Croissance/Premier jour personnalisé/Tâches structurées
Cette page décrit le travail de l'équipe Croissance (Growth team) sur le projet « tâches structurées », qui est lié aux projets « tâches des nouveaux arrivants » et « page d'accueil des nouveaux arrivants ». Cette page contient les principaux atouts, les conceptions, les questions ouvertes et les décisions. La plupart des mises à jour progressives sur l'état d'avancement des projets seront affichées sur la page générale des mises à jour, certaines mises à jour importantes ou détaillées étant affichées ici.
- 2020-05-01: planification et documentation des notes initiales
- 2020-05-17: begin community discussion
- 2020-05-29: initial wireframes
- 2020-08-24: week of planning meetings
- 2020-09-08: call for community discussion on latest designs
- 2020-10-21: user testing of desktop designs
L'équipe chargée de la croissance a déployé le projet "newcomer tasks" en novembre 2019, qui suggère aux nouveaux arrivants un flux d'articles à modifier sur leur page d'accueil personnelle. Depuis avril 2020, les articles suggérés proviennent uniquement d'articles dont les modèles de maintenance ont été appliqués par des éditeurs expérimentés, qui ne donnent pas aux nouveaux arrivants d'indications particulières sur les phrases, mots ou sections qui nécessitent une attention particulière. Malgré ce manque d'orientation, nous sommes heureux de constater que les nouveaux venus ont fait des suggestions d'édition productives.
Bien que les modèles de maintenance offrent divers types de modifications à effectuer par les nouveaux arrivants, il se peut qu'ils soient trop vastes et trop ouverts pour que les nouveaux arrivants puissent réaliser leurs modifications avec succès. Et sur les appareils mobiles, les éditeurs visuels ou de wikitexte peuvent submerger les nouveaux arrivants qui tentent de les faire sur un petit écran.
C'est pourquoi nous voulons expérimenter une idée appelée « tâches structurées ». Il s'agit de décomposer le processus d'édition des articles en une série d'étapes que les nouveaux venus peuvent accomplir facilement. En suivant les exemples réussis des équipes Android et Language, nous pensons que ces types de modifications seront plus faciles à effectuer pour les nouveaux arrivants et plus faciles à réaliser sur un téléphone portable, ce qui aidera un plus grand nombre de nouveaux arrivants à effectuer davantage de modifications. Ces tâches structurées seraient accessibles aux nouveaux arrivants dans le cadre du projet de tâches pour les nouveaux arrivants.
Modifier, c'est compliqué
Grâce à l'expérience de l'équipe Croissance, nous en sommes venus à penser que les premiers moments d'un nouveau venu sur le wiki peuvent rapidement déterminer s'il veut rester ou partir. Nous pensons que les nouveaux arrivants veulent rester lorsqu'ils peuvent rapidement faire une modification et avoir une expérience positive. Mais contribuer à Wikipédia — pour presque tout type de contribution — est compliqué, et cela rend difficile pour eux de réussir une modification rapidement. Par exemple, il faut suivre une douzaine d'étapes pour faire quelque chose d'aussi simple que d'ajouter une seule phrase à un article :
- rechercher le bon article ;
- voir si l'information que vous souhaitez ajouter est déjà présente dans l'article ;
- choisir une section dans laquelle ajouter la phrase ;
- cliquer pour commencer à modifier ;
- taper la phrase à la bonne place ;
- cliquer sur le bouton « Sourcer » ;
- retourner sur la source pour en avoir le lien ou pour citer l'information ;
- remplir et valider le formulaire de référence ;
- cliquer pour publier la modification ;
- remplir le résumé de modification ;
Les nouveaux arrivants qui ouvrent l'éditeur visuel ou l'éditeur de wikitexte pour la première fois ne savent pas quelles sont ces étapes, dans quel ordre les faire, ni sur quels boutons cliquer pour les réaliser. En d'autres termes, leur expérience n'est pas « structurée ». Il se peut qu'ils soient simplement dépassés et qu'ils partent. Ou ils peuvent aussi tenter de réaliser quelque chose, commettre une erreur et recevoir des commentaires négatifs de la part de rédacteurs expérimentés. C'est le but de ce projet : comment aider les nouveaux venus à réaliser ces étapes dans le bon ordre ?
Building on knowledge from other teams
Adding structure to editing workflows has been part of the Wikimedia projects for a long time. Some examples include:
- HotCat: lets users choose categories to add to articles with a few clicks, instead of manually editing the wikitext.
- Commons Upload Wizard: breaks the process of uploading media to Commons into a series of a simple steps.
- Citoid: available in the Visual Editor, this breaks down the process of adding a citation into steps that include algorithms to automatically produce the citation text and template.
Most recently, the idea of "structured tasks" has been working well on the Wikipedia Android app and in the Content Translation tool. We're inspired by their work.
With their "suggested edits" project, the Android team broke down the process of adding a title description to a Wikipedia article into one easy step of typing into a text box. They have since done the same with translating title descriptions across languages. In order to do the same tasks without a structured workflow, users would have to go to Wikidata and go through several steps to make those same edits. The team learned that this method works: many Android users make hundreds of these small contributions.
The Language team built the Content Translation tool, which does several things to structure the process of translating an article. It offers a side-by-side interface built for translations, it breaks the translation down into sections, and it automatically applies machine translation algorithms. Though Wikipedians could translate articles before the existence of the tool, the number of manual steps required made it very difficult. This tool is successful, with hundreds of thousands of translations completed. We learned that when translating an article is broken down into steps, with rote parts (e.g. running machine translation) taken care of automatically, more articles get translated.
The Growth team is thinking about applying these same principles to content edits in articles, like adding links, adding images, adding references, and adding sentences.
The Wikipedia Android app offers "suggested edits", such as translating title descriptions, which users can do in one easy step, instead of going through Wikidata.
The Content Translation tool breaks down the process of translating an article into one section at a time, with automated assistance from machine translation algorithms.
Une esquisse de tâche structurée
La meilleure façon d'expliquer comment nous envisageons les tâches structurées peut être de montrer un rapide croquis. La première tâche structurée à laquelle nous avons pensé est « ajouter un wikilien » (lien interne). Mais les mêmes idées pourraient s'appliquer aux tâches structurées pour « ajouter une image », « ajouter une référence », ou même « ajouter un fait ».
Dans la fonctionnalité des tâches pour les nouveaux venus, beaucoup de nouveaux venus effectuent des tâches « ajouter un wikilien » — dans lesquelles ils ajoutent des liens bleus internes dans les articles qui n'en ont pas beaucoup. Cela semble être une simple tâche d'édition pour commencer. Mais nous pensons que beaucoup de nouveaux arrivants ne comprennent pas comment passer par les étapes de l'ajout d'un lien et ne savent pas quels mots transformer en liens. Nous imaginons un flux de travail qui les guide pas à pas, à l'aide d'un algorithme qui peut deviner quels mots ou phrases pourraient constituer les meilleurs liens.
Dans le schéma ci-dessous, le nouveau venu arrive sur un article, et se voit suggérer un mot qui pourrait faire un bon lien interne. S'il est d'accord pour qu'il en fasse un lien, il est guidé à travers les étapes de la création du lien. Cela leur apprendra, espérons-le, à ajouter des liens par eux-mêmes à l'avenir — et peut-être apprécieront-ils de continuer à recevoir ces suggestions de liens algorithmiques. En ce qui concerne l'algorithme, l'équipe de recherche du WMF a effectué des travaux préliminaires qui nous rendent confiants dans la possibilité d'un tel algorithme.
En y réfléchissant, nous avons esquissé une deuxième idée. Au lieu d'apprendre au nouveau venu à ajouter des liens à l'aide de l'éditeur visuel, cet flux de travail permet à l'utilisateur de confirmer ou de rejeter rapidement les recommandations de l'algorithme, en éditant directement l'article. Bien que ce système ne leur apprenne pas comment ajouter des liens via l'éditeur, il pourrait aider un nouveau venu à éditer à un volume élevé, et pourrait mieux convenir à un utilisateur qui essaie d'être productif avec des tâches simples, par exemple quand qu'il est en déplacement. Il pourrait également convenir aux utilisateurs qui sont « uniquement » intéressés par des modifications très simples, comme c'est le cas pour l'application Android, où de nombreux éditeurs sont « uniquement » intéressés par la description des titres.
En ce qui concerne les tâches structurées, il semble que la question suivante se pose : les flux de travail doivent-ils être davantage axés sur la formation des nouveaux arrivants à l'utilisation des outils traditionnels ou sur la capacité des nouveaux arrivants à effectuer des modifications faciles à un volume plus important ?
Why this idea is prioritized
We think that quickly making productive edits is what leads to newcomer success. Once they've done some edits, the rest of the wiki experience quickly becomes richer. Newcomers can then see their impact, get thanked, ask informed questions to their mentors, create their userpage, etc. Therefore, we want lots of newcomers to make their first edits as soon as possible. We have already seen from the newcomer tasks project that many newcomers are looking for easy tasks to do. But we also have observed these things:
- Only about 25% of the newcomers who click on a suggestion actually edit it.
- Only about 25% of those who do a suggested edit do another one.
- There are a handful of newcomers who really thrive on suggested edits, doing dozens of them every day. This shows the potential for newcomers to accomplish a lot of wiki work.
- In live user tests, when newcomers are told to copyedit an article or add links to an article, they frequently want to know exactly which sentence or words need their attention. In other words, attempting to edit the full article is too open-ended.
Taking these points along with the experiences described above of the Android and Content Translation teams, we think we could increase the number of newcomers editing and continuing to edit by structuring some of the content editing workflows in Wikipedia.
Opportunities with structured tasks
When we break down editing workflows into steps, we call them "structured tasks". Here are some of the possible benefits we think could come from structured tasks:
- Make it easy for newcomers to make meaningful contributions.
- Develop editing workflows that make sense for mobile. Mobile design principles tell us that users should see one step at a time, not a complicated workspace.
- Let newcomers increase their skills incrementally. They could take on successfully more challenging types of tasks.
- Let people find an editing experience that fits them. By giving newcomers a feed of structured tasks, they could find the type of tasks that they prefer.
- Perhaps similar workflows could be opened to experienced editors in the future.
Concerns and downsides to structured tasks
Whenever we add new ways for people to edit Wikipedia, there are many things that can go wrong:
- By making editing too quick and easy, we may attract vandals, or users who don't apply enough care when editing.
- Giving newcomers simple workflows may keep them from learning the traditional editing tools, which are essential for doing the most impactful wiki work.
- Structured tasks may not be good at accounting for differences across languages, idiosyncrasies with wikitext, and could cause other kinds of bugs.
- Algorithms that surface structured tasks may not be accurate enough, and falsely encourage newcomers to complete edits they shouldn't.
In May 2020, we conducted discussions with community members in six languages (English, French, Korean, Arabic, Vietnamese, Czech) about the above ideas for structured tasks. The English discussion mostly took place on the discussion page here, with other conversations on English Wikipedia, and local language conversations on the other five Wikipedias. We heard from 35 community members, and this section summarizes some of the most popular and interesting thoughts. These discussions heavily influenced our next set of designs.
- Community members were generally positive about the potential for structured tasks to help newcomers start editing. But it was also a widely expressed view that it's important for newcomers to be introduced to the conventional source and visual editors during the process. Community members want to make sure that newcomers are not siloed in a separate editing experience, and that they can find their way to more valuable edits.
- The Czech community talked about ideas for how the structured tasks can place inside the visual editor, so that newcomers can start getting used to being in the editor. Perhaps the editing tools that are not needed for the structured task can be grayed-out.
- Community members asked why we are choosing "add a link" as our first structured task, as opposed to higher-value types of edits. We talked about how this task is one of the easiest for us to build, which will help us prototype and learn from structured tasks sooner, and how it is a comparatively low-risk task, with fewer opportunities for newcomers to damage articles.
- Several communities mentioned that spelling corrections would be a particularly valuable task, and we talked about technical options for how to generate lists of potential spelling mistakes. See these notes for more details.
- We also talked about whether reverting vandalism is a good fit for newcomers. It doesn't seem like the answer is clear, and this will have to be discussed more in the future.
- An idea that was mentioned multiple times is how to "step newcomers up" to progressively more challenging tasks, perhaps while giving them rewards for successfully completing easier ones.
Types of tasks
There are many different editing workflows that have the potential to become structured. We began to list workflows when we first designed the newcomer tasks workflow here, and we have since narrowed down to a shorter list of task types that seem best suited to being structured. The table below contains that short list, ranked in a potential priority order.
|Potential priority||Task type||How it might work||Advantages||Concerns|
|1||Add a link||For articles without enough wikilinks, an algorithm (existing) suggests words or phrases that should become wikilinks, and the newcomer accepts or rejects the suggestions.||Linking is a quick and easy way to edit, and has low potential to damage articles.||Understanding when to add a link takes judgment, and we don't want articles to be overlinked. It is also not the most valuable type of edit.|
|2||Add an image||For articles without an illustration, an algorithm (potential) suggests an image from Commons. This might be a simple algorithm that just looks at what images are used on that article in other languages. The newcomer decides if the image belongs, and where in the article to add it.||Good images make a big difference in an article, and newcomers are interested in adding images.||Adding the wrong image to an article could damage the article in a very visible way.|
|3||Add a reference||Some sentences or paragraphs clearly need citations. An algorithm (in development) would point out which sentences likely need suggestions, and the newcomer would seek sources to add as citations in a step-by-step workflow.||References are of clear importance to the core of the encyclopedia.||This task may not be exciting to newcomers. They may also struggle to find and use sources without guidance.|
|4||Copyedit||Using open-source spellcheck dictionaries and code, or using Wiktionary, identify likely misspelled words, and point them out to newcomers, who can use the visual editor or wikitext editor to fix them one at a time.||Clearly valuable and needed in any wiki, satisfying to newcomers. Helps them start editing the main text of articles, as opposed to peripherals parts of the article.||Scaling to any language may be difficult, depending on the availability of good spellchecking algorithms.|
|5||Add a section||An algorithm detects when an article could use additional sections, based on the kinds of section headers that similar articles have (e.g. all biographies of scientists tend to have "Publications" sections). The newcomer is walked through producing a well-referenced paragraph.||Real content additions that could help close knowledge gaps.||A much more challenging task than the others, requiring many wiki skills to be used together. May produce low-quality content.|
The Growth team currently (May 2020) wants to prioritize the "add a link" workflow over the other ones listed in the table above. Although other workflows, such as "copyedit", seem to be more valuable, there are a set of reasons we would want to start first with "add a link":
- In the near term, the most important thing we would want to do first is to prove the concept that "structured tasks" can work. Therefore, we would want to build the simplest one, so that we can deploy to users and gain learnings, without having to invest too much in the first version. If the first version goes well, then we would have the confidence to invest in types of tasks that are more difficult to build.
- "Add a link" seems to be the simplest for us to build because there already exists an algorithm built by the WMF Research team that seems to do a good job of suggesting wikilinks (see the Algorithm section).
- Adding a wikilink doesn't usually require the newcomer to type anything of their own, which we think will make it particularly simple for us to design and build -- and for the newcomer to accomplish.
- Adding a wikilink seems to be a low-risk edit. In other words, the content of an article can't be as compromised through adding links incorrectly as it could through adding references or images incorrectly.
Notes on "copyedit"
In conversations with community members on this project's discussion page, many people brought up the question of how to make a structured task around copyediting. Correcting spelling, grammar, punctuation, and tone seemed to everyone to be a clearly useful task that should be prioritized. The Growth team initially shied away from this workflow because of scaling concerns: even if we were able to find or develop an algorithm that could reliably find copyedits in one language, would we be able to do that in dozens of other languages?
We began to learn more about this by talking with User:Beland, who developed the "moss" script for English Wikipedia's Typo Team. We wanted to understand how the process works, and what it might look like to do something similar in other languages. In short, it sounds like the most promising avenue is through existing open-source spellcheckers and dictionaries. Two examples are the aspell and hunspell libraries. Below are our notes from learning about "moss" with User:Beland.
- Prospects for doing something similar in other languages
- A process like this should theoretically work in other languages, given that other languages also have Wiktionaries and open-source spellcheckers.
- But it would not be possible to deploy in a new language without native speakers validating it. There would likely need to be customization for many languages.
- Likely more challenges for languages without word segmentation (e.g. Japanese).
- Likely more challenges for agglutinative languages.
- Different projects have differing manuals of style, which may cause issues.
- If an algorithm is performing poorly, it should always be possible to change its thresholds so that it identifies fewer potential errors, but with higher confidence.
- How does moss work?
- Download the dump files of all of English Wikipedia every two weeks.
- In order to cut down on false positives, remove templates and everything inside quotation marks, etc. Only want to work on the main text in the article: the things written “in Wikipedia’s voice”.
- Check that every word is in English Wiktionary.
- Uses Python NLTK (natural language toolkit) for word segmentation.
- Looks at edit distance to classify misspellings. e.g. “T1” is one edit distance (95% precision). Also classifies “TS” whitespace errors.
- Also includes an English open-source spellchecker to narrow the search space so that the algorithm can run faster.
- He has also started trying to add grammar rules (e.g. identifying passive voice), but that’s more experimental, and much more difficult than spelling.
- At the end of the process, it produces a list of articles and likely typos. The user opens the article and searches for the likely typo.
Many copyedit requests are also editors whose native language is not English, asking for English polishing. See WikiProject Guild of Copy Editors.
While the "structured task sketch" section above contains some quick initial sketches to demonstrate the idea behind structured tasks, this section contains our current design thinking. To look into the full set of thinking around designs for the "add a link" structured task, see this slideshow, which contains background, user stories, and initial design concepts.
When we design a feature, we look into similar features in other software platforms outside of the Wikimedia world. These are some highlights from comparative reviews done in preparation for Android’s suggested edits feature, which remain relevant for our project.
- Task types – are divided into five main types: Creating, Rating, Translating, Verifying content created by others (human or machine), and Fixing content created by others.
- Visual design & layout – incentivizing features (stats, leaderboards, etc) and onboarding is often very visually rich, compared to pared back, simple forms to complete short edits. Gratifying animations often compensate for lack of actual reward.
- Incentives – Most products offered intangible incentives grouped into: Awards and ranking (badges) for achieving set milestones, Personal pride and gratification (stats), or Unlocking features (access rights)
- Users motivations – those with more altruistic motivations (e.g., help others learn) are more likely to be incentivized by intangible incentives than those with self-interested motivations (e.g., career/financial benefits)
- Personalization/Customization – was used in some way on most apps reviewed. The most common customization was via surveys during account creation or before a task; and geolocalization used for system-based personalization.
- Guidance – Almost all products reviewed had at least basic guidance prior to task completion, most commonly introductory ‘tours’. In-context help was also provided in the form of instructional copy, tooltips, step-by-step flows, as well as offering feedback mechanisms (ask questions, submit feedback)
After organizing our thoughts and doing background research, the first visuals in the design process are "wireframes". These are simply meant to experiment and display some of the ideas we think could work well in a structured task workflow. For full context around these wireframes, see the design brief slideshow.
Mobile mockups: August 2020
Our team discussed the wireframes from the previous section. We considered what would be best for the newcomers, taking into account the preferences expressed by community members, and thinking about engineering constraints. In August 2020, we took the next step of creating mockups, meant to show in more detail what the feature might look like. These mockups (or similar versions) will be used in team discussions, community discussions, and user tests. One of the most important things we thought about with these mockups is the concern we heard consistently from community members during the discussion: structured tasks may be a good way to introduce newcomers to editing, but we also want to make sure they can find and use the traditional editing interfaces if they are interested.
We have mockups for two different design concepts. We're not necessarily aiming to choose one design concept or the other. Rather, the two concepts are meant to demonstrate different approaches. Our final designs may contain the best elements from both concepts:
- Concept A: the structured task edit takes place in the Visual Editor. The user can see the whole article, and switch out of "recommendation mode" into source or visual editor mode. Less focused on adding the links, but easier access to the visual and source editors.
- Concept B: the structured task edit takes place in its own new area. The user is shown only the paragraph of the article that needs their attention, and can go edit the article if they choose. Fewer distractions from adding links, but more distant access to the visual and source editors.
Please note that the focus in this set of mockups is on the user flow and experience, not on the words and language. Our team will go through a process to determine the best way to write the words in the feature and to explain to the user whether a link should be added.
To view these design concepts, we recommend viewing the full set of slides below.
You can also try out the "interactive prototypes" that we're using for live user tests. These prototypes, for Concept A and for Concept B, show what it might feel like to use "add a link" on mobile. They work on desktop browsers and Android devices, but not iPhones. Note that not everything is clickable -- only the parts of the design that are important for the workflow.
In discussing these designs, our team is hoping for input on a set of essential questions:
- Should the edit happen at the article (more context)? Or in a dedicated experience for this type of edit (more focus, but bigger jump to go use the editor)?
- What if someone wants to edit the link target or text? Should we prevent it or let them go to a standard editor? Is this the opportunity to teach them about the visual editor?
- We know it’s essential for us to support newcomers discovering traditional editing tools. But when do we do that? Do we do it during the structured task experience with reminders that the user can go to the editor? Or periodically at completion milestones, like after they finish a certain number of structured tasks?
- Is "bot" the right term here? What are some other options? "Algorithm", "Computer", "Auto-", "Machine", etc.?" What might better help convey that machine recommendations are fallible and the importance of human input?
Mobile user testing: September 2020
During the week of September 7, 2020, we used usertesting.com to conduct 10 tests of the mobile interactive prototypes, 5 tests each of Concepts A and B, all in English. By comparing how users interact with the two different approaches at this early stage, we wanted to better understand whether one or the other is better at providing users with good understanding and ability to successfully complete structured tasks, and to set them up for other kinds of editing afterward. Specific questions we wanted to answer were:
- Do users understand how they are improving an article by adding wikilinks?
- Do users seem like they will want to cruise through a feed of link edits?
- Do users understand that they're being given algorithmic suggestions?
- Do users make better considerations on machine-suggested links when they have the full context of the article (like in Concept A)?
- Do users complete tasks more confidently and quickly in a focused UI (like in Concept B)?
- Do users feel like they can progress to other, non-structured tasks?
- The users generally were able to exhibit good judgment for adding links. They understood that AI is fallible and that they have to think critically about the suggestions.
- While general understanding of what the task would be ("adding links") was low at first, they understood it well once they actually started doing the task. Understanding in Concept B was marginally lower.
- Concept B was not better at providing focus. The isolation of excerpts in many cases was mistaken for the whole article. There were also many misunderstandings in Concept B about whether the user would be seeing more suggestions for the same term, for the same article, or for different articles.
- Concept A better conveyed expectations on task length than Concept B. But the additional context of a whole article did not appear to be the primary factor of why.
- As participants proceed through several tasks, they become more focused on the specific link text and destination, and less on the article context. This seemed like it could lead to users making weak decisions, and this is a design challenge. This was true for both Concepts A and B.
- Almost every user intuitively knew they could exit from the suggestions and edit the article themselves by tapping the edit pencil.
- All users liked the option to view their edits once they finished, either to verify or admire them.
- “AI” was well understood as a concept and term. People knew the link suggestions came from AI, and generally preferred that term over other suggestions. This does not mean that the term will translate well to other languages.
- Copy and onboarding needs to be succinct and accessible in multiple points. Reading our instructions is important, but users tended not to read closely. This is a design challenge.
- We want to build Concept A for mobile, but absorbing some of the best parts of Concept B's design. These are the reasons why:
- User tests did not show advantages to Concept B.
- Concept A gives more exposure to rest of editing experience.
- Concept A will be more easily adapted to an “entry point in reading experience”: in addition to users being able to find tasks in a feed on their homepage, perhaps we could let them check to see if suggestions are available on articles as they read them.
- Concept A was generally preferred by community members who commented on the designs, with the reason being that it seemed like it would help users understand how editing works in a broader sense.
- We still need to design and test for desktop.
The team had these ideas from watching the user tests:
- Should we consider a “sandbox” version of the feature that lets users do a dry run through an article for which we know the “right” and “wrong” answers, and can then teach them along the way?
- Where and when should we put the clear door toward other kinds of editing? Should we have an explicit moment at the end of the flow that actively invites them copyedit or do another level task?
- It’s hard to explain the rules of adding a link before they try the task, because they don't have context. How might we show them the task a little bit, before they read the rules?
- Perhaps we could onboard the users in stages? First they learn a few of the rules, then they do some links, then we teach them a few more pointers, then they do more links?
- Should users have a cooling-off period after doing lots of suggestions really fast, where we wait for patrollers to catch up, so we can see if the user has been reverted?
Desktop mockups: October 2020
After designing, testing, and deciding on Concept A for mobile users, we moved on to thinking about desktop users. We again have the same question around Concepts A and B. The links below open interactive prototypes of each, which we are using for user testing.
- Concept A: the structured task takes place at the article, in the editor, using some of the existing visual editor components. This gives users greater exposure to the editing context and may make it more likely that they explore other kinds of editing tasks.
- Concept B: the structured task takes place on the newcomer homepage, essentially embedding the compact mobile experience into the page. Because the user doesn't have to leave the page, this may encourage them to complete more edits. They could also see their impact statistics increase as they edit.
We are user testing these designs during the week of October 23. See below for mockups showing the main interaction in each concept.
Link recommendation algorithm
See this page for an explanation of the link recommendation algorithm and for statistics around its accuracy.. In short, we believe that users will experience an accuracy around 75%, meaning that 75% of the suggestions they get should be added. It is possible to tune this number, but the higher the accuracy is, the fewer candidate link we will be able to recommend. After the feature is deployed, we can look at revert rates to get a sense of how to tune that parameter.
Link recommendation service backend
To follow along with engineering progress on the backend "add link" service, please see this page on Wikitech.