推荐引擎现在常见于各种网站和应用程序,通常使用某种人工智能(AI)来驱动个性化选择。随着依赖对与他们相关的项目的建议依赖,提出了建议内容清单,以鼓励持续互动的方式成为推动参与和用户忠诚度的主要因素。

我们最近进行了用户研究,调查了用户期望围绕建议书作为个性化内容的来源。

我们的学习参与者赞赏的个性化建议,帮助他们避免信息过载;这些建议在他们的时候最有效优先于通用内容Understanding the source of the recommendations也很重要,就像是ability to interact with suggestionsin order to provide positive or negative feedback, although many users didn’t bother to do so.

优先考虑个性化建议书

仅适用于您的内容,始终优先于通用项目。在网上,信息personalizedto the individual was seen as a valuable feature, helping users sift through the vast inventories on ecommerce or entertainment sites to find those few pearls they were interested in.

但是,这些个性化的建议很难找到某些网站,因为它们在页面上定位太低,低于促销内容的通用部分。这种放置使得它们更少可发现,使其包含在网站上基本上无价值。

“This [Recommended For Youcontent] should be higher up on the list. … Why not have this higher up on the list?This is what I'm going to buy. This is where I'm going to spend my money.

“可能更接近顶部,也许是正确的刚抵达。因为很多时候我并不总是滚动在主页上,通常我会看看第一个事情然后去看我想买东西的部分。这个页面上有很多东西Recommended For You是第二件事。所以,我可能会错过它。这实际上是我第一次见到它。“

Sephora主页的屏幕截图
sephora.com:个性化建议 - 在Recommended For You旋转木马 - 在主页上呈现太低。用户仅在提示他们浏览主页后发现它,并且可以在其他通用促销区域之上看到它。

个性化内容如此高度重视通用内容,即一个频繁的Sephora用户希望该网站的所有领域都适合她的个人偏好。

“I guess [in] any area of the website that I clicked on it would be nice to see things that they think I would like the most. … Especially new, just arrived things, because通常有很多东西,所以如果我最喜欢的东西是最重要的至少,即使他们展示了其他一切。“

这些个性化推荐区域出现在网站的主页上的越高,用户越有可能注意到并使用它们。例如,亚马逊包括几个清晰个性化的内容区域,附近的主页顶部,以及整个长滚动页面,并且用户通常被用户提及,作为对单个用户的高度量身定制的网站。事件列表网站EventBrite并不享受同样级别的街道可信度,但由于其个性化建议在主页上突出显示,即在主横幅/搜索区域下方,用户很容易找到推荐的活动列表。

Screenshot of Eventbrite homepage
EventBrite.com:为您的活动在主页上突出了个性化建议的一节,增加了用户注意到并浏览该内容的可能性。

显然说明数据来源

尽可能具体,具体对用于制作个性化建议的数据。此信息不仅为用户介绍其网上使用情况的哪些方面都被跟踪并考虑了建议,也是如此增加了该建议的可信度并明确表示内容是个性化的。

建议的解释(例如,从用户的行为历史中提及特定项目)helps users gauge the type of content included在该部分的建议中,并确定他们是否对建议感兴趣,或者宁愿寻找别的东西。

“在Netflix上,他们有不同的类别,显示'我们认为你会喜欢这部电影,因为你专门看了这部电影,'它会给你看电影的名字,它给你一个10-15的列表他们认为你想要根据你看到的电影。...... [我喜欢,因为我能想到,]哦,那部电影很好,寒冷和搞笑,容易观察,这就是我现在的情绪,所以我要仔细看看这些电影。或者,哦,那部电影非常激烈,很多戏剧,很多神秘,这就是我现在的心情。“

Screenshot of recommendations on Netflix
Netflix标有关于关于特定数据来源的建议部分(过去观看的电影或显示)用于制定建议。此信息允许用户了解他们为什么显示内容并为建议提供上下文。

至少,网站应该包括一些reference to the overall source(s) of data用于创建建议 - 如基于您过去的历史要么与您购买的物品有关。清晰而简明陈述这些来源时,and avoid adding vague descriptions such as “more.” Amazon Video, for example, stated that aRecommended Moviessection was根据您观看的标题和更多,留下了哪些用户想知道那些更多来源可能是。

亚马逊Prime视频建议的屏幕截图
亚马逊视频包括描述了用于推荐区域的通用数据源的字幕。但是,包含这句话和更多left users wondering what counted as more.
关于Amazon.com美容产品上市页面的建议屏幕截图
亚马逊产品上市页面具有突出的个性化建议,并具有明确标题,指出物品是与您已浏览的项目有关

单独的类别的建议

显示一组建议的特定来源的另一个好处是它倾向于强迫个性化内容的分离成较小的块,而不是将所有建议在单个类别中聚集在一起。用户赞赏能够浏览更多特定类别的建议,特别是对于具有大型和变化的库存的网站 - 该库存是电子商务产品还是娱乐内容。正如您不会将网站上的所有内容丢弃到一个大小的列表页面,不要将所有建议丢弃到单个分组中。

“These [你的每日混合es关于Spotify]有时候有点乐于助人,因为它们被分解为流派。...... [混合]是所有的音乐类型,这是我丈夫听到的所有音乐,所以它已成功过滤出他喜欢的类型。“

Spotify的屏幕截图是您的日常混合建议
Spotify建议创建单独的你的每日混合基于类型的播放列表。一名研究参与者表示,她更喜欢更广泛的这些建议你每周发现playlist, which grouped together songs that were too diverse.

这些建议群体越具体,他们将向用户指导相关项目越有用。如果个性化建议过于多样化(也许是因为用户拥有各种兴趣),就会感到困难,人们将不太可能与他们互动 - 无论是通过浏览还是通过积极编辑建议。

例如,几个竞赛Brite用户指出,他们的建议包括专业和个人活动的组合。虽然这个名单是基于他们过去的行为(他们已经购买了两种类型的事件的门票),但是当他们浏览活动时,它越来越相关,才能在空闲时间参加。

Similarly, a user on Amazon complained that her list of Kindle-book recommendations had become too unwieldy to browse and manage, due to the varied nature of the books she had purchased.根据通用电气分离这些推荐项nre会缓解这个问题的大部分问题,并鼓励持续互动。

“我曾经努力管理亚马逊会推荐的书籍会推荐,然后,我购买了许多不同类型的书籍以及试图通过竖起大拇指告诉亚马逊的任务或甚至给予五星级评分,它只是没有真正的工作。因为我可以为一个真正善良的小说五星级的小说,但那么我为技术书做了什么,这对我需要它的目的真的很好,但不是喜欢的,我想读这个。“

允许用户进行微调建议

并非所有用户都将有动力与建议进行互动,以便改善它们,但对于那些是那些的用户提供method to give feedback on recommendations要么编辑用于创建推荐的数据。People are most likely to interact with recommended content if they are frequent, loyal visitors of a site or if personalization is a major component of the service provided by the site. For instance, a Spotify user checked the recommended songs in her personalized每周发现playlist. When she heard a song she didn’t like, she searched for a way to give feedback to Spotify, so it wouldn’t play similar songs in the future. Sadly, the rating functionality was not available in this section of the web app, though it had been present in other areas.

“Oh, it's horrible, I hate this song! You can’t do anything! You're helpless in the face of songs that you don't like. Sometimes you can thumb up or thumb down on the app, but I don't know if you can do it on the computer. But that’s what I would do on the app … It seems like it's能够让我发现每周好的基本部分,能够提供负面反馈,以及积极的反馈。“

用户的屏幕截图有关Spotify的每周建议
Spotify:一项研究参与者在她的个性化播放列表中包含的歌曲提供反馈时感到失望,以改善未来的播放列表推荐。

编辑过去的活动的能力,如浏览历史或过去的购买允许用户指示系统折扣唯一的行为,例如为朋友购买礼物。在亚马逊,一个链接View or edit past browsing history在推荐项目列表旁边显示(请参阅以前的Amazon屏幕截图)。

在Amazon.com上的用户编辑浏览历史记录页面的屏幕截图
Amazon: Users can view and manage their browsing history to affect the types of products that the site will recommend for them in the future.

同样,Netflix上的用户可以通过访问其帐户信息并导航到他们的账户信息来查看他们的过去的活动观看历史页。在这里,用户可以删除他们味道的物品,因为他们的味道是不典型的(例如,因为别人用他们的个人资料观看他们)。(然而,表示该项目将不再用于提出建议。用户赞赏直接和描述性消息传递。

在Netflix上编辑其查看历史的用户屏幕截图
Netflix: Removing an item from an account’s past activity displayed a message that the item would no longer be used for recommendations.

快速且经常更新建议

When users did choose to interact with a site to fine-tune recommendations (for example, by posting a rating, adding an item to a list of favorites, or updating their profile information), they expected this action to be reflected in the recommendations immediately — especially when they provided negative feedback on a suggestion.

For example, when a personalized targeted advertisement was marked as irrelevant, people wanted the ad immediately removed. Pinterest allowed users to hide ads — referred to asPromoted Pins- 通过弹出菜单。单击隐藏广告立即用广告隐藏的占位符邮件替换了广告空间。这种处理并没有强制整个页面刷新或内容以回流,同时仍然有效地表明已经注册了反馈。

在pinterest.com上隐藏广告的进展的屏幕截图
Pinterest:互动以隐藏促销的PIN(广告)立即从用户的饲料中删除广告。

Of course, marking items as unwanted or irrelevant is not limited to advertisements. While browsing personalized show recommendations on Hulu, one study participant noticed several reality shows that did not match his interests. He was wasn’t sure why they were recommended to him. When asked what he would do to make his list of recommended shows better, he attempted to remove a show by giving it a very negative rating. He assumed that, because he gave such negative feedback, the site would update its recommendations not only to remove that specific show from the list of suggestions, but also to recommend fewer reality shows overall. Upon refreshing the page, however, he was presented with the same list of recommendations. He assumed that the system needed some ‘thinking’ time before it would be able to update its recommendations.

“所以现在我正在给我的反馈,我说我会亲自给它1星。[刷新页面,但建议没有改变。]所以也许需要一些时间才能重新处理?但下次我登录Hulu,我会期待那个所以要走了。对我来说更有趣的事情应该占据。“

当网站迅速对个性化建议进行更新时,用户留下了深刻的印象和动机,继续与该网站接触。

“只是通过保存其他类型的事件,改变Events For You......我不知道它会更快地改变......这很好,它显示了软件从你那里学习。“

结论

Personalized content has the potential to increase user engagement and satisfaction, when it is well executed. Good recommendations help users quickly identify items that interest them and foster ongoing brand loyalty, as users are likely to return to those sites and services that make it easy for them to find what they want.

Make personalized content discoverable by prioritizing it over generic content, such as site-wide promotions. If applicable, separate personalized recommendations into clear categories, and note the source of the data used to create the recommendations. Providing methods to interact with the suggested items empowers users to help hone future recommendations and can encourage increased engagement with the personalized content.