Facilitating efficient identification of relevant data
The present technology provides for facilitating efficient identification of relevant metrics. In one embodiment, a set of candidate metrics for which to determine relevance to a user is identified. For each candidate metric, a set of distribution parameters is determined, including a first distribution parameter based on implicit positive feedback associated with the metric and usage data associated with the metric and a second distribution parameter based on the usage data associated with the metric. Such usage data can efficiently facilitate identifying relevance even with an absence of negative feedback. Using the set of distribution parameters, a corresponding distribution is generated. Each distribution can then be sampled to identify a relevance score for each candidate metric indicating an extent of relevance of the corresponding metric. Based on the relevance scores for each candidate metric, a candidate metric is designated as relevant to the user.
1. One or more computer storage media having computer-executable instructions embodied thereon that, when executed by one or more processors, cause the one or more processors to perform a method, the method comprising:
determining a set of distribution parameters for a metric corresponding with a quantitative measure used to perform analytics, wherein a first distribution parameter is determined based on implicit positive feedback obtained in relation to the metric and usage data associated with the metric and a second distribution parameter is determined based on the usage data associated with the metric, wherein the second distribution parameter is determined using direct implicit negative feedback, wherein the metric is selected from a set of metrics measuring various performance data;
generating a distribution for the metric using the set of distribution parameters for the metric;
sampling the distribution to identify a relevance score of the metric indicating an extent of relevance of the metric to a user; and
based on the relevance score indicating the metric is relevant to the user, providing an indication of the metric, or data associated therewith, for presentation to the user.
2. The computing system of claim 1 , wherein the distribution is generated without any explicit feedback from the user.
3. The computing system of claim 1 , wherein the direct implicit negative feedback is obtained based on low usage count or based on usage data and a number of negative feedbacks.
4. The computing system of claim 1 , wherein the implicit positive feedback is obtained in accordance with the user selecting or clicking on data associated with the metric.
5. The computing system of claim 1 , wherein the usage data is obtained in accordance with the user accessing or using data associated with the metric.
6. The computing system of claim 1 , wherein determining the set of distribution parameters for the metric is performed via a feedback updating process to update the first distribution parameter based on the implicit positive feedback and a usage updating process to update the first distribution parameter and the second distribution parameter based on the usage data.
7. The computing system of claim 1 , wherein the relevance score indicates the metric is relevant to the user based on the relevance score being a set of highest relevance scores among a set of relevance scores or based on the relevance score exceeding a threshold relevance value.
8. The computing system of claim 7 , wherein the set of relevance scores includes a plurality of relevance scores that correspond with other metrics.
9. The computing system of claim 1 , wherein the usage data is provided via a remote analytics system that captures usage data from user interactions with a website or an application.
10. The computing system of claim 1 , wherein the metric is selected from a set of metrics measuring various performance metrics associated with an organization, the metric being selected based on an occurrence of an anomaly associated with the metric.
11. A computer-implemented method comprising:
identifying a set of candidate metrics for which to determine relevance to a user;
determining a set of distribution parameters for each candidate metric corresponding with a quantitative measure used to perform analytics, each set of distribution parameters including a first distribution parameter based on implicit positive feedback associated with the metric, wherein each candidate metric is selected from a set of metrics measuring various performance data;
generating a distribution for each candidate metric using the corresponding set of distribution parameters;
sampling each distribution to identify a relevance score for each candidate metric indicating an extent of relevance of the corresponding metric to the user; and
based on the relevance scores for each candidate metric, designating at least one candidate metric of the set of candidate metrics as relevant to the user.
12. The method of claim 11 , wherein the set of candidate metrics are identified based on each of the candidate metrics corresponding with a detected anomaly.
13. The method of claim 11 , wherein determining the set of distribution parameters for each candidate metric includes determining the first distribution parameter based further on usage data associated with the metric and determining a second distribution parameter based on the usage data associated with the metric.
14. The method of claim 11 , wherein the set of distribution parameters is determined without any explicit feedback provide by the user.
15. The method of claim 11 further comprising providing an indication of the at least one candidate metric, or data associated therewith, to a user device for display.
16. A computing system comprising:
a processor; and
computer storage memory having computer-executable instructions stored thereon which, when executed by the processor, configure the computing system to:
means for determining a set of distribution parameters for a metric corresponding with a quantitative measure used to perform analytics, wherein a first distribution parameter is determined based on implicit positive feedback obtained in relation to the metric and usage data associated with the metric and a second distribution parameter is determined based on the usage data associated with the metric, wherein the metric is selected from a set of metrics measuring various performance data;
means for generating a distribution for the metric using the set of distribution parameters for the metric; and
means for identifying a relevance score of the metric using the distribution, the relevance score indicating an extent of relevance of the metric to a user.
17. The media of claim 16 , wherein the second distribution parameter is determined without any explicit feedback associated with the metric from the user.
18. The media of claim 16 , wherein the relevance score is identified based on a random sampling of the distribution.
19. The media of claim 16 further comprising means for providing an indication of the metric as relevant to the user.
20. The media of claim 16 , wherein the implicit positive feedback is obtained in accordance with the user selecting or clicking on data associated with the metric, and the usage data is obtained in accordance with the user accessing or using data associated with the metric.