From 5dccba5e3dd453389675bded26e9797dc425ab2b Mon Sep 17 00:00:00 2001 From: Konstantinos Kagkelidis Date: Mon, 28 Nov 2022 16:38:03 +0200 Subject: [PATCH 1/9] REC-150 create a new view to display graphs --- metrics.py | 31 ++- preprocessor.py | 9 +- webservice/app.py | 21 ++ webservice/templates/graphs.html | 340 ++++++++++++++++++++++++++ webservice/templates/kpis.html | 6 + webservice/templates/metric_desc.html | 6 + webservice/templates/rsmetrics.html | 6 + 7 files changed, 407 insertions(+), 12 deletions(-) create mode 100644 webservice/templates/graphs.html diff --git a/metrics.py b/metrics.py index 8dd1f1f..9291bb8 100644 --- a/metrics.py +++ b/metrics.py @@ -609,25 +609,29 @@ def top5_services_ordered(object, k=5, base='https://marketplace.eosc-portal.eu' return topk_services -@statistic('A dictionary of the number of recommendations per day') -def recommendations_per_day(object): +@statistic('A dictionary of the number of recommended items per day') +def recommended_items_per_day(object): """ - It returns a statistical report in dictionary format. Specifically, the key - is set for each particular day found and its value contains the respective - number of recommendations committed. The dictionary includes all in-between - days (obviously, with the count set to zero). Recommendations are already - filtered by those where the user or service does not exist in users' or services' catalogs. + It returns a a timeseries of recommended item counts per day. Each timeseries item has two fields: date and value """ # count recommendations for each day found in entries res=object.recommendations.groupby(by=object.recommendations['Timestamp'].dt.date).count().iloc[:,0] # fill the in between days with zero recommendations res=res.asfreq('D', fill_value=0) - + # convert datetimeindex to string res.index=res.index.format() - return res.to_dict() + # convert series to dataframe with extra column having the dates + res = res.to_frame().reset_index() + + # rename columns to date, value + res.rename(columns={ res.columns[0]: "date", res.columns[1]: "value" }, inplace = True) + + # return a list of objects with date and value fields + return res.to_dict(orient='records') + @statistic('A dictionary of the number of user actions per day') def user_actions_per_day(object): @@ -650,4 +654,11 @@ def user_actions_per_day(object): # convert datetimeindex to string res.index=res.index.format() - return res.to_dict() + # convert series to dataframe with extra column having the dates + res = res.to_frame().reset_index() + + # rename columns to date, value + res.rename(columns={ res.columns[0]: "date", res.columns[1]: "value" }, inplace = True) + + # return a list of objects with date and value fields + return res.to_dict(orient='records') diff --git a/preprocessor.py b/preprocessor.py index e1bcea3..3882b1a 100755 --- a/preprocessor.py +++ b/preprocessor.py @@ -212,6 +212,9 @@ def __init__(self, source_page_id, target_page_id, order): _query=query.copy() _query['date'] = _query.pop('timestamp') for rec in recdb["recommendation"].find(_query).sort("user_id"): + # if dataset contains null references to user_ids replace them with the value -1 + if not rec["user_id"]: + rec["user_id"] = -1 recs.append({'user_id':int(rec['user_id']), 'resource_ids': list(map(lambda x: x['service_id'],rec['recommendation'])), 'resource_scores': list(map(lambda x: x['score'],rec['recommendation'])), @@ -224,8 +227,10 @@ def __init__(self, source_page_id, target_page_id, order): # store data to Mongo DB rsmetrics_db["user_actions"].delete_many({"provider":provider['name'], "ingestion":'batch'}) -rsmetrics_db["user_actions"].insert_many(luas) +if len(luas) > 0: + rsmetrics_db["user_actions"].insert_many(luas) rsmetrics_db["recommendations"].delete_many({"provider":provider['name'], "ingestion":'batch'}) -rsmetrics_db["recommendations"].insert_many(recs) +if len(recs) > 0: + rsmetrics_db["recommendations"].insert_many(recs) diff --git a/webservice/app.py b/webservice/app.py index 4b65a6f..296762f 100644 --- a/webservice/app.py +++ b/webservice/app.py @@ -142,6 +142,27 @@ def html_kpis(provider_name): return render_template('./kpis.html', data=result) +@app.route("/ui/reports//graphs", strict_slashes=False) +def html_graphs(provider_name): + '''Serve html page about graphs per provider''' + reports = db_get_provider_names() + if not provider_name in reports: + abort(404) + + result = {} + + stats_needed = ['start', 'end'] + for stat_name in stats_needed: + result[stat_name] = get_statistic(provider_name, stat_name).get_json() + + result['timestamp'] = get_api_index(provider_name).get_json()['timestamp'] + result['sidebar_info'] = app.sidebar_info + result['report'] = provider_name + result['reports'] = reports + result['metric_active'] = None + + return render_template('./graphs.html', data=result) + @app.route("/ui/descriptions/metrics/", strict_slashes=False) def html_metric_description(metric_name): diff --git a/webservice/templates/graphs.html b/webservice/templates/graphs.html new file mode 100644 index 0000000..b928a71 --- /dev/null +++ b/webservice/templates/graphs.html @@ -0,0 +1,340 @@ + + + + + + + + + Graphs + + + + + + + + + + + +
+
+ +
+
+ +
+
+
+ + + +
+
+
+
Report:
+
+
+
+
+
+ + + + + +
+
+
+
+
+
+ + +
+
Graphs +
Graphic visualisations of various metrics +
+
+
+
+ {% set start_data = data.start.value.split('.')[0].split(' ') %} + {% set end_data = data.end.value.split('.')[0].split(' ') %} + Start Date: +
{{start_data[0]}}
+ End Date: +
{{end_data[0]}}
+
+
+
+
+
+
+
+
User actions per day
+
+
+ +
+
+ +
+
+
+
Recommended items per day
+
+
+ +
+
+
+ + + +
+ +
+
+
+ + + + diff --git a/webservice/templates/kpis.html b/webservice/templates/kpis.html index fbd2633..d0a9e36 100644 --- a/webservice/templates/kpis.html +++ b/webservice/templates/kpis.html @@ -160,6 +160,12 @@ KPIs +
  • + + + Graphs + +
  • Metrics Documentation
  • {%for key, item in data.sidebar_info.metric_descriptions.items() | sort %}
  • diff --git a/webservice/templates/metric_desc.html b/webservice/templates/metric_desc.html index 20261f3..b827e67 100644 --- a/webservice/templates/metric_desc.html +++ b/webservice/templates/metric_desc.html @@ -173,6 +173,12 @@ KPIs
  • +
  • + + + Graphs + +
  • Metrics Documentation
  • {%for key, item in data.sidebar_info.metric_descriptions.items() | sort %}
  • diff --git a/webservice/templates/rsmetrics.html b/webservice/templates/rsmetrics.html index 5b9c6d2..03de083 100644 --- a/webservice/templates/rsmetrics.html +++ b/webservice/templates/rsmetrics.html @@ -163,6 +163,12 @@ KPIs
  • +
  • + + + Graphs + +
  • Metrics Documentation
  • {%for key, item in data.sidebar_info.metric_descriptions.items() | sort %}
  • From e33a5ae1d9a3c6d0f95332d5c5e8022ca2ae44c3 Mon Sep 17 00:00:00 2001 From: Nikolaos Triantafyllis Date: Mon, 5 Dec 2022 15:07:17 +0200 Subject: [PATCH 2/9] REC-144 First version of docusaurus documentation --- website/docs/Introduction.md | 8 --- website/docs/dashboard_views/_category_.json | 8 +++ website/docs/dashboard_views/graphs.md | 18 +++++++ website/docs/dashboard_views/kpis.md | 12 +++++ .../metrics_descriptions/_category_.json | 8 +++ .../metrics_descriptions/catalog_coverage.md | 39 ++++++++++++++ .../click_through_rate.md | 43 ++++++++++++++++ .../diversity_gini_index.md | 48 ++++++++++++++++++ .../diversity_shannon_entropy.md | 43 ++++++++++++++++ .../metrics_descriptions/hit_rate.md | 39 ++++++++++++++ .../metrics_descriptions/novelty.md | 42 +++++++++++++++ .../metrics_descriptions/user_coverage.md | 39 ++++++++++++++ website/docs/dashboard_views/rsmetrics.md | 13 +++++ website/docs/introduction.md | 30 +++++++++++ website/docs/static/img/graphs_recs.png | Bin 0 -> 265344 bytes website/docs/static/img/graphs_uas.png | Bin 0 -> 282016 bytes website/docs/static/img/preprocessor.png | Bin 0 -> 108433 bytes website/docs/static/img/rsmetrics.png | Bin 0 -> 76136 bytes website/docusaurus.config.js | 13 +++++ website/package.json | 6 ++- 20 files changed, 400 insertions(+), 9 deletions(-) delete mode 100644 website/docs/Introduction.md create mode 100644 website/docs/dashboard_views/_category_.json create mode 100644 website/docs/dashboard_views/graphs.md create mode 100644 website/docs/dashboard_views/kpis.md create mode 100644 website/docs/dashboard_views/metrics_descriptions/_category_.json create mode 100644 website/docs/dashboard_views/metrics_descriptions/catalog_coverage.md create mode 100644 website/docs/dashboard_views/metrics_descriptions/click_through_rate.md create mode 100644 website/docs/dashboard_views/metrics_descriptions/diversity_gini_index.md create mode 100644 website/docs/dashboard_views/metrics_descriptions/diversity_shannon_entropy.md create mode 100644 website/docs/dashboard_views/metrics_descriptions/hit_rate.md create mode 100644 website/docs/dashboard_views/metrics_descriptions/novelty.md create mode 100644 website/docs/dashboard_views/metrics_descriptions/user_coverage.md create mode 100644 website/docs/dashboard_views/rsmetrics.md create mode 100644 website/docs/introduction.md create mode 100644 website/docs/static/img/graphs_recs.png create mode 100644 website/docs/static/img/graphs_uas.png create mode 100644 website/docs/static/img/preprocessor.png create mode 100644 website/docs/static/img/rsmetrics.png diff --git a/website/docs/Introduction.md b/website/docs/Introduction.md deleted file mode 100644 index 4c2bab8..0000000 --- a/website/docs/Introduction.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -id: introduction -title: Introduction -sidebar_position: 1 -slug: / ---- - -The Recommender System (RS) is a novel component of the EOSC Portal meant to improve user experience. It provides EOSC users with recommendations concerning resources that could be of their interest, based on a multi-focal perspective of the users. Measuring the success of such system is crucial to get valuable insights in many aspects that affect user experience. In this approach, an independent metrics framework as a service is being introduced to support the evaluation and adaptation of recommendation mechanisms. The use of additional diagnostic metrics and visualizations offers deeper and sometimes surprising insights into a model’s performance. The evaluation is quantitatively being performed by processing information such as resources, user actions, ratings, and recommendations in order to measure the impact of the AI-enhanced services and user satisfaction as well as to incorporate this feedback and improve the services provided, via a user-friendly API and dashboard UI. diff --git a/website/docs/dashboard_views/_category_.json b/website/docs/dashboard_views/_category_.json new file mode 100644 index 0000000..fb8569a --- /dev/null +++ b/website/docs/dashboard_views/_category_.json @@ -0,0 +1,8 @@ +{ + "label": "Dashboard Views", + "position": 2, + "link": { + "type": "generated-index" + } +} + diff --git a/website/docs/dashboard_views/graphs.md b/website/docs/dashboard_views/graphs.md new file mode 100644 index 0000000..5d6f2ad --- /dev/null +++ b/website/docs/dashboard_views/graphs.md @@ -0,0 +1,18 @@ +--- +id: graphs +title: Graphs +sidebar_position: 3 +--- + +The RS Metrics software designs and exposes information graphs concerning attributes defined in the RS. The current implementation exposes: +* User Actions per day +* Recommended Items per day + +## User Actions per day +Concerning this graph the x-axis shows the referenced date-time range, whereas the y-axis shows the amount of user actions occurred. The granularity of the information is per day. +![User Actions per day Graph](../static/img/graphs_uas.png) + + +## Recommended Items per day +Concerning this graph the x-axis shows the referenced date-time range, whereas the y-axis shows the amount of recommended items delivered to the users. The granularity of the information is per day. +![Recommended Items per day Graph](../static/img/graphs_recs.png) diff --git a/website/docs/dashboard_views/kpis.md b/website/docs/dashboard_views/kpis.md new file mode 100644 index 0000000..ae74212 --- /dev/null +++ b/website/docs/dashboard_views/kpis.md @@ -0,0 +1,12 @@ +--- +id: kpis +title: KPIs +sidebar_position: 2 +--- + +The RS Metrics software implements a suite of KPIs exposed through a rich UI/dashboard and a rest API. KPIs indicate measurable values that demonstrate how effectively a company is achieving key business objectives. It currently delivers the below KPIs metrics: +* Click-Through Rate +* Hit Rate +* Top 5 ordered Services +* Top 5 recommended Services + diff --git a/website/docs/dashboard_views/metrics_descriptions/_category_.json b/website/docs/dashboard_views/metrics_descriptions/_category_.json new file mode 100644 index 0000000..91e6f1b --- /dev/null +++ b/website/docs/dashboard_views/metrics_descriptions/_category_.json @@ -0,0 +1,8 @@ +{ + "label": "Metrics Descriptions", + "position": 3, + "link": { + "type": "generated-index" + } +} + diff --git a/website/docs/dashboard_views/metrics_descriptions/catalog_coverage.md b/website/docs/dashboard_views/metrics_descriptions/catalog_coverage.md new file mode 100644 index 0000000..62fa215 --- /dev/null +++ b/website/docs/dashboard_views/metrics_descriptions/catalog_coverage.md @@ -0,0 +1,39 @@ +--- +id: catalog_coverage +title: Catalog Coverage +sidebar_position: 1 +--- + +# Catalog Coverage + +## Summary +The percentage (%) of the division of the unique services found in recommendations to the total number of published services. + +## Description +The Catalog Coverage is described by the formula: $$\frac{unique\_rec\_services}{services}$$ + +## Output + +| Type | Float | +| --- | ----------- | +| Min | 0 | +| Max | 100 | + +:::info +Catalog Coverage is 0 when none of the services is being recommended, and 100 when all of them are being recommended. +::: + +## Prerequisites: +* all available recommendations +* all available services + +## Process Flow: +* ### Retrieve recommendations +Retrieve all available recommendations found in source +* ### Gather all unique services +Gather all unique services found in all available recommendations +* ### Retrieve services +Retrieve all available published services found in source +* ### Calculate ratio +Calculate the percentage (%) of the division of the unique services found in recommendations to the total number of published services + diff --git a/website/docs/dashboard_views/metrics_descriptions/click_through_rate.md b/website/docs/dashboard_views/metrics_descriptions/click_through_rate.md new file mode 100644 index 0000000..0c1936a --- /dev/null +++ b/website/docs/dashboard_views/metrics_descriptions/click_through_rate.md @@ -0,0 +1,43 @@ +--- +id: click_through_rate +title: Click-Through Rate +sidebar_position: 2 +--- + +# Click-Through Rate + +## Summary +The number of user clicks through recommendations panels divided by the total times recommendation panels were presented to users. + +## Description +The number of user clicks through recommendations panels divided by the total times recommendation panels were presented to users. Takes into account all historical data of user actions.

    The metric is expressed by the formula: $$Click-Through Rate=\frac{clicks}{views}$$

    + +## Output + +| Type | Float | +| --- | ----------- | +| Min | 0 | +| Max | +$$\infty$$ | + +:::info +A value of 0 indicates that no clicks through recommendations panels occurred. +::: + +## Prerequisites: +* all available user actions + +## Process Flow: +* ### Retrieve user actions with recommendation panel +Get only the user actions that present a recommendation panel to the user in the source page. Those are actions with the following source paths: + * /services + * /services + * /services/c/{any category name} +* ### Count user actions with recommendation panel +Count the items in the above list as they represent the times recommendations panels were presented to the users of the portal +* ### Filter list +Narrow the above list into a new subset by selecting only user actions that originate from a recommendation panel. Those are actions that have the 'recommendation' string in the Action column +* ### Count user actions with clicks through recommendation panel +Count the items in the subset as they represent the times users clicked through recommendations +* ### Calculate ratio +Divide the items of the subset with the items of the first list to get the click-through rate + diff --git a/website/docs/dashboard_views/metrics_descriptions/diversity_gini_index.md b/website/docs/dashboard_views/metrics_descriptions/diversity_gini_index.md new file mode 100644 index 0000000..6633548 --- /dev/null +++ b/website/docs/dashboard_views/metrics_descriptions/diversity_gini_index.md @@ -0,0 +1,48 @@ +--- +id: diversity_gini_index +title: Diversity Gini Index +sidebar_position: 4 +--- + +# Diversity Gini Index + +## Summary +Measures Recommendations' diversity. The index is 0 when all items are chosen equally often, and 1 when a single item is always chosen. + +## Description +The diversity ($$G$$) of the recommendations according to Gini Index. The index is 0 when all items are chosen equally often, and 1 when a single item is always chosen (see [book](https://link.springer.com/10.1007/978-1-4939-7131-2_110158)). Generally, the Gini Index mathematical expression is defined as:

    + $$G=\frac{1}{n-1}\sum_{j=1}^{n}(2j-n-1)p(i_j)$$, where $$i_1,\ldots,i_n$$ is the list of items ordered according to increasing $$p(i)$$ and each item $$i$$ accounts for a proportion $$p(i)$$ of user recommendations. +In RS Metrics the computation is determined by the following formula: + $$Diversity=\frac{1}{n-1}\sum_{j=1}^{n}(2j-n-1)\left(\frac{count(j)}{recommendations}\right)$$

    + +## Output + +| Type | Float | +| --- | ----------- | +| Min | 0 | +| Max | 1 | + +:::info +The index is 0 when all items are chosen equally often, and 1 when a single item is always chosen. +::: + +## Prerequisites: +* recommendations without anonymous users +* all available services + +## Process Flow: +* ### Clean up +Recommendations clean up; entries removal where users or services are not found in "users" or "services" files accordingly +* ### Services Impact +Calculation of the impact of the services, by counting how many times each service i was suggested to all possible users: count(j) +* ### Sort Services Impact from low to high +Sort the number of how many times each service (i.e. i) was suggested from the lower to the higher value, in order to apply the respective weight (j). The computation includes services with 0 recommendation occurrence +* ### Recommended Probability of the Services +For each service calculate its recommended probability by dividing the number of service's occurrence found in the recommendations to the total number of recommendations +* ### Service-based product computation +Calculation of the product of the recommended probability from previous step and services' respective index j, for each service individually +* ### Gini Index computation +Computation of the overall value by summing all values from previous step + + + diff --git a/website/docs/dashboard_views/metrics_descriptions/diversity_shannon_entropy.md b/website/docs/dashboard_views/metrics_descriptions/diversity_shannon_entropy.md new file mode 100644 index 0000000..2ca9865 --- /dev/null +++ b/website/docs/dashboard_views/metrics_descriptions/diversity_shannon_entropy.md @@ -0,0 +1,43 @@ +--- +id: diversity_shannon_entropy +title: Diversity Shannon Entropy +sidebar_position: 4 +--- + +# Diversity Shannon Entropy + +## Summary +Measures Recommendations' diversity. The entropy is 0 when a single item is always chosen or recommended, and log n when n items are chosen or recommended equally often. + +## Description +The diversity ($$H$$) of the recommendations according to Shannon Entropy. The entropy is 0 when a single item is always chosen or recommended, and log(n) when n items are chosen or recommended equally often (see [book](https://link.springer.com/10.1007/978-1-4939-7131-2_110158)). Generally, the Shannon Entropy mathematical expression is defined as: $$H=-\sum_{i=1}^{n}p(i)\log_2 p(i)$$

    +In RS Metrics the formula is determined as: $$Diversity=-\sum_{i=1}^{services}\left(\frac{count(i)}{recommendations}\right)\log_2 \left(\frac{count(i)}{recommendations}\right)$$

    + +## Output + +| Type | Float | +| --- | ----------- | +| Min | 0 | +| Max | +$$\infty$$ | + +:::info +The entropy is 0 when a single item is always chosen or recommended, and log n when n items are chosen or recommended equally often. +::: + +## Prerequisites: +* recommendations without anonymous users +* all available services + +## Process Flow: +process: +* ### Clean up +Recommendations clean up; entries removal where users or services are not found in "users" or "services" files accordingly +* ### Services Impact +Calculation of the impact of the services, by counting how many times each service i was suggested to all possible users: count(i) +* ### Recommended Probability of the Services +For each service calculate its recommended probability by dividing the number of service's occurrences found in the recommendations to the total number of recommendations +* ### Service-based product computation +Calculation of the product of the recommended probability from previous step and the logarithmic value of it, for each service individually +* ### Shannon Entropy computation +Computation of the overall value by summing all values from previous step + diff --git a/website/docs/dashboard_views/metrics_descriptions/hit_rate.md b/website/docs/dashboard_views/metrics_descriptions/hit_rate.md new file mode 100644 index 0000000..f780291 --- /dev/null +++ b/website/docs/dashboard_views/metrics_descriptions/hit_rate.md @@ -0,0 +1,39 @@ +--- +id: hit_rate +title: Hit Rate +sidebar_position: 5 +--- + +# Hit Rate + +## Summary +The ratio of user hits divided by the total number of users. + +## Description +The ratio of user hits divided by the total number of users (user hit: a user that has accessed at least one service that is also a personal recommendation). The metric is expressed by the formula: $$Hit Rate=\frac{hits}{users}$$ + +## Output + +| Type | Float | +| --- | ----------- | +| Min | 0 | +| Max | +$$\infty$$ | + +:::info +A value of 0 indicates that no user hits occurred. +::: + +## Prerequisites: +* all available recommendations by registered users +* all available users + +## Process Flow: +* ### Retrieve user-service association +For each user get the recommended services and the services the user accessed "services" files accordingly +* ### Calculate hits +Check if the user has at least one accessed service in recommendations. If yes increase number of hits by one +* ### Calculate ratio +Divide user hits by the total number of users + + + diff --git a/website/docs/dashboard_views/metrics_descriptions/novelty.md b/website/docs/dashboard_views/metrics_descriptions/novelty.md new file mode 100644 index 0000000..93070ba --- /dev/null +++ b/website/docs/dashboard_views/metrics_descriptions/novelty.md @@ -0,0 +1,42 @@ +--- +id: novelty +title: Novelty +sidebar_position: 6 +--- + +# Novelty + +## Summary +The novelty metric expresses the ability of the system to recommend items not generally seen before by the population of users. + +## Description +Calculating novelty of the recommender system based on the user actions using the following formula: $$Novelty=\frac{\sum\nolimits_{i \in R}-log(p(i))}{|R|}$$ For each service item $i$ belonging to the set of recommended services $R$ calculate the portion $p(i)$ of the times the service has been viewed to the total views of the services produced by the user actions data. + +## Output + +| Type | Float | +| --- | ----------- | +| Min | 0 | +| Max | +$$\infty$$ | + +:::info +Novelty expresses the ability of the system to recommend items that are novel (not seen before) by the population of users. A smaller number expresses that more services are being recommended that the users have not seen before. +::: + +## Prerequisites: +* all available recommendations associated with registered users +* a subset of the available user actions associated with registered users that expresses transitions to service pages + +## Process Flow: +* ### Clean up +Recommendations and user actions clean up; entries removal where users or services are not found in "users" or "services" files accordingly +* ### User actions that target services +Identify and keep user actions that express transition to target pages that are views of services. Additionally, user actions where the source and the target page belong to the same service's space are removed from the process. +* ### Calculate views for each service +Group and count user actions that express views for each recommended service id +* ### Calculate view propability p(i) of each service +Calculation of the view propability of each service which is the fraction of the service's views to the total service views +* ### Overall Novelty computation +Computation of the overall value by summing the negative log of all recommended service views from previous step and dividing them by the total number of recommended services + + diff --git a/website/docs/dashboard_views/metrics_descriptions/user_coverage.md b/website/docs/dashboard_views/metrics_descriptions/user_coverage.md new file mode 100644 index 0000000..e2516d0 --- /dev/null +++ b/website/docs/dashboard_views/metrics_descriptions/user_coverage.md @@ -0,0 +1,39 @@ +--- +id: user_coverage +title: User Coverage +sidebar_position: 7 +--- + +# User Coverage + +## Summary +The percentage (%) of the division of the unique users found in recommendations to the total number of users. + +## Description +The User Coverage is described by the formula $$\frac{unique\_rec\_users}{users}$$ + +## Output + +| Type | Float | +| --- | ----------- | +| Min | 0 | +| Max | 100 | + +:::info +User Coverage is 0 when recommendations are being suggested to none users, and 100 when recommendations are being suggested to all of the users. +::: + +## Prerequisites: +* all available recommendations +* all available users + +## Process Flow: +* ### Retrieve recommendations +Retrieve all available recommendations found in source +* ### Gather all unique users +Gather all unique users found in all available recommendations +* ### Retrieve users +Retrieve all available users found in source +* ### Calculate ratio + Calculate the percentage (%) of the division of the unique users found in recommendations to the total number of users + diff --git a/website/docs/dashboard_views/rsmetrics.md b/website/docs/dashboard_views/rsmetrics.md new file mode 100644 index 0000000..a291871 --- /dev/null +++ b/website/docs/dashboard_views/rsmetrics.md @@ -0,0 +1,13 @@ +--- +id: rsmetrics +title: RS Metrics +sidebar_position: 1 +--- + +The RS Metrics software implements a web service presenting reports through a rich UI/dashboard and a rest API. Complete documentation of how metrics perform the necessary calculations, and what is the expected range of the output values is also exhibited. It currently delivers the below metrics: +* Catalog Coverage +* Diversity Gini Index +* Diversity Shannon Entropy +* Novelty +* User Coverage + diff --git a/website/docs/introduction.md b/website/docs/introduction.md new file mode 100644 index 0000000..ec24eac --- /dev/null +++ b/website/docs/introduction.md @@ -0,0 +1,30 @@ +--- +id: introduction +title: Introduction +sidebar_position: 1 +slug: / +--- + +## Metrics Framework +

    +The Recommender System (RS) is a novel component of the EOSC Portal meant to improve user experience. It provides EOSC users with recommendations concerning resources that could be of their interest, based on a multi-focal perspective of the users. Measuring the success of such system is crucial to get valuable insights in many aspects that affect user experience. In this approach, an independent metrics framework as a service is being introduced to support the evaluation and adaptation of recommendation mechanisms. The use of additional diagnostic metrics and visualizations offers deeper and sometimes surprising insights into a model’s performance. The evaluation is quantitatively being performed by processing information such as resources, user actions, ratings, and recommendations in order to measure the impact of the AI-enhanced services and user satisfaction as well as to incorporate this feedback and improve the services provided, via a user-friendly API and dashboard UI.

    + +## Components Functionality + +### Preprocessor +This module is responsible for: +* data retrieval through a connector module that claims and transforms data from various sources +* service-associated knowledge correlations +* dummy or dissociated data removal +* tagging of various associations in the data, i.e. registered or anonymous -related users and services +* generation of statistics information. + +![Preprocessor](static/img/preprocessor.png) + +### RS Metrics +This module is responsible for: +* processing the data +* computing the designated evaluation metrics +* producing the necessary information in a homogenized manner. + +![RSMetrics](static/img/rsmetrics.png) diff --git a/website/docs/static/img/graphs_recs.png b/website/docs/static/img/graphs_recs.png new file mode 100644 index 0000000000000000000000000000000000000000..f267b1da2a6d442524dabb349cd17d2e002a4861 GIT binary patch literal 265344 zcmeFYWmH_twlE689fCD(L4!B$4k5ThaGJ)oaSQGa2@WA7xI=IP1a}C*oyNU`2YY0n 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Date: Tue, 6 Dec 2022 10:12:34 +0200 Subject: [PATCH 3/9] REC-153 Add policy/terms pages and relevant links to ui --- webservice/templates/graphs.html | 59 ++++--- webservice/templates/kpis.html | 29 ++-- webservice/templates/metric_desc.html | 28 ++-- webservice/templates/rsmetrics.html | 35 ++-- website/docs/policies/Terms.md | 19 +++ website/docs/policies/_category_.json | 7 + website/docs/policies/tom.md | 67 ++++++++ website/package-lock.json | 228 +++++++++++++++++++++++++- 8 files changed, 410 insertions(+), 62 deletions(-) create mode 100644 website/docs/policies/Terms.md create mode 100644 website/docs/policies/_category_.json create mode 100644 website/docs/policies/tom.md diff --git a/webservice/templates/graphs.html b/webservice/templates/graphs.html index b928a71..6d2b8df 100644 --- a/webservice/templates/graphs.html +++ b/webservice/templates/graphs.html @@ -232,32 +232,45 @@ - +

    -