{"id":9198,"date":"2026-09-01T07:17:41","date_gmt":"2026-09-01T07:17:41","guid":{"rendered":"https:\/\/aboutknowledge.com\/?p=9198"},"modified":"2026-09-01T09:33:30","modified_gmt":"2026-09-01T09:33:30","slug":"ai-based-customer-recommendations","status":"publish","type":"post","link":"https:\/\/aboutknowledge.com\/zh\/ai-based-customer-recommendations\/","title":{"rendered":"AI-Based Customer Recommendations"},"content":{"rendered":"

What a recommendation actually is<\/h2>\n

A recommendation answers one question: given what this customer has bought and looked at, what else are they likely to want?<\/strong><\/p>\n

It is not clairvoyance. It is a pattern \u2014 customers who bought this also bought that, or customers like this one tend to buy this next.<\/p>\n

Done well it is genuinely useful to the customer. Done badly it is the shop suggesting a second identical item to someone who just bought one, which makes you look like you are not paying attention.<\/p>\n

Where they appear<\/h2>\n

Four places, with different economics.<\/p>\n

On a product page.<\/strong> “Customers also bought” or “goes well with this”. The most familiar and usually the safest.<\/p>\n

At checkout.<\/strong> Suggesting an add-on before the order completes. Effective, and the place where getting it wrong costs most \u2014 a badly timed suggestion adds friction at the moment you least want it.<\/p>\n

In email.<\/strong> Based on purchase history. Works well for consumables and reorders.<\/p>\n

To a salesperson.<\/strong> Suggesting what to discuss on the next call. Underused in B2B and often the highest-value version, because a person filters it before the customer hears it.<\/p>\n

\n

FIGURE 1: WHERE RECOMMENDATIONS EARN THEIR PLACE<\/p>\n

\n
\n

On the product page<\/p>\n

    \n
  • Complementary items. Familiar, safe, and the customer is already browsing.<\/li>\n<\/ul>\n<\/div>\n
    \n

    In email<\/p>\n

      \n
    • Reorder reminders and consumables. Works because the timing is predictable.<\/li>\n<\/ul>\n<\/div>\n
      \n

      To a salesperson<\/p>\n

        \n
      • A prompt before a call. A person filters it, so a poor suggestion costs nothing.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n

        The kinds<\/h2>\n

        Three approaches, and knowing which you are using explains its weaknesses.<\/p>\n

        Bought-together.<\/strong> Customers who bought A also bought B. Simple, effective, and needs volume \u2014 a few hundred orders will surface coincidences rather than patterns.<\/p>\n

        Similar customer.<\/strong> Customers resembling this one bought B. Better for larger catalogues, and it needs enough customers to find resemblance.<\/p>\n

        Complementary by attribute.<\/strong> Products that go with this one, defined by their characteristics or by rules you set.<\/p>\n

        That last one deserves attention.<\/strong> For many businesses, a well-maintained list of “these go together”, set by someone who knows the products, outperforms a model trained on thin data.<\/p>\n

        If you have fewer than a few thousand orders, do that instead.<\/strong> It is not less sophisticated in any way that matters. It is more accurate.<\/p>\n

        Where they work best<\/h2>\n

        Consumables and repeat purchases.<\/strong> Someone who buys filters every three months is highly predictable, and a well-timed reminder is a service rather than a sale.<\/p>\n

        Genuine complements.<\/strong> A printer and its cartridges. A machine and its spare parts. These relationships are real and stable.<\/p>\n

        Large catalogues.<\/strong> Where customers cannot reasonably browse everything, recommendation is navigation.<\/p>\n

        Where they do not<\/h2>\n

        Considered, infrequent purchases.<\/strong> Somebody buying one significant item every three years has no pattern to learn from.<\/p>\n

        Small catalogues.<\/strong> If you sell forty products, customers can see them all. Recommendation adds nothing.<\/p>\n

        Thin data.<\/strong> A few hundred orders produces suggestions based on noise, presented with the same confidence as good ones.<\/p>\n

        \n

        FIGURE 2: WHEN TO USE A MODEL AND WHEN TO USE RULES<\/p>\n

        \n
        \n

        A model works when<\/p>\n

          \n
        • Thousands of orders in the history<\/li>\n
        • Repeat and consumable purchases<\/li>\n
        • A catalogue too large to browse<\/li>\n
        • Stable product relationships<\/li>\n<\/ul>\n<\/div>\n
          \n

          Rules work better when<\/p>\n

            \n
          • A few hundred orders<\/li>\n
          • Infrequent, considered purchases<\/li>\n
          • A small catalogue<\/li>\n
          • You know the pairings better than the data does<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n

            The mistakes that annoy customers<\/h2>\n

            Five, and all of them are avoidable.<\/p>\n

            Suggesting what they just bought.<\/strong> A second washing machine to someone who bought one yesterday. It is the most common failure and it makes you look inattentive.<\/p>\n

            Suggesting out-of-stock items.<\/strong> In Odoo this is easily avoided \u2014 the recommendation and the stock are the same database. There is no excuse for it.<\/p>\n

            Suggesting something more expensive with no reason given.<\/strong> If you are recommending an upgrade, say what it does differently.<\/p>\n

            Too many suggestions.<\/strong> Three relevant items beat twelve. A long list signals that you do not know which one fits.<\/p>\n

            Ignoring the obvious.<\/strong> If a customer bought a device that needs a specific consumable, suggest the consumable. That does not need a model.<\/p>\n

            The advantage in Odoo<\/h2>\n

            Because it is one database, a recommendation can use what a standalone shop engine cannot.<\/p>\n

            Actual purchase history<\/strong>, not just website behaviour.<\/p>\n

            Real stock<\/strong>, so nothing out of stock is suggested.<\/p>\n

            Full customer context<\/strong> \u2014 their tier, their pricing, their credit position, whether they have an open complaint.<\/p>\n

            That last point matters more than it sounds. Recommending an upsell to a customer with an unresolved support ticket is a bad look<\/strong>, and a system that can see both avoids it.<\/p>\n

            Measuring it<\/h2>\n

            Four numbers, and one caution.<\/p>\n

            Attach rate.<\/strong> How often a suggestion is added to an order.<\/p>\n

            Average order value.<\/strong> Should rise if it is working.<\/p>\n

            Return rate on recommended items.<\/strong> Watch this. If it rises, you are successfully selling people things they did not want, which is worse than selling nothing.<\/p>\n

            Unsubscribes on recommendation emails.<\/strong> Rising means the suggestions are wrong or too frequent.<\/p>\n

            The caution:<\/strong> attach rate alone is a bad measure. A high attach rate with a rising return rate is a loss dressed up as a win.<\/p>\n

            \n

            FIGURE 3: A SENSIBLE WAY TO START<\/p>\n

            \n
            \n

            Start with rules you know<\/p>\n

              \n
            • Obvious pairings, set by someone who knows the products. Often better than a thin model.<\/li>\n<\/ul>\n<\/div>\n
              \n

              Exclude what is out of stock<\/p>\n

                \n
              • One database means there is no excuse for suggesting the unavailable.<\/li>\n<\/ul>\n<\/div>\n
                \n

                Show three, not twelve<\/p>\n

                  \n
                • A long list says you do not know which one fits.<\/li>\n<\/ul>\n<\/div>\n
                  \n

                  Watch returns, not just attach rate<\/p>\n

                    \n
                  • Selling people what they did not want is a loss with good statistics.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n

                    Where to begin<\/h2>\n

                    1. Fix your product data.<\/strong> Categories, attributes and relationships. Recommendations are only as good as the structure underneath them.<\/p>\n

                    2. Start with rules.<\/strong> Set obvious pairings manually. Measure the effect. This is often most of the available value.<\/p>\n

                    3. Add automatic recommendations<\/strong> once you have enough order history to support them.<\/p>\n

                    4. Exclude out-of-stock items<\/strong> always.<\/p>\n

                    5. Limit to three suggestions.<\/strong><\/p>\n

                    6. Watch returns as closely as attach rate.<\/strong><\/p>\n

                    The short version<\/h2>\n

                    Recommendations work where purchases repeat, complements are real, and you have enough history for patterns to be genuine rather than coincidental.<\/p>\n

                    For many businesses, hand-set rules outperform a model.<\/strong> That is not a lesser option \u2014 with a few hundred orders, it is the more accurate one.<\/p>\n

                    Never suggest what is out of stock, or what they just bought.<\/strong> And measure returns alongside attach rate, because selling people things they send back is not a win.<\/p>\n

                    \n

                    Wondering whether recommendations would work for your catalogue?<\/p>\n

                    Get in touch. We will look at your order volume and product relationships, and tell you honestly whether a model or a set of rules will serve you better.<\/p>\n<\/div>","protected":false},"excerpt":{"rendered":"

                    What a recommendation actually is A recommendation answers one question: given what this customer has bought and looked at, what else are they likely to want? It is not clairvoyance. It is a pattern \u2014 customers who bought this also bought that, or customers like this one tend to buy this next. Done well it […]<\/p>\n","protected":false},"author":3,"featured_media":9199,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[26],"tags":[],"class_list":["post-9198","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai"],"yoast_head":"\nAI Customer Recommendations: Where They Work Best<\/title>\n<meta name=\"description\" content=\"Learn how AI customer recommendations work and where to use them \u2014 product pages, checkout, email, and sales calls \u2014 to boost sales without adding friction.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/aboutknowledge.com\/zh\/ai-based-customer-recommendations\/\" \/>\n<meta property=\"og:locale\" content=\"zh_HK\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI-Based Customer Recommendations\" \/>\n<meta property=\"og:description\" content=\"Learn how AI customer recommendations work and where to use them \u2014 product pages, checkout, email, and sales calls \u2014 to boost sales without adding friction.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/aboutknowledge.com\/zh\/ai-based-customer-recommendations\/\" \/>\n<meta property=\"og:site_name\" content=\"AboutKnowledge\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/aboutknowledge28\/\" \/>\n<meta property=\"article:published_time\" content=\"2026-09-01T07:17:41+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2026-09-01T09:33:30+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/aboutknowledge.com\/wp-content\/uploads\/2026\/09\/istockphoto-1304484797-612x612-1.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"612\" \/>\n\t<meta property=\"og:image:height\" content=\"408\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"author\" content=\"kopraveen\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"kopraveen\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"6 \u5206\u9418\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\\\/\\\/aboutknowledge.com\\\/ai-based-customer-recommendations\\\/#article\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/aboutknowledge.com\\\/ai-based-customer-recommendations\\\/\"},\"author\":{\"name\":\"kopraveen\",\"@id\":\"https:\\\/\\\/aboutknowledge.com\\\/#\\\/schema\\\/person\\\/f14efbc95a95a4cec982367fb079cdf4\"},\"headline\":\"AI-Based Customer Recommendations\",\"datePublished\":\"2026-09-01T07:17:41+00:00\",\"dateModified\":\"2026-09-01T09:33:30+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\\\/\\\/aboutknowledge.com\\\/ai-based-customer-recommendations\\\/\"},\"wordCount\":1099,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\\\/\\\/aboutknowledge.com\\\/#organization\"},\"image\":{\"@id\":\"https:\\\/\\\/aboutknowledge.com\\\/ai-based-customer-recommendations\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/aboutknowledge.com\\\/wp-content\\\/uploads\\\/2026\\\/09\\\/istockphoto-1304484797-612x612-1.webp\",\"articleSection\":[\"AI\"],\"inLanguage\":\"zh-HK\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\\\/\\\/aboutknowledge.com\\\/ai-based-customer-recommendations\\\/#respond\"]}]},{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/aboutknowledge.com\\\/ai-based-customer-recommendations\\\/\",\"url\":\"https:\\\/\\\/aboutknowledge.com\\\/ai-based-customer-recommendations\\\/\",\"name\":\"AI Customer Recommendations: Where They Work Best\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/aboutknowledge.com\\\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\\\/\\\/aboutknowledge.com\\\/ai-based-customer-recommendations\\\/#primaryimage\"},\"image\":{\"@id\":\"https:\\\/\\\/aboutknowledge.com\\\/ai-based-customer-recommendations\\\/#primaryimage\"},\"thumbnailUrl\":\"https:\\\/\\\/aboutknowledge.com\\\/wp-content\\\/uploads\\\/2026\\\/09\\\/istockphoto-1304484797-612x612-1.webp\",\"datePublished\":\"2026-09-01T07:17:41+00:00\",\"dateModified\":\"2026-09-01T09:33:30+00:00\",\"description\":\"Learn how AI customer recommendations work and where to use them \u2014 product pages, checkout, email, and sales calls \u2014 to boost sales without adding friction.\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/aboutknowledge.com\\\/ai-based-customer-recommendations\\\/#breadcrumb\"},\"inLanguage\":\"zh-HK\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/aboutknowledge.com\\\/ai-based-customer-recommendations\\\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"zh-HK\",\"@id\":\"https:\\\/\\\/aboutknowledge.com\\\/ai-based-customer-recommendations\\\/#primaryimage\",\"url\":\"https:\\\/\\\/aboutknowledge.com\\\/wp-content\\\/uploads\\\/2026\\\/09\\\/istockphoto-1304484797-612x612-1.webp\",\"contentUrl\":\"https:\\\/\\\/aboutknowledge.com\\\/wp-content\\\/uploads\\\/2026\\\/09\\\/istockphoto-1304484797-612x612-1.webp\",\"width\":612,\"height\":408},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/aboutknowledge.com\\\/ai-based-customer-recommendations\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/aboutknowledge.com\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"AI-Based Customer Recommendations\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/aboutknowledge.com\\\/#website\",\"url\":\"https:\\\/\\\/aboutknowledge.com\\\/\",\"name\":\"AboutKnowledge\",\"description\":\"System Integrator You Can Trust\",\"publisher\":{\"@id\":\"https:\\\/\\\/aboutknowledge.com\\\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/aboutknowledge.com\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"zh-HK\"},{\"@type\":\"Organization\",\"@id\":\"https:\\\/\\\/aboutknowledge.com\\\/#organization\",\"name\":\"AboutKnowledge (Hong Kong) Limited\",\"url\":\"https:\\\/\\\/aboutknowledge.com\\\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"zh-HK\",\"@id\":\"https:\\\/\\\/aboutknowledge.com\\\/#\\\/schema\\\/logo\\\/image\\\/\",\"url\":\"https:\\\/\\\/aboutknowledge.com\\\/wp-content\\\/uploads\\\/2021\\\/11\\\/logo.png\",\"contentUrl\":\"https:\\\/\\\/aboutknowledge.com\\\/wp-content\\\/uploads\\\/2021\\\/11\\\/logo.png\",\"width\":560,\"height\":256,\"caption\":\"AboutKnowledge (Hong Kong) Limited\"},\"image\":{\"@id\":\"https:\\\/\\\/aboutknowledge.com\\\/#\\\/schema\\\/logo\\\/image\\\/\"},\"sameAs\":[\"https:\\\/\\\/www.facebook.com\\\/aboutknowledge28\\\/\",\"https:\\\/\\\/www.linkedin.com\\\/company\\\/104125547\"]},{\"@type\":\"Person\",\"@id\":\"https:\\\/\\\/aboutknowledge.com\\\/#\\\/schema\\\/person\\\/f14efbc95a95a4cec982367fb079cdf4\",\"name\":\"kopraveen\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"zh-HK\",\"@id\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/1e4b2c06a01572b023ee9a6a7052f720b27e9c900dc4222fd2882d26d352bf7a?s=96&d=mm&r=g\",\"url\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/1e4b2c06a01572b023ee9a6a7052f720b27e9c900dc4222fd2882d26d352bf7a?s=96&d=mm&r=g\",\"contentUrl\":\"https:\\\/\\\/secure.gravatar.com\\\/avatar\\\/1e4b2c06a01572b023ee9a6a7052f720b27e9c900dc4222fd2882d26d352bf7a?s=96&d=mm&r=g\",\"caption\":\"kopraveen\"},\"url\":\"https:\\\/\\\/aboutknowledge.com\\\/zh\\\/author\\\/kopraveen\\\/\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"AI Customer Recommendations: Where They Work Best","description":"Learn how AI customer recommendations work and where to use them \u2014 product pages, checkout, email, and sales calls \u2014 to boost sales without adding friction.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/aboutknowledge.com\/zh\/ai-based-customer-recommendations\/","og_locale":"zh_HK","og_type":"article","og_title":"AI-Based Customer Recommendations","og_description":"Learn how AI customer recommendations work and where to use them \u2014 product pages, checkout, email, and sales calls \u2014 to boost sales without adding friction.","og_url":"https:\/\/aboutknowledge.com\/zh\/ai-based-customer-recommendations\/","og_site_name":"AboutKnowledge","article_publisher":"https:\/\/www.facebook.com\/aboutknowledge28\/","article_published_time":"2026-09-01T07:17:41+00:00","article_modified_time":"2026-09-01T09:33:30+00:00","og_image":[{"width":612,"height":408,"url":"https:\/\/aboutknowledge.com\/wp-content\/uploads\/2026\/09\/istockphoto-1304484797-612x612-1.webp","type":"image\/webp"}],"author":"kopraveen","twitter_card":"summary_large_image","twitter_misc":{"Written by":"kopraveen","Est. reading time":"6 \u5206\u9418"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/aboutknowledge.com\/ai-based-customer-recommendations\/#article","isPartOf":{"@id":"https:\/\/aboutknowledge.com\/ai-based-customer-recommendations\/"},"author":{"name":"kopraveen","@id":"https:\/\/aboutknowledge.com\/#\/schema\/person\/f14efbc95a95a4cec982367fb079cdf4"},"headline":"AI-Based Customer Recommendations","datePublished":"2026-09-01T07:17:41+00:00","dateModified":"2026-09-01T09:33:30+00:00","mainEntityOfPage":{"@id":"https:\/\/aboutknowledge.com\/ai-based-customer-recommendations\/"},"wordCount":1099,"commentCount":0,"publisher":{"@id":"https:\/\/aboutknowledge.com\/#organization"},"image":{"@id":"https:\/\/aboutknowledge.com\/ai-based-customer-recommendations\/#primaryimage"},"thumbnailUrl":"https:\/\/aboutknowledge.com\/wp-content\/uploads\/2026\/09\/istockphoto-1304484797-612x612-1.webp","articleSection":["AI"],"inLanguage":"zh-HK","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/aboutknowledge.com\/ai-based-customer-recommendations\/#respond"]}]},{"@type":"WebPage","@id":"https:\/\/aboutknowledge.com\/ai-based-customer-recommendations\/","url":"https:\/\/aboutknowledge.com\/ai-based-customer-recommendations\/","name":"AI Customer Recommendations: Where They Work Best","isPartOf":{"@id":"https:\/\/aboutknowledge.com\/#website"},"primaryImageOfPage":{"@id":"https:\/\/aboutknowledge.com\/ai-based-customer-recommendations\/#primaryimage"},"image":{"@id":"https:\/\/aboutknowledge.com\/ai-based-customer-recommendations\/#primaryimage"},"thumbnailUrl":"https:\/\/aboutknowledge.com\/wp-content\/uploads\/2026\/09\/istockphoto-1304484797-612x612-1.webp","datePublished":"2026-09-01T07:17:41+00:00","dateModified":"2026-09-01T09:33:30+00:00","description":"Learn how AI customer recommendations work and where to use them \u2014 product pages, checkout, email, and sales calls \u2014 to boost sales without adding friction.","breadcrumb":{"@id":"https:\/\/aboutknowledge.com\/ai-based-customer-recommendations\/#breadcrumb"},"inLanguage":"zh-HK","potentialAction":[{"@type":"ReadAction","target":["https:\/\/aboutknowledge.com\/ai-based-customer-recommendations\/"]}]},{"@type":"ImageObject","inLanguage":"zh-HK","@id":"https:\/\/aboutknowledge.com\/ai-based-customer-recommendations\/#primaryimage","url":"https:\/\/aboutknowledge.com\/wp-content\/uploads\/2026\/09\/istockphoto-1304484797-612x612-1.webp","contentUrl":"https:\/\/aboutknowledge.com\/wp-content\/uploads\/2026\/09\/istockphoto-1304484797-612x612-1.webp","width":612,"height":408},{"@type":"BreadcrumbList","@id":"https:\/\/aboutknowledge.com\/ai-based-customer-recommendations\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/aboutknowledge.com\/"},{"@type":"ListItem","position":2,"name":"AI-Based Customer Recommendations"}]},{"@type":"WebSite","@id":"https:\/\/aboutknowledge.com\/#website","url":"https:\/\/aboutknowledge.com\/","name":"AboutKnowledge","description":"System Integrator You Can Trust","publisher":{"@id":"https:\/\/aboutknowledge.com\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/aboutknowledge.com\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"zh-HK"},{"@type":"Organization","@id":"https:\/\/aboutknowledge.com\/#organization","name":"AboutKnowledge (Hong Kong) Limited","url":"https:\/\/aboutknowledge.com\/","logo":{"@type":"ImageObject","inLanguage":"zh-HK","@id":"https:\/\/aboutknowledge.com\/#\/schema\/logo\/image\/","url":"https:\/\/aboutknowledge.com\/wp-content\/uploads\/2021\/11\/logo.png","contentUrl":"https:\/\/aboutknowledge.com\/wp-content\/uploads\/2021\/11\/logo.png","width":560,"height":256,"caption":"AboutKnowledge (Hong Kong) Limited"},"image":{"@id":"https:\/\/aboutknowledge.com\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/aboutknowledge28\/","https:\/\/www.linkedin.com\/company\/104125547"]},{"@type":"Person","@id":"https:\/\/aboutknowledge.com\/#\/schema\/person\/f14efbc95a95a4cec982367fb079cdf4","name":"kopraveen","image":{"@type":"ImageObject","inLanguage":"zh-HK","@id":"https:\/\/secure.gravatar.com\/avatar\/1e4b2c06a01572b023ee9a6a7052f720b27e9c900dc4222fd2882d26d352bf7a?s=96&d=mm&r=g","url":"https:\/\/secure.gravatar.com\/avatar\/1e4b2c06a01572b023ee9a6a7052f720b27e9c900dc4222fd2882d26d352bf7a?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/1e4b2c06a01572b023ee9a6a7052f720b27e9c900dc4222fd2882d26d352bf7a?s=96&d=mm&r=g","caption":"kopraveen"},"url":"https:\/\/aboutknowledge.com\/zh\/author\/kopraveen\/"}]}},"_links":{"self":[{"href":"https:\/\/aboutknowledge.com\/zh\/wp-json\/wp\/v2\/posts\/9198","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/aboutknowledge.com\/zh\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/aboutknowledge.com\/zh\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/aboutknowledge.com\/zh\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/aboutknowledge.com\/zh\/wp-json\/wp\/v2\/comments?post=9198"}],"version-history":[{"count":1,"href":"https:\/\/aboutknowledge.com\/zh\/wp-json\/wp\/v2\/posts\/9198\/revisions"}],"predecessor-version":[{"id":9200,"href":"https:\/\/aboutknowledge.com\/zh\/wp-json\/wp\/v2\/posts\/9198\/revisions\/9200"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/aboutknowledge.com\/zh\/wp-json\/wp\/v2\/media\/9199"}],"wp:attachment":[{"href":"https:\/\/aboutknowledge.com\/zh\/wp-json\/wp\/v2\/media?parent=9198"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/aboutknowledge.com\/zh\/wp-json\/wp\/v2\/categories?post=9198"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/aboutknowledge.com\/zh\/wp-json\/wp\/v2\/tags?post=9198"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}