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    • 3. 发明申请
    • COMPARISON-BASED ACTIVE SEARCHING/LEARNING
    • 基于比较的主动搜索/学习
    • WO2013169968A1
    • 2013-11-14
    • PCT/US2013/040248
    • 2013-05-09
    • THOMSON LICENSINGIOANNIDIS, EfstratiosMASSOULIE, Laurent
    • IOANNIDIS, EfstratiosMASSOULIE, Laurent
    • G06F17/30
    • G06F17/30451G06F17/3002G06F17/30023G06N99/005
    • A method is provided for performing a content search through comparisons, where a user is presented with two candidate objects and reveals which is closer to the user's intended target object. The disclosed principles provide active strategies for finding the user's target with few comparisons. The so-called rank-net strategy for noiseless user feedback is described. For target distributions with a bounded doubling constant, rank-net finds the target in a number of steps close to the entropy of the target distribution and hence of the optimum. The case of noisy user feedback is also considered. In that context a variant of rank-nets is also described, for which performance bounds within a slowly growing function (doubly logarithmic) of the optimum are found. Nu-merical evaluations on movie datasets show that rank-net matches the search efficiency of generalized binary search while incurring a smaller computational cost.
    • 提供了一种用于通过比较来执行内容搜索的方法,其中向用户呈现两个候选对象,并且揭示哪个更靠近用户的预期目标对象。 所公开的原则提供了用于通过少量比较来查找用户的目标的主动策略。 描述了所谓的无噪声用户反馈的排序策略。 对于具有有限倍增常数的目标分布,等级网在接近目标分布熵的几个步骤中找到目标,因此找到目标。 还会考虑用户反馈异常的情况。 在这种情况下,还描述了排序网格的变体,其中找到了最优的缓慢增长函数(双对数)的性能界限。 电影数据集的动词评估表明,排序网匹配广义二叉搜索的搜索效率,同时导致较小的计算成本。
    • 4. 发明申请
    • RECOMMENDING CONTENT
    • 推荐内容
    • WO2011080138A1
    • 2011-07-07
    • PCT/EP2010/070232
    • 2010-12-20
    • THOMSON LICENSINGMASSOULIE, LaurentIOANNIDIS, Efstratios
    • MASSOULIE, LaurentIOANNIDIS, Efstratios
    • G06F17/30
    • G06F17/30867G06F3/048
    • A method for recommending content items to a user is provided. It includes: (i) receiving one of at least an acceptance input and a rejection input from a user in relation to content presented to the user; (ii) in response to an acceptance input, rendering the presented content, or in response to a rejection input, selecting fresh content for presentation; and, (iii) repeating steps (i) and (ii) until a acceptance input is received. Content is selected in dependence on a associated probability associated with that content. The probability is increased in response to an acceptance input, the increase being determined in part on a measure of a predicted reduction in user satisfaction that would be associated with an additional rejection input.
    • 提供了一种向用户推荐内容的方法。 它包括:(i)相对于呈现给用户的内容,从用户接收至少一个接受输入和拒绝输入; (ii)响应于接受输入,呈现呈现的内容,或响应于拒绝输入,选择新的内容进行呈现; 和(iii)重复步骤(i)和(ii),直到收到接受输入。 根据与该内容相关联的相关概率来选择内容。 响应于接受输入而增加概率,该增加部分地取决于与另外的拒绝输入相关联的预测的用户满意度降低的度量。
    • 5. 发明申请
    • INTERACTIVE CONTENT SEARCH USING COMPARISONS
    • 使用比较搜索的互动内容
    • WO2013119626A1
    • 2013-08-15
    • PCT/US2013/024881
    • 2013-02-06
    • TECHNICOLOR USA, INC.MASSOULIE, LaurentIOANNIDIS, Efstratios
    • MASSOULIE, LaurentIOANNIDIS, Efstratios
    • G06F17/30
    • G06F17/30451G06F17/30017G06F17/30244
    • In interactive content search through comparisons, a search for a target object in a database is performed by finding the object most similar to the target from a small list of objects. A new object list is then presented based on the earlier selections. This process is repeated until the target is included in the list presented, at which point the search terminates. A solution to the interactive content search problem is provided under the scenario of heterogeneous demand, where target objects are selected from a non-uniform probability distribution. It has been assumed that objects are embedded in a doubling metric space which is fully observable to the search algorithm. Based on these assumptions, an efficient comparison-based search method is provided whose cost in terms of the number of queries can be bounded by the doubling constant of the embedding c , and the entropy of demand distribution, H . More precisely, the present principles show that the average search costs scales C F = O(c 5 H) , which improves upon the previously best known bound and is order optimal for constant c .
    • 在通过比较的交互式内容搜索中,通过从小的对象列表中找到与目标最相似的对象来执行数据库中的目标对象的搜索。 然后基于先前的选择来呈现新的对象列表。 重复该过程直到目标被包括在所呈现的列表中,此时搜索终止。 在异构需求的场景下提供了交互式内容搜索问题的解决方案,其中从不均匀的概率分布中选择目标对象。 已经假设对象被嵌入在搜索算法中完全可观察到的加倍度量空间中。 基于这些假设,提供了一种有效的基于比较的搜索方法,其查询数量的成本可以由嵌入c的倍增常数和需求分布熵H限制。更准确地说,本原理 显示平均搜索成本缩放CF = O(c 5 H),其改善了先前最为已知的界限,并且对于常数c是顺序优化的。