正如我们在之前的博客文章中所描述的那样，在Netflix，我们广泛使用个性化设置，并将每种情况都视为向我们的5700万以上会员中的每个人提供正确内容的机会。成员与我们的推荐进行互动的主要方式是通过主页，当他们在任何受支持的设备上登录Netflix时，就会看到该主页。主页的主要功能是帮助每个成员轻松找到自己喜欢的观看内容。我们面临的一个问题是，我们的目录包含的视频远远超过单个页面上显示的视频，并且每个成员都有自己独特的兴趣爱好。因此，一般的算法挑战就变成了如何最佳地调整每个成员的主页，使其相关，涵盖他们的兴趣和和观看意图，并且仍然允许他们探索其它内容。
As we’ve described in our previous blog posts, at Netflix we use personalization extensively and treat every situation as an opportunity to present the right content to each of our over 57 million members. The main way a member interacts with our recommendations is via the homepage, which they see when they log into Netflix on any supported device. The primary function of the homepage is to help each member easily find something to watch that they will enjoy. A problem we face is that our catalog contains many more videos than can be displayed on a single page and each member comes with their own unique set of interests. Thus, a general algorithmic challenge becomes how to best tailor each member’s homepage to make it relevant, cover their interests and intents, and still allow for exploration of our catalog.
这种类型的问题并不是Netflix独有的，新闻网站，搜索引擎和在线商店等其他组织也面临着这种问题。任何需要从大量可用的可能性中选择项目，然后以连贯且易于导航的方式展示它们的站点，都将面临相同的挑战。当然，优化Netflix主页的问题有其独特的方面，，比如界面限制，以及其电影和电视的消费方式与其他媒体相比都存在差异。
This type of problem is not unique to Netflix, it is faced by others such as news sites, search engines, and online stores. Any site that needs to choose items from a large number of available possibilities and then present them in a coherent and easy-to-navigate manner will face the same general challenges. Of course, the problem of optimizing Netflix homepages has its own unique aspects, ranging from interface constraints to differences with how movies and TV are consumed compared to other media.
An example of a personalized Netflix homepage on our website.Currently, the Netflix homepage on most devices is structured with videos (movies and TV shows) organized into thematically coherent rows presented in a two-dimensional layout. Members can scroll either horizontally on a row to see more videos in that row or vertically to see other rows. Thus, a key part of our personalization approach is how we choose rows to display on the homepage. This involves figuring out how to select the rows most relevant to each member, how to populate those rows with videos, and how to arrange them on the limited page area such that selecting a video to watch is intuitive. In the rest of this post, we will highlight what we think are the most relevant and interesting aspects of this problem and how we can go about solving some of them.
当前，大多数设备上的Netflix主页都是由视频（电影和电视节目）组成的，这些视频按二维布局呈现为主题一致的行。成员可以水平滚动查看该行中的更多视频，也可以垂直滚动查看其他行。因此，我们的个性化方法的关键部分是我们如何选择要显示在首页上的行。这涉及弄清楚如何选择与每个成员最相关的行，如何用视频填充这些行，以及如何将它们排列在有限的页面区域上，从而直观地选择要观看的视频。在本文的其余部分，我们将重点介绍我们认为与该问题最相关，最有趣的方面，以及我们如何才能解决其中的一些问题。
Evolution of our personalization approach.
Why Rows Anyway?
We organize our homepage into a series of rows to make it easy for members to navigate through a large portion of our catalog. By presenting coherent groups of videos in a row, providing a meaningful name for each row, and presenting rows in a useful order, members can quickly decide whether a whole set of videos in a row is likely to contain something that they are interested in watching. This allows members to either dive deeper and look for more videos in the theme or to skip them and look at another row. This would not be the case if, for example, the page contained a large, unorganized collection of relevant videos.
我们将首页组织成一系列的行，以使成员可以轻松浏览目录的很大一部分。通过连续显示一组连续的视频，为每行提供一个有意义的名称并按有用的顺序显示行，成员可以快速确定一行完整的视频集是否可能包含他们感兴趣的观看内容。这使成员可以更深入地研究主题中的更多视频，或者跳过它们并查看另一行。例如，如果该页面包含大量无组织的相关视频，则不是这种情况。
A possible row of titles that might be watched by one of our Netflix original characters.One natural way to group videos is by genre or sub-genre or other video metadata dimensions like release date. Of course, the relationship between videos in a row does not have to be due to metadata alone, but can also be formed from behavioral information (for example from collaborative filtering algorithms), videos we think a member is likely to watch, or even groups of videos watched by a friend. Thus, each row can offer a unique and personalized slice of the catalog for a member to navigate. Part of the challenge and fun of creating a personalized homepage is figuring out new ways to create useful groupings of videos, which we are constantly experimenting with (e.g., rows of titles that might be watched by one of our Netflix original characters shown above).
对视频进行分组的一种自然方法是按流派或子流派或其他视频元数据维度（例如发布日期）进行分类。当然，连续视频之间的关系不必仅取决于元数据，还可以由行为信息（例如，来自协作过滤算法），我们认为成员可能观看的视频甚至是分组来形成朋友观看的视频数量。因此，每一行都可以提供目录的唯一且个性化的切片，以供成员导航。创建个性化主页的挑战和乐趣的一部分是想出新方法来创建有用的视频分组，我们正在不断尝试（例如，上面显示的Netflix原始角色之一可能会观看的标题行）。
Process for creating and choosing rows.
Once we have a set of possible video groups to consider for a page, we can begin to assemble the homepage from them. To do this, we start by finding candidate groupings that are likely relevant for a member based on the information we know about them. This also involves coming up with the evidence (or explanations) to support the presentation of a row, for example the movies that the member has previously watched in a genre. Next, we filter each group to handle concerns like maturity rating or to remove some previously watched videos. After filtering, we rank the videos in each group according to a row-appropriate ranking algorithm, which produces an ordering of videos such that the most relevant videos for the member in a group are at the front of the row. From this set of row candidates we can then apply a row selection algorithm to assemble the full page. As the page is assembled, we do additional filtering like deduplication to remove repeat videos and format rows to the appropriate size for the device.
一旦我们考虑了一组可能的视频组用于一个页面，就可以开始从中组合主页。为此，我们首先根据我们了解的候选成员分组来查找可能与该成员相关的候选分组。这还涉及拿出证据（或解释）来支持一行的呈现，例如，该成员以前观看过的流派的电影。接下来，我们过滤每个组以处理诸如成熟度等级之类的问题，或删除一些以前观看的视频。过滤后，我们根据适合行的排名算法对每个组中的视频进行排名，该算法产生视频的排序，从而使与该组成员最相关的视频位于该行的最前面。然后，从这组候选行中，我们可以应用行选择算法来组装整个页面。在页面组装完成后，我们会进行重复数据删除等其他过滤操作，以删除重复的视频并将行格式设置为适合设备的大小。
Page-level algorithmic challenge 页面级算法挑战
To algorithmically create a good personalized homepage means assembling one page per member profile and device from thousands of videos that may be relevant for a member and from easily tens of thousands of potential rows, each with a variable number of videos. On top of that, we need to balance several factors that often compete for precious screen real estate. Our approach to personalization and recommendation largely focuses on helping our members find something new to watch, which we call discovery. However, we also want to make it easy for a member to watch the next episode of a show or re-watch something that they watched in the past, which normally falls outside the realm of recommendation. =We want our recommendations to be accurate in that they are relevant to the tastes of our members, but they also need to be diverse so that we can address the spectrum of a member’s interests versus only focusing on one. We want to be able to highlight the depth in the catalog we have in those interests and also the breadth we have across other areas to help our members explore and even find new interests. We want our recommendations to be fresh and responsive to the actions a member takes, such as watching a show, adding to their list, or rating; but we also want some stability so that people are familiar with their homepage and can easily find videos they’ve been recommended in the recent past.= Finally, we need to be able to place task-oriented rows, such as “My List,” in amongst the more discovery-oriented rows.
要通过算法创建良好的个性化首页，是指从成千上万的与会员相关的视频和成千上万的潜在行（每个视频的数目不固定）为每个会员个人资料和设备组合一页。最重要的是，我们需要权衡一些经常争夺珍贵屏幕的因素。我们的个性化和推荐方法主要集中在帮助我们的会员找到值得关注的新事物，我们称之为发现discovery。但是，我们还希望使成员能够轻松观看节目的下一集或重新观看他们过去观看的内容，而这通常超出了推荐范围。
我们希望我们的建议是准确的，因为它们与我们的会员的口味有关，但它们也必须是多种多样的，以便我们可以解决会员利益的范围而不是仅仅关注一个人的利益。我们希望能够突显我们对这些兴趣感兴趣的目录的深度，以及我们在其他领域所拥有的广度，以帮助我们的成员探索甚至发现新的兴趣。我们希望我们的建议是新鲜的，并能响应会员采取的行动，例如观看表演，添加到他们的列表或评分；但我们也希望保持稳定性，以便人们熟悉其主页并可以轻松找到他们最近推荐的视频。
最后，我们需要能够将面向任务的行（例如“我的列表”）放在更多面向发现的行中。
Each device has different hardware capabilities that can limit the number of videos or rows displayed at any one time and how big the whole page can be. As such, the page generation process must be aware of the constraints of the device for which it is creating the page, including the number of rows, the minimum and maximum length of a row, the size of the visible portion of the page, and whether or not certain rows are required or are not applicable for a certain device.
While there are many challenges to page generation, tackling recommendation problems at this level also opens up new solutions. As mentioned before, selecting a diverse set of items is important in a recommendation system. However, it can be challenging to navigate a diverse ranking since the relevant items may be blended with other items that do not match someone’s current intent. However, by presenting a two-dimensional navigation layout, a member can scroll vertically to easily skip over entire groups of content that may not match their current intent and then find a more relevant set, which they can then scroll horizontally to see more recommendations in that set. This allows for coherent, meaningful individual rows to be selected while maintaining the diversity of the videos shown on the whole page, and thus lets the member have both relevance and diversity.
每个设备具有不同的硬件功能，这些功能可以限制一次显示的视频或行的数量以及整个页面的大小。因此，页面生成过程必须知道为其创建页面的设备的约束，包括行数，行的最小和最大长度，页面可见部分的大小以及是否需要某些行或不适用于某些设备。
尽管页面生成面临许多挑战，但在此级别解决推荐问题也带来了新的解决方案。如前所述，在推荐系统中选择一组不同的项目很重要。但是，由于相关项目可能会与不符合某人当前意图的其他项目混合在一起，因此导航到不同的排名可能会带来挑战。但是，通过呈现二维导航布局，成员可以垂直滚动以轻松跳过可能不符合其当前意图的整个内容组，然后找到一个更相关的集合，然后他们可以水平滚动以查看更多建议。该集。这样一来，可以选择连贯且有意义的单个行，同时保持整个页面上显示的视频的多样性，
Building a page algorithmically 通过算法构建页面
There are several approaches for how we can build our homepage algorithmically. The most basic is a rule-based approach, which we used for a long time. Here a set of rules define a template that dictates for all members what types of rows can go in certain positions on the page. For example, the rules could specify that the first row would be Continue Watching (if any), then Top Picks (if any), then Popular on Netflix, then 5 personalized genre rows, and so on. The only personalization in this approach was from selecting candidate rows in a personalized way, such as including“Because you watched” rows for videos someone has watched in the past and genre rows based on known genre preferences. To choose specific rows within each type, simple heuristics and sampling were used. We evolved this template using A/B testing to understand where to place rows for all members.
有几种方法可用于以算法方式构建主页。最基本的是基于规则的方法，我们使用了很长时间。在这里，一组规则定义了一个模板，该模板为所有成员指示在页面上的某些位置可以进入哪种类型的行。例如，规则可以指定第一行是“继续观看”（如果有），然后是“热门精选”（如果有），然后是“在Netflix上受欢迎”，然后是5个个性化类型行，依此类推。这种方法唯一的个性化设置是通过以个性化方式选择候选行，例如包括某人过去观看过的视频的“因为您观看了”行，以及根据已知的流派首选项进行的流派。为了选择每种类型中的特定行，使用了简单的试探法和采样。
This approach served us well, but it ignored many aspects we consider important for the quality of the page, such as the quality of the videos in the row, the amount of diversity on the page, the affinity of members for specific kinds of rows, and the quality of the evidence we can surface for each video. It also made it hard to add new types of rows, because for a new row to succeed it would need to not only contain a relevant set of videos in a good order but also be placed appropriately in the template. Because of this, the rules for the template grew over time and became too complex to handle the variety of rows and how they should all be placed, which represented a local optimum for the member experience.
这种方法对我们很有帮助，但它忽略了许多我们认为对页面质量至关重要的方面，例如行中视频的质量，页面上的多样性，成员对特定类型行的亲和力，以及我们可以为每个视频展示的证据的质量。这也使添加新类型的行变得困难，因为要使新行成功，它不仅需要以良好的顺序包含一组相关的视频，而且还需要适当地放置在模板中。因此，模板的规则随着时间的增长而变得越来越复杂，以至于无法处理各种各样的行以及应如何放置所有行，这代表了成员体验的局部最优。
To address these issues, we can instead think of personalizing the ordering of rows on the homepage. The simplest approach for doing this is to treat rows as items in a ranking problem, which we call a row-ranking approach. For this approach, we could leverage a lot of existing recommendation or learning-to-rank approaches by developing a scoring function for rows, applying it to all the candidate rows independently, sorting by that function, and then picking the top ones to fill the page. Even though the space of rows may be relatively big, this type of approach could be relatively fast and may result in reasonable accuracy. However, doing this would lack any notion of diversity, so someone could easily get a page full of slight variations of their interests, such as many rows each with different variants of comedies: late-night, family, romantic, action, etc.
为了解决这些问题，我们可以考虑对主页上的行进行个性化设置。执行此操作的最简单方法是将行视为排序问题中的项目，我们称之为行排序方法。对于这种方法，我们可以通过为行开发一个评分函数，将其独立地应用于所有候选行，按该函数进行排序，然后选择最上面的行来填充行，从而利用很多现有的推荐或学习排名方法页。即使行的空间可能相对较大，这种方法也可能相对较快并可能导致合理的准确性。但是，这样做会缺乏多样性的概念，因此某人可以轻松获得一个页面，其中充满了他们的兴趣的细微变化，例如许多行，每行都有不同的喜剧变体：深夜，家庭，浪漫，
A simple way to add in diversity is to switch from a row-ranking approach to a stage-wise approach using a scoring function that considers both a row as well as its relationship to both the previous rows and the previous videos already chosen for the page. In this case, one can take a simple greedy approach and pick the row that maximizes this function as the next row to use and then re-score all the rows for the next position taking that selection into account. Depending on the diversity function, this greedy selection may not lead to an optimal page. Using a stage-wise approach with k-row lookahead could result in a more optimal page than greedy selection, but it comes with increased computational cost. Other approaches to greedily add diversity based on submodular function maximization can also be used.
添加多样性的一种简单方法是使用评分函数从行排名方法切换到逐级方法，该评分函数既考虑行又考虑其与先前行和先前已为页面选择的视频的关系。 在这种情况下，可以采用简单的贪心方法，选择最大化此函数的行作为要使用的下一行，然后将下一个位置的所有行重新评分。 这种贪心的选择可能无法产生最佳页面。 使用具有k行前瞻的分阶段方法可以产生比贪心选择更优化的页面，但是它带来了增加的计算成本。也可以使用其他基于子模块函数最大化贪婪地增加分集的方法。
However, even the stage-wise algorithm is not guaranteed to produce an optimal page because a fixed horizon may limit the ability to fill in better rows further down the page. Thus, if we can instead take a page-wise approach by defining a full-page scoring function, we can try to optimize it by choosing rows and videos appropriately to fill the page. Of course, the space of possible pages is huge, even larger than the space of possible rows. Since a page layout is defined in a discrete space, directly optimizing a function that defines the quality of the whole page is a computationally prohibitive integer programming problem.
然而，即使是阶段式算法也不能保证产生最佳页面，因为固定的时间范围可能会限制在页面下方填充更好的行的能力。 如果我们可以定义整页评分函数，我们可以尝试通过适当选择行和视频来填充页面来优化它。 当然，页面组合的搜索空间很大，因此直接优化定义整个页面质量的函数在算力上是很难实现的。
When solving a page optimization problem with any of these approaches, there are also various constraints that need to be taken into account that were mentioned before, like deduping, filtering, and device-specific constraints. Each of these constraints add to the complexity of the optimization problem.
当使用这些方法中的任何一种来解决页面优化问题时，还需要考虑前面提到的各种约束，例如重复数据删除，过滤和特定于设备的约束。这些约束中的每一个都增加了优化问题的复杂性。
Notional importance of navigation modeling.Members are more likely to scan vertically than horizontally, which means videos presented in the upper left are much more likely to be seen than those in the lower right.
导航建模的名义重要性。成员更有可能在垂直方向而非水平方向进行扫描，这意味着左上角的视频比右下角的视频更有可能被观看。
When forming the homepage it is also important to consider how members navigate the page, i.e., to consider which positions on the page they are likely to pay attention to and interact with in a session. Placing the most relevant videos in the positions that are most likely to be seen, which tends to be the upper-left corner, should reduce the time for a member to find something relevant to watch. However, modeling navigation on a two-dimensional page is difficult, especially taking into account that different people may navigate d
