Close Menu
    Trending
    • STOP Building Useless ML Projects – What Actually Works
    • Credit Risk Scoring for BNPL Customers at Bati Bank | by Sumeya sirmula | Jul, 2025
    • The New Career Crisis: AI Is Breaking the Entry-Level Path for Gen Z
    • Musk’s X appoints ‘king of virality’ in bid to boost growth
    • Why Entrepreneurs Should Stop Obsessing Over Growth
    • Implementing IBCS rules in Power BI
    • What comes next for AI copyright lawsuits?
    • Why PDF Extraction Still Feels LikeHack
    AIBS News
    • Home
    • Artificial Intelligence
    • Machine Learning
    • AI Technology
    • Data Science
    • More
      • Technology
      • Business
    AIBS News
    Home»Machine Learning»Does High Click-through Rate Lead to High Conversion Rate? | by Lucy (Jingyu) Zou | Expedia Group Technology | May, 2025
    Machine Learning

    Does High Click-through Rate Lead to High Conversion Rate? | by Lucy (Jingyu) Zou | Expedia Group Technology | May, 2025

    Team_AIBS NewsBy Team_AIBS NewsMay 6, 2025No Comments9 Mins Read
    Share Facebook Twitter Pinterest LinkedIn Tumblr Reddit Telegram Email
    Share
    Facebook Twitter LinkedIn Pinterest Email


    An iterative method to suggestion programs at Expedia Group

    People relax by a pool with a vast view looking over a calm ocean.
    Photograph by Sasha Kaunas on Unsplash

    Within the quickly evolving panorama of on-line journey enterprise, delivering exact and personalised property suggestions has develop into a cornerstone for enhancing consumer expertise and engagement. This weblog presents a complete overview of the evolution of property suggestion programs at Expedia Group™, illustrating our classes realized all through mannequin growth and iterative journey from primary similarity fashions to superior personalised methods. Particularly, we concentrate on how we optimize goal variable primarily based on enterprise aims and on-line analysis observations. This weblog addresses the query of whether or not excessive click-through fee (CTR) at all times correlates positively with excessive conversion fee (CVR). Moreover, we define our future route in leveraging rising applied sciences and methodologies to additional advance suggestion with extra personalization and accuracy. Our work showcase sensible purposes and developments in journey suggestion programs, providing classes realized and finest practices that may be utilized throughout the trade.

    Suggestion system has been enjoying a essential function in enhancing the e-commerce corporations revenue¹’², transaction³, and enhancing the consumer experiences by offering personalised suggestions primarily based on consumer preferences⁴’⁵. Suggestion fashions have revolutionized the shoppers’ on-line buying experiences with extra favorable product suggestions in response to every buyer’s interplay history⁶’⁷, product demand estimation⁸, and consumer’s demographic profile⁹’¹⁰.

    Expedia Group is a expertise firm within the on-line journey market. The corporate leverages totally different suggestion programs to not solely
    present beneficial insights to enterprise companions by way of pricing methods optimization, occupancy charges enchancment, and visitor satisfaction enhancement¹¹’¹², but additionally empower vacationers’ buying experiences by means of personalised suggestions¹³’¹⁴’¹⁵ starting from flights, resorts, to trip leases, automobile leases, and actions.

    Numerous analysis have been executed within the suggestion programs with numerous fashions and strategies, starting from collaborative filtering¹⁶’¹⁷’¹⁸, matrix factorization¹⁹’²⁰, factorization machine²¹’²², tree-based learning-to-rank²³’²⁴’²⁵, to deep neural community ranking²⁶’²⁷ and deep reinforcement studying primarily based ranking²⁸. Lots of them are constructed on prime of earlier works with new concepts and rising applied sciences corresponding to DeepFM²⁹, BERT4Rec³⁰, and LLM assisted suggestion systems³¹.

    Whereas the above suggestion fashions are crucial and confirmed to enhance the advice programs’ efficiency, the iterative journey of property suggestion programs at Expedia Group are usually not tied strictly to those mannequin iterations. As an alternative, our journey comes intently with the progressive understanding of the shoppers’ intent and interactions on our web sites and apps, and the way these implicit relevance alerts might be leveraged to enhance the vacationers’ buying expertise with higher CVR and better income.

    Subsequent, we are going to stroll by means of the issue assertion, how the identical downside has advanced primarily based on the insights from our vacationers’ interplay information, and the way totally different fashions are proposed and utilized to unravel the reshaped downside in every stage. Determine 1 under outlines the general iterative journey. IM refers to Interplay Matrix, and pAction refers to a multi-task mannequin framework. Each of those strategies will likely be launched afterward within the weblog. We consider our work contributes to the Medium group by providing finest practices in using state-of-the-art suggestion fashions to unravel actual enterprise issues.

    Iteration journey of Expedia Group similar property models
    Determine 1: Iteration journey of EG related property fashions

    When folks speak about resort suggestion at Expedia Group, a lot of them could consider the traditional suggestion downside: the vacationers sort within the vacation spot in thoughts, hit the search button, and all of the related resorts related to the designated vacation spot could be retrieved and sorted within the search engine outcome web page (SERP web page).
    Though that’s some of the essential placements on our web sites, there are extra suggestion capabilities in Expedia Group.

    Related resort suggestion downside

    Suppose a traveler A is exploring locations to remain for an upcoming trip. A clicked on a resort, and wished to search out related out there properties. A both wants to return to the SERP web page and additional slim down his or her search, or begin one other search if the earlier web page is closed, inflicting further efforts. Related resort suggestion functionality is designed to unravel this resort buying expertise ache level by using a carousel placement contained in the resort particulars web page. With this functionality, vacationers may simply scroll down on the resort particulars web page and discover related properties that go well with their wants within the suggestion carousel with out going again to the SERP web page.

    Example similar property recommendation carousel
    Instance related property suggestion carousel

    Preliminary effort

    Because the related resort suggestion carousel goals to encourage vacationers with related resorts to the at the moment considered one, we can’t use the advice mannequin designed for the SERP web page. The baseline mannequin developed to serve the same property use case makes use of item-to-item click on interactions generated from the Expedia Group traveler interplay dataset. We experimented with item-to-item, user-to-item Interplay Matrix (IM) in addition to matrix factorization techniques³².
    Fashions are evaluated on a held out take a look at set on a session-level, the place rating metrics (NDCG, precision, and recall) had been calculated to know how properly the fashions carry out in predicting the following clicked property.
    The item-to-item IM obtained the perfect efficiency from our offline evaluation, which is generated from a matrix through which each rows and columns signify totally different resorts, and the values of the matrix signify an aggregation of the co-clicks of the row and column resorts throughout traveler classes. For illustration functions, we’ve proven an instance snapshot of the item-to-item IM in Determine 2.

    Snapshot of the item-to-item Interaction Matrix (IM) Example
    Determine 2: Merchandise-to-item IM Toy Instance

    Does excessive CTR result in excessive CVR?

    As soon as a mannequin will get deployed within the manufacturing setting, our crew retains monitor of key enterprise metrics for every placement, together with CTR and CVR. By on-line metric evaluation, we discovered that the correlation coefficient between on-line CTR and CVR is barely unfavorable over a time frame. This means that increased CTR doesn’t essentially result in increased CVR within the downstream funnel. We realized that solely optimizing on clicks offline may affect conversions negatively in the long term. If a mannequin optimizes on consumer clicks with out bookings, that will point out that traveler usually must spend extra time exploring earlier than discovering the perfect property.
    Within the journey trade, Airbnb has discovered that the correlation coefficients between CTR and cancellation fee is optimistic, stating that solely rising clicks with out bookings will not be splendid for the business³³. With this studying in thoughts, we famous that future property suggestions ought to not concentrate on clicks solely, but additionally on downstream bookings in addition to consumer engagements.

    Aiming in the direction of reserving

    To drive downstream conversion, our subsequent iteration of the same property mannequin focuses on optimizing properties reserving for every consumer session by together with further context options corresponding to consumer location and search filters. We experimented with totally different CART-building (Classification and Regression Tree) methods (XGBoost and LightGBM) together with totally different loss functions³⁴’³⁵.
    In the end, we proceeded with deploying the contextualized mannequin, which was the perfect performing lightGBM ranker with pairwise loss. We noticed important enchancment in conversions in each on-line A/B testing and offline metrics with a raise of 4.2% for ndcg@5 and 12% for ndcg@20, respectively.

    LightGBM tree growth
    LightGBM tree progress from https://lightgbm.readthedocs.io/

    Worth added from historic clicks

    With purpose of offering extra personalised suggestions, the crew acknowledged that property clicks comprise beneficial data of traveler desire of their journey and shouldn’t be undermined. Customers’ historic clicks present beneficial insights on vacationers’ preferences for property sorts, places, facilities and way more.
    Recognizing that property clicks reveal beneficial traveler preferences, we developed a Hyper-personalized mannequin by incorporating historic clicks into the ETL pipeline. This allowed us to engineer options representing preferences for property location, star ranking, and visitor ranking. We additionally used property embedding to supply data on reputation, facilities, and ratings¹⁴. The Hyper-personalized mannequin achieved a 33% uplift in ndcg@5 in comparison with the contextualized mannequin.

    Labeling past e book sign

    After preliminary findings on the connection between CTR and CVR, the crew tracked numerous on-line metrics to know long-term conversion drivers. Evaluation revealed that lengthy clicks correlate positively with bookings. A professional lengthy click on happens when a traveler additional engages with the property particulars web page after the preliminary click on, corresponding to viewing opinions, maps, or facilities. This impressed the design of our multi-task mannequin, pAction, which optimizes for clicks, lengthy clicks, and bookings to seize the complete traveler journey and study consumer preferences.
    Multi-task fashions leverage shared representations to study data throughout totally different duties and have confirmed to achieve success in net search rating, show advertising³⁶’³⁷’³⁸, and many others. with a concentrate on totally different duties’ aims. The brand new pAction mannequin can be extra strong in low-traffic areas, addressing label sparsity by together with much less fashionable properties with latest click on site visitors. Optimizing consumer engagement throughout totally different journey phases attracted adoption from numerous Expedia Group groups, together with the homepage, reserving cancellation web page, and Journeys crew.

    Though nice progress has been made in iterating the resort suggestion system in Expedia Group, there are nonetheless a lot of room left to additional enhance its efficiency to raised serve numerous vacationers’ wants.

    Place bias correction

    Our fashions are skilled on datasets constructed from traveler interactions, which is thought to introduce bias because of closed loop consumer suggestions. This ends in increased ranked gadgets getting extra consideration and thus extra downstream bookings³⁹. De-biasing the place affect will assist the mannequin study the true relationship between the vacationers’ interactions and the beneficial gadgets to additional enhance their buying expertise.

    Leveraging Generative AI

    LLM fashions have demonstrated spectacular efficiency throughout totally different domains together with recommendation⁴⁰. We will leverage GenAI to raised perceive the vacationers’ wants and preferences and enhance the machine studying mannequin’s efficiency. Current analysis has proven that constructing generative retrieval fashions for sequential suggestions outperforms conventional state-of-the-art suggestion models⁴¹. We proceed to discover GenAI powered suggestions to additional enhance buyer journey and satisfaction.

    Thanks for studying! I’d like to supply particular because of Jianjun Yuan, Yonghai Li for his or her enter on this weblog, Expedia Group Suggestions crew and Mani Najmabadi for content material suggestions, and Christian Sommeregger, Jack Pennington on their work for the pAction mannequin.

    Learn about life at Expedia Group



    Source link

    Share. Facebook Twitter Pinterest LinkedIn Tumblr Email
    Previous ArticleThese Sleep Earbuds Can be Perfect for the Office, Now 25% Off
    Next Article Why the humanoid workforce is running late
    Team_AIBS News
    • Website

    Related Posts

    Machine Learning

    Credit Risk Scoring for BNPL Customers at Bati Bank | by Sumeya sirmula | Jul, 2025

    July 1, 2025
    Machine Learning

    Why PDF Extraction Still Feels LikeHack

    July 1, 2025
    Machine Learning

    🚗 Predicting Car Purchase Amounts with Neural Networks in Keras (with Code & Dataset) | by Smruti Ranjan Nayak | Jul, 2025

    July 1, 2025
    Add A Comment
    Leave A Reply Cancel Reply

    Top Posts

    STOP Building Useless ML Projects – What Actually Works

    July 1, 2025

    I Tried Buying a Car Through Amazon: Here Are the Pros, Cons

    December 10, 2024

    Amazon and eBay to pay ‘fair share’ for e-waste recycling

    December 10, 2024

    Artificial Intelligence Concerns & Predictions For 2025

    December 10, 2024

    Barbara Corcoran: Entrepreneurs Must ‘Embrace Change’

    December 10, 2024
    Categories
    • AI Technology
    • Artificial Intelligence
    • Business
    • Data Science
    • Machine Learning
    • Technology
    Most Popular

    IEEE’s role in combatting climate change at COP29

    March 14, 2025

    EHRs: The Hidden Distraction in Your Doctor’s Office

    March 30, 2025

    Rebuilding Earth | by Griffin | Apr, 2025

    April 23, 2025
    Our Picks

    STOP Building Useless ML Projects – What Actually Works

    July 1, 2025

    Credit Risk Scoring for BNPL Customers at Bati Bank | by Sumeya sirmula | Jul, 2025

    July 1, 2025

    The New Career Crisis: AI Is Breaking the Entry-Level Path for Gen Z

    July 1, 2025
    Categories
    • AI Technology
    • Artificial Intelligence
    • Business
    • Data Science
    • Machine Learning
    • Technology
    • Privacy Policy
    • Disclaimer
    • Terms and Conditions
    • About us
    • Contact us
    Copyright © 2024 Aibsnews.comAll Rights Reserved.

    Type above and press Enter to search. Press Esc to cancel.