IgD Level

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The transformative potential of reflective diaries for elite English cricketers

The sport of cricket has a history of its players suffering from mental health issues. The psychological study of cricket and, in particular, the attendant demands of participating at an elite level has not previously received rigorous academic attention. This study explored ten elite male cricketers’ experiences of keeping a daily reflective diary for one month during the competitive season. The aim was to assess how valuable qualitative diaries are in this field. Participants were interviewed regarding their appraisal of the methodology as a self‐help tool that could assist coping with performance pressures and wider life challenges. Three outcomes were revealed: first, that diary keeping was an effective opportunity to reflect upon the past and enhance one’s self (both as an individual and a performer); second, that diary keeping acted as a form of release that allowed participants to progress; and third, that diary keeping allowed participants to discover personal patterns of success that increased the likeliness of optimum performance

Subgrid snow depth coefficient of variation spanning alpine to sub-alpine mountainous terrain

Given the substantial variability of snow in complex mountainous terrain, a considerable challenge of coarse scale modeling applications is accurately representing the subgrid variability of snowpack properties. The snow depth coefficient of variation (CVds) is a useful metric for characterizing subgrid snow distributions but has not been well defined by a parameterization for mountainous environments. This study utilizes lidar-derived snow depth datasets spanning alpine to sub-alpine mountainous terrain in Colorado, USA to evaluate the variability of subgrid snow distributions within a grid size comparable to a 1000 m resolution common for hydrologic and land surface models. The subgrid CVds exhibited a wide range of variability across the 321 km2 study area (0.15 to 2.74) and was significantly greater in alpine areas compared to subalpine areas. Mean snow depth was the dominant driver of CVds variability in both alpine and subalpine areas, as CVds decreased nonlinearly with increasing snow depths. This negative correlation is attributed to the static size of roughness elements (topography and canopy) that strongly influence seasonal snow variability. Subgrid CVds was also strongly related to topography and forest variables; important drivers of CVds included the subgrid variability of terrain exposure to wind in alpine areas and the mean and variability of forest metrics in subalpine areas. Two statistical models were developed (alpine and subalpine) for predicting subgrid CVds that show reasonable performance statistics. The methodology presented here can be used for characterizing the variability of CVds in snow-dominated mountainous regions, and highlights the utility of using lidar-derived snow datasets for improving model representations of snow processes

Modelling Relevance towards Multiple Inclusion Criteria when Ranking Patients

In the medical domain, information retrieval systems can be used for identifying cohorts (i.e. patients) required for clinical studies. However, a challenge faced by such search systems is to retrieve the cohorts whose medical histories cover the inclusion criteria specified in a query, which are often complex and include multiple medical conditions. For example, a query may aim to find patients with both 'lupus nephritis' and 'thrombotic thrombocytopenic purpura'. In a typical best-match retrieval setting, any patient exhibiting all of the inclusion criteria should naturally be ranked higher than a patient that only exhibits a subset, or none, of the criteria. In this work, we extend the two main existing models for ranking patients to take into account the coverage of the inclusion criteria by adapting techniques from recent research into coverage-based diversification. We propose a novel approach for modelling the coverage of the query inclusion criteria within the records of a particular patient, and thereby rank highly those patients whose medical records are likely to cover all of the specified criteria. In particular, our proposed approach estimates the relevance of a patient, based on the mixture of the probability that the patient is retrieved by a patient ranking model for a given query, and the likelihood that the patient's records cover the query criteria. The latter is measured using the relevance towards each of the criteria stated in the query, represented in the form of sub-queries. We thoroughly evaluate our proposed approach using the test collection provided by the TREC 2011 and 2012 Medical Records track. Our results show significant improvements over existing strong baselines