{"id":1476,"date":"2025-09-03T21:40:15","date_gmt":"2025-09-03T21:40:15","guid":{"rendered":"https:\/\/dev95.site\/building-etsy-buyer-profiles-with-llms\/"},"modified":"2025-09-03T21:40:15","modified_gmt":"2025-09-03T21:40:15","slug":"building-etsy-buyer-profiles-with-llms","status":"publish","type":"post","link":"https:\/\/dev95.site\/ar\/building-etsy-buyer-profiles-with-llms\/","title":{"rendered":"Building Etsy Buyer Profiles with LLMs"},"content":{"rendered":"<div id=\"dev95-3547073072\" class=\"dev95-- dev95-entity-placement\"><script async=\"async\" data-cfasync=\"false\" src=\"https:\/\/pl27862732.profitableratecpmnetwork.com\/2ad7a50e0bbc23ac6801d7b77c501463\/invoke.js\"><\/script>\r\n<div id=\"container-2ad7a50e0bbc23ac6801d7b77c501463\"><\/div><\/div><div>\n<p>Every day, shoppers from Etsy&#8217;s community of nearly 90M buyers visit our marketplace to search for unique, handmade, and vintage items. But with over 100 million listings, how do we help each buyer find exactly what they&#8217;re looking for? Traditional search and recommendation systems often fall short of capturing the nuanced interests that make each Etsy buyer unique, from specific styles to aesthetic preferences. At Etsy, understanding our buyers&#8217; interests is central to delivering engaging, personalized experiences. <\/p>\n<p>Recently, we explored enhancing our personalization by leveraging large language models (LLMs) to create detailed buyer profiles based on buyers\u2019 browsing and purchasing behaviors. We strive towards privacy by design and build these exploratory models with buyer privacy in mind. While this work is still in experimentation, we\u2019re excited to share our early efforts and what we\u2019ve learned.<\/p><div id=\"dev95-3954853828\" class=\"dev95- dev95-entity-placement\"><center>\r\n<script>\r\n  atOptions = {\r\n    'key' : '4ba6b6513c00e0ba76511f798ae56401',\r\n    'format' : 'iframe',\r\n    'height' : 50,\r\n    'width' : 320,\r\n    'params' : {}\r\n  };\r\n<\/script>\r\n<script src=\"https:\/\/www.highrevenueformat.com\/4ba6b6513c00e0ba76511f798ae56401\/invoke.js\"><\/script>\r\n\t<\/center><\/div>\n<h1>Understanding Buyer Profiles<\/h1>\n<p>Buyer profiles anonymously capture nuanced interests for each user, like preferred styles and product categories. For example, do they tend to prefer minimalist styles? Do they mostly shop for home decor, or jewelry? They also offer insight into specific shopping missions a buyer may be on, like finding the perfect fall sweater. These profiles help us better understand our users, making the Etsy experience feel tailored to each individual buyer.<\/p>\n<h1>Technical Implementation<\/h1>\n<p>Our process begins by retrieving user activity data and listing information from internal sources like our feature store and BigQuery. This includes users\u2019 recent searches, item views, purchases, and favorites. <\/p>\n<figure>\n<img data-recalc-dims=\"1\" decoding=\"async\" alt=\"\" src=\"https:\/\/i0.wp.com\/i.etsystatic.com\/inv\/d90c38\/7167110206\/inv_fullxfull.7167110206_rvsp064p.jpg?w=1280&#038;ssl=1\"><br \/>\n<\/figure>\n<p>Once we have the users\u2019 session data with interactions, we then prompt the LLM to interpret this data and generate structured buyer profiles.<\/p>\n<figure>\n<img data-recalc-dims=\"1\" decoding=\"async\" alt=\"\" src=\"https:\/\/i0.wp.com\/i.etsystatic.com\/inv\/ec98a6\/7167223046\/inv_fullxfull.7167223046_f1d4hdhg.jpg?w=1280&#038;ssl=1\"><figcaption>Buyer profile data structure<\/figcaption><\/figure>\n<p>In the case that the LLM does not have enough supporting data to infer a user&#8217;s categorical interests (i.e. these interests would also not be obvious to a human looking at this user&#8217;s interactions), these fields and the confidence hashmap can be empty. The LLM will always include an explanation and any observed interests in the explanation section of the buyer profile. <\/p>\n<figure>\n<img data-recalc-dims=\"1\" decoding=\"async\" alt=\"\" src=\"https:\/\/i0.wp.com\/i.etsystatic.com\/inv\/3289ad\/7167112652\/inv_fullxfull.7167112652_4veuwows.jpg?w=1280&#038;ssl=1\"><br \/>\n<\/figure>\n<h1>Bringing Personalization to Scale<\/h1>\n<p>Initially, using this method to generate profiles for our roughly 90 million buyers would have been exceedingly costly and taken weeks to update.<br \/>\nWe optimized this by:<\/p>\n<ul>\n<li>Shifting our listings data source from API endpoints to BigQuery tables that are clustered and partitioned for efficient querying.<\/li>\n<li>Decreasing the volume of input tokens. Initially, we were including about 2 years of session data. Now, we\u2019ve reduced this to just the last 9 months. The 9 month timeframe also allowed us to lighten the prompt corrections (and cached input tokens) by reducing the weight of holiday shopping. <\/li>\n<li>Increasing LLM and BigQuery batch sizes for data processing.<\/li>\n<li>Introducing parallel processing with managed concurrency to avoid request rate limits.<\/li>\n<li>Scaling up computational resources for session data retrieval and LLM processing tasks.<\/li>\n<\/ul>\n<p>These improvements reduced buyer profile generation time dramatically \u2014 from 21 days down to 3 days for 10 million users.<br \/>\nCost management was also crucial. By adjusting the prompt to get high quality results with a smaller model, we significantly lowered the cost and made large-scale personalization economically feasible. Through these various cost management techniques, we were able to reduce the estimated cost by 94% per million users.<\/p>\n<p>Additionally, to scale and schedule buyer profile refreshes, we used Airflow as an orchestration tool. By batching and staggering tasks by user_id, we parallelize some tasks while avoiding too many concurrent requests on BigQuery &amp; OpenAI APIs (as visualized in this Directed Acyclic Graph).<\/p>\n<figure>\n<img data-recalc-dims=\"1\" decoding=\"async\" alt=\"\" src=\"https:\/\/i0.wp.com\/i.etsystatic.com\/inv\/4c215d\/7215106915\/inv_fullxfull.7215106915_9syjqngx.jpg?w=1280&#038;ssl=1\"><figcaption>A snapshot of a running buyer profile DAG with staggered batching<\/figcaption><\/figure>\n<h1>Experiments and Applications<\/h1>\n<p>With these powerful insights in hand, we\u2019ve started to explore several key applications of buyer profiles that would make the Etsy search experience feel unique to each shopper.<\/p>\n<h2>Query Rewriting<\/h2>\n<p>Query rewriting is the process of transforming a user\u2019s submitted search query on the backend to better represent their intent to the underlying search systems. We already use query rewriting for different use cases at Etsy. It can correct spelling, add specifics to ambiguous queries, adjust niche terminology, or complete incomplete queries (where the user accidentally pressed enter before they finished writing their query).<\/p>\n<p>To further personalize Etsy\u2019s search experience, we explored enriching search queries with predicted interests from buyer profiles. For instance, a simple query like &#8220;cool posters&#8221; becomes &#8220;cool posters + hippie|boho|vintage|nature,&#8221; significantly boosting relevance.<\/p>\n<h3>Search results without personalization<\/h3>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" alt=\"\" src=\"https:\/\/i0.wp.com\/i.etsystatic.com\/inv\/8042d1\/7215156101\/inv_fullxfull.7215156101_i7tyi5gw.jpg?w=1280&#038;ssl=1\"> <\/p>\n<h3>Search results with personalization using example buyer profile<\/h3>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" alt=\"\" src=\"https:\/\/i0.wp.com\/i.etsystatic.com\/inv\/57f38c\/7215157261\/inv_fullxfull.7215157261_3eozeu9r.jpg?w=1280&#038;ssl=1\"> <\/p>\n<h2>Refinement Pills<\/h2>\n<p>On the Etsy website, \u201crefinement pills\u201d work as an interactive query reformulation. When a user clicks on an option, the path will be prefixed to the user\u2019s query and a new search will be executed. A mix of different types of refinement pills are generated: single pills (just one word) and grouped pills (a dropdown of options). With buyer profiles, we can present high-confidence buyer interests as clickable filters or refinement pills in the search interface, enabling users to effortlessly refine their searches based on predicted preferences. <\/p>\n<h3>Refinement pills without personalization<\/h3>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" alt=\"\" src=\"https:\/\/i0.wp.com\/i.etsystatic.com\/inv\/0065d9\/7215158991\/inv_fullxfull.7215158991_4vahuj39.jpg?w=1280&#038;ssl=1\"> <\/p>\n<h3>Refinement pills with personalization using example buyer profile<\/h3>\n<p><img data-recalc-dims=\"1\" decoding=\"async\" alt=\"\" src=\"https:\/\/i0.wp.com\/i.etsystatic.com\/inv\/c6eec8\/7167281650\/inv_fullxfull.7167281650_ovxaywx2.jpg?w=1280&#038;ssl=1\"><\/p>\n<p>If a user clicks on a \u201cNature\u201d refinement pill as in the example above, the search query &#8220;study lamps&#8221; would be reformulated to &#8220;Nature study lamps&#8221; and the new, more specific search will be executed.<\/p>\n<h1>Measuring Success<\/h1>\n<h2>Validating Profile Accuracy<\/h2>\n<p>As we work to roll out buyer profile-based personalization, understanding the accuracy of our predictions is crucial. Through systematic experimentation, we&#8217;re working to establish how well our LLM-generated profiles reflect actual user interests and shopping behaviors.<\/p>\n<h2>Key Accuracy Metrics<\/h2>\n<ul>\n<li>Click-through Rate (CTR) Lift: Comparing CTR on personalized search results versus baseline helps us quantify immediate engagement improvements<\/li>\n<li>Conversion Rate Impact: Tracking purchase rates from personalized searches reveals whether we&#8217;re surfacing items users actually want to buy<\/li>\n<li>Refinement Pill Engagement: Monitoring clicks on suggested refinement pills helps measure how well we&#8217;ve predicted user interests<\/li>\n<li>Search Query Reformulation Success: Analyzing subsequent user actions after query rewriting indicates whether our enrichments align with user intent<\/li>\n<\/ul>\n<h2>Profile Refresh Strategy<\/h2>\n<p>We&#8217;ll work to maintain profile accuracy through several methods:<\/p>\n<ul>\n<li>Dynamically refreshing timing based on user activity levels. These include the number and frequency of recent interactions. For example, does the user click on the refinement pills proposed? Are the personalized items viewed?<\/li>\n<li>Detecting interest drift when search patterns change significantly<\/li>\n<li>Taking into account seasonal considerations, such as the winter holiday shopping season<\/li>\n<li>Monitoring performance degradation signals when personalization effectiveness drops below thresholds of engagement listed above<\/li>\n<\/ul>\n<h1>Future Work<\/h1>\n<h2>The Cold Start User Problem<\/h2>\n<p>To extend this personalized experience to new users who lack extensive session data, we experimented with &#8220;inheritance profiles&#8221; using collaborative filtering. By matching early interaction signals (like brief session data or initial search terms) to existing profiles of similar users, we could predict interests for new users earlier in their relationship with Etsy.<\/p>\n<p>This user-based collaborative filtering approach would allow us to have profiles for as many users as possible, even those with minimal behavioral data. Below is a diagram for a sample new user who might inherit aspects of a similar profile as the one above, based on just a few searches.<\/p>\n<figure>\n<img data-recalc-dims=\"1\" decoding=\"async\" alt=\"\" src=\"https:\/\/i0.wp.com\/i.etsystatic.com\/inv\/ef8f8c\/7215162515\/inv_fullxfull.7215162515_o2x0riwx.jpg?w=1280&#038;ssl=1\"><br \/>\n<\/figure>\n<h1>Conclusion<\/h1>\n<p>As we continue to refine these profiles and expand their applications across the Etsy marketplace, we&#8217;re excited about the possibilities. We hope to create more intuitive search experiences and surface hidden gems aligned with each buyer&#8217;s unique tastes. <\/p>\n<p>With nearly 90 million active buyers and over 100 million listings, the impact of personalization at Etsy&#8217;s scale is huge. Every improvement we make impacts millions of shopping journeys, helping buyers discover that perfect vintage find, custom wedding gift, or miniature dragon they didn&#8217;t even know they were looking for. <\/p>\n<h1>Acknowledgements<\/h1>\n<p>Huge thanks to Orson Adams, David Blincoe, Jugal Gala, Davis Kim, Haoming Chen, Yinlin Fu, Julia Zhou, and the entire Search Mission Understanding team for their invaluable support and guidance.<\/p>\n<\/div>\n<div class=\"pvc_clear\"><\/div>\n<p id=\"pvc_stats_1476\" class=\"pvc_stats total_only\" data-element-id=\"1476\" style=\"\"><i class=\"pvc-stats-icon medium\" aria-hidden=\"true\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" version=\"1.0\" viewbox=\"0 0 502 315\" preserveaspectratio=\"xMidYMid meet\"><g transform=\"translate(0,332) scale(0.1,-0.1)\" fill=\"\" stroke=\"none\"><path d=\"M2394 3279 l-29 -30 -3 -207 c-2 -182 0 -211 15 -242 39 -76 157 -76 196 0 15 31 17 60 15 243 l-3 209 -33 29 c-26 23 -41 29 -80 29 -41 0 -53 -5 -78 -31z\"\/><path d=\"M3085 3251 c-45 -19 -58 -50 -96 -229 -47 -217 -49 -260 -13 -295 52 -53 146 -42 177 20 16 31 87 366 87 410 0 70 -86 122 -155 94z\"\/><path d=\"M1751 3234 c-13 -9 -29 -31 -37 -50 -12 -29 -10 -49 21 -204 19 -94 39 -189 45 -210 14 -50 54 -80 110 -80 34 0 48 6 76 34 21 21 34 44 34 59 0 14 -18 113 -40 219 -37 178 -43 195 -70 221 -36 32 -101 37 -139 11z\"\/><path d=\"M1163 3073 c-36 -7 -73 -59 -73 -102 0 -56 133 -378 171 -413 34 -32 83 -37 129 -13 70 36 67 87 -16 290 -86 209 -89 214 -129 231 -35 14 -42 15 -82 7z\"\/><path d=\"M3689 3066 c-15 -9 -33 -30 -42 -48 -48 -103 -147 -355 -147 -375 0 -98 131 -148 192 -74 13 15 57 108 97 206 80 196 84 226 37 273 -30 30 -99 39 -137 18z\"\/><path d=\"M583 2784 c-38 -19 -67 -74 -58 -113 9 -42 211 -354 242 -373 16 -10 45 -18 66 -18 51 0 107 52 107 100 0 39 -1 41 -124 234 -80 126 -108 162 -133 173 -41 17 -61 16 -100 -3z\"\/><path d=\"M4250 2784 c-14 -9 -74 -91 -133 -183 -95 -150 -107 -173 -107 -213 0 -55 33 -94 87 -104 67 -13 90 8 211 198 130 202 137 225 78 284 -27 27 -42 34 -72 34 -22 0 -50 -8 -64 -16z\"\/><path d=\"M2275 2693 c-553 -48 -1095 -270 -1585 -649 -135 -104 -459 -423 -483 -476 -23 -49 -22 -139 2 -186 73 -142 361 -457 571 -626 285 -228 642 -407 990 -497 242 -63 336 -73 660 -74 310 0 370 5 595 52 535 111 1045 392 1455 803 122 121 250 273 275 326 19 41 19 137 0 174 -41 79 -309 363 -465 492 -447 370 -946 591 -1479 653 -113 14 -422 18 -536 8z m395 -428 c171 -34 330 -124 456 -258 112 -119 167 -219 211 -378 27 -96 24 -300 -5 -401 -72 -255 -236 -447 -474 -557 -132 -62 -201 -76 -368 -76 -167 0 -236 14 -368 76 -213 98 -373 271 -451 485 -162 444 86 934 547 1084 153 49 292 57 452 25z m909 -232 c222 -123 408 -262 593 -441 76 -74 138 -139 138 -144 0 -16 -233 -242 -330 -319 -155 -123 -309 -223 -461 -299 l-81 -41 32 46 c18 26 49 83 70 128 143 306 141 649 -6 957 -25 52 -61 116 -79 142 l-34 47 45 -20 c26 -10 76 -36 113 -56z m-2057 25 c-40 -58 -105 -190 -130 -263 -110 -324 -59 -707 132 -981 25 -35 42 -64 37 -64 -19 0 -241 119 -326 174 -188 122 -406 314 -532 468 l-58 71 108 103 c185 178 428 349 672 473 66 33 121 60 123 61 2 0 -10 -19 -26 -42z\"\/><path d=\"M2375 1950 c-198 -44 -350 -190 -395 -379 -18 -76 -8 -221 19 -290 114 -284 457 -406 731 -260 98 52 188 154 231 260 27 69 37 214 19 290 -38 163 -166 304 -326 360 -67 23 -215 33 -279 19z\"\/><\/g><\/svg><\/i> <img data-recalc-dims=\"1\" loading=\"lazy\" decoding=\"async\" width=\"16\" height=\"16\" alt=\"Loading\" src=\"https:\/\/i0.wp.com\/dev95.site\/wp-content\/plugins\/page-views-count\/ajax-loader-2x.gif?resize=16%2C16&#038;ssl=1\" border=\"0\" \/><\/p>\n<div class=\"pvc_clear\"><\/div>","protected":false},"excerpt":{"rendered":"<p>Every day, shoppers from Etsy&#8217;s community of nearly 90M buyers visit our marketplace to search for unique, handmade, and vintage items. But with over 100 million listings, how do we help each buyer find exactly what they&#8217;re looking for? Traditional<\/p>\n<div class=\"hosteria-entry-more\"><a href=\"https:\/\/dev95.site\/ar\/building-etsy-buyer-profiles-with-llms\/\" class=\"no-underline font-light  group-hover:text-primary-800 dark:group-hover:text-primary-300 py-1\">Read more &gt;&gt;&gt;<\/a><\/div>\n<div class=\"pvc_clear\"><\/div>\n<p id=\"pvc_stats_1476\" class=\"pvc_stats total_only\" data-element-id=\"1476\" style=\"\"><i class=\"pvc-stats-icon medium\" aria-hidden=\"true\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" version=\"1.0\" viewbox=\"0 0 502 315\" preserveaspectratio=\"xMidYMid meet\"><g transform=\"translate(0,332) scale(0.1,-0.1)\" fill=\"\" stroke=\"none\"><path d=\"M2394 3279 l-29 -30 -3 -207 c-2 -182 0 -211 15 -242 39 -76 157 -76 196 0 15 31 17 60 15 243 l-3 209 -33 29 c-26 23 -41 29 -80 29 -41 0 -53 -5 -78 -31z\"\/><path d=\"M3085 3251 c-45 -19 -58 -50 -96 -229 -47 -217 -49 -260 -13 -295 52 -53 146 -42 177 20 16 31 87 366 87 410 0 70 -86 122 -155 94z\"\/><path d=\"M1751 3234 c-13 -9 -29 -31 -37 -50 -12 -29 -10 -49 21 -204 19 -94 39 -189 45 -210 14 -50 54 -80 110 -80 34 0 48 6 76 34 21 21 34 44 34 59 0 14 -18 113 -40 219 -37 178 -43 195 -70 221 -36 32 -101 37 -139 11z\"\/><path d=\"M1163 3073 c-36 -7 -73 -59 -73 -102 0 -56 133 -378 171 -413 34 -32 83 -37 129 -13 70 36 67 87 -16 290 -86 209 -89 214 -129 231 -35 14 -42 15 -82 7z\"\/><path d=\"M3689 3066 c-15 -9 -33 -30 -42 -48 -48 -103 -147 -355 -147 -375 0 -98 131 -148 192 -74 13 15 57 108 97 206 80 196 84 226 37 273 -30 30 -99 39 -137 18z\"\/><path d=\"M583 2784 c-38 -19 -67 -74 -58 -113 9 -42 211 -354 242 -373 16 -10 45 -18 66 -18 51 0 107 52 107 100 0 39 -1 41 -124 234 -80 126 -108 162 -133 173 -41 17 -61 16 -100 -3z\"\/><path d=\"M4250 2784 c-14 -9 -74 -91 -133 -183 -95 -150 -107 -173 -107 -213 0 -55 33 -94 87 -104 67 -13 90 8 211 198 130 202 137 225 78 284 -27 27 -42 34 -72 34 -22 0 -50 -8 -64 -16z\"\/><path d=\"M2275 2693 c-553 -48 -1095 -270 -1585 -649 -135 -104 -459 -423 -483 -476 -23 -49 -22 -139 2 -186 73 -142 361 -457 571 -626 285 -228 642 -407 990 -497 242 -63 336 -73 660 -74 310 0 370 5 595 52 535 111 1045 392 1455 803 122 121 250 273 275 326 19 41 19 137 0 174 -41 79 -309 363 -465 492 -447 370 -946 591 -1479 653 -113 14 -422 18 -536 8z m395 -428 c171 -34 330 -124 456 -258 112 -119 167 -219 211 -378 27 -96 24 -300 -5 -401 -72 -255 -236 -447 -474 -557 -132 -62 -201 -76 -368 -76 -167 0 -236 14 -368 76 -213 98 -373 271 -451 485 -162 444 86 934 547 1084 153 49 292 57 452 25z m909 -232 c222 -123 408 -262 593 -441 76 -74 138 -139 138 -144 0 -16 -233 -242 -330 -319 -155 -123 -309 -223 -461 -299 l-81 -41 32 46 c18 26 49 83 70 128 143 306 141 649 -6 957 -25 52 -61 116 -79 142 l-34 47 45 -20 c26 -10 76 -36 113 -56z m-2057 25 c-40 -58 -105 -190 -130 -263 -110 -324 -59 -707 132 -981 25 -35 42 -64 37 -64 -19 0 -241 119 -326 174 -188 122 -406 314 -532 468 l-58 71 108 103 c185 178 428 349 672 473 66 33 121 60 123 61 2 0 -10 -19 -26 -42z\"\/><path d=\"M2375 1950 c-198 -44 -350 -190 -395 -379 -18 -76 -8 -221 19 -290 114 -284 457 -406 731 -260 98 52 188 154 231 260 27 69 37 214 19 290 -38 163 -166 304 -326 360 -67 23 -215 33 -279 19z\"\/><\/g><\/svg><\/i> <img loading=\"lazy\" decoding=\"async\" width=\"16\" height=\"16\" alt=\"Loading\" src=\"https:\/\/dev95.site\/wp-content\/plugins\/page-views-count\/ajax-loader-2x.gif\" border=\"0\" \/><\/p>\n<div class=\"pvc_clear\"><\/div>","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"fp_fajr_begins":"","fp_fajr_iqamah":"","fp_dhuhr_begins":"","fp_dhuhr_iqamah":"","fp_asr_begins":"","fp_asr_iqamah":"","fp_maghrib_begins":"","fp_maghrib_iqamah":"","fp_isha_begins":"","fp_isha_iqamah":"","fp_midnight":"","fp_midnight_name":"","fp_sunrise":"","fp_single_prayer_begins_title":"","fp_single_prayer_iqamah_title":"","fp_prayer_times_for_today":"","fp_hijra_date":"","fp_fajr_name":"","fp_dhuhr_name":"","fp_asr_name":"","fp_maghrib_name":"","fp_isha_name":"","fp_sunrise_name":"","fp_currentDate":"","fp_current_time":"","fp_current_title":"","fp_current_location":"","fp_masjid_name":"","fp_prayer_title":"","fp_next_prayer_iqamah_time":"","fp_next_prayer_iqamah_title":"","fp_next_prayer_begins_time":"","fp_next_prayer_begins_title":"","fp_next_prayer_title":"","_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[37],"tags":[],"class_list":["post-1476","post","type-post","status-publish","format-standard","hentry","category-posts"],"a3_pvc":{"activated":true,"total_views":0,"today_views":0},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.5 - 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