Measuring the User Satisfaction in a Recommendation Interface with Multiple Carousels

• URL: http://arxiv.org/abs/2105.07062v1
• Date: Fri, 14 May 2021 20:33:51 GMT
• Title: Measuring the User Satisfaction in a Recommendation Interface with Multiple Carousels
• Authors: Nicol\`o Felicioni, Maurizio Ferrari Dacrema, Paolo Cremonesi
• Abstract summary: It is common for video-on-demand and music streaming services to adopt a user interface composed of several recommendation lists. We propose a two-dimensional evaluation protocol for a carousel setting that will measure the quality of a recommendation carousel.
• Score: 7.8851236034886645
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It is common for video-on-demand and music streaming services to adopt a user interface composed of several recommendation lists, i.e. widgets or swipeable carousels, each generated according to a specific criterion or algorithm (e.g. most recent, top popular, recommended for you, editors' choice, etc.). Selecting the appropriate combination of carousel has significant impact on user satisfaction. A crucial aspect of this user interface is that to measure the relevance a new carousel for the user it is not sufficient to account solely for its individual quality. Instead, it should be considered that other carousels will already be present in the interface. This is not considered by traditional evaluation protocols for recommenders systems, in which each carousel is evaluated in isolation, regardless of (i) which other carousels are displayed to the user and (ii) the relative position of the carousel with respect to other carousels. Hence, we propose a two-dimensional evaluation protocol for a carousel setting that will measure the quality of a recommendation carousel based on how much it improves upon the quality of an already available set of carousels. Our evaluation protocol takes into account also the position bias, i.e. users do not explore the carousels sequentially, but rather concentrate on the top-left corner of the screen. We report experiments on the movie domain and notice that under a carousel setting the definition of which criteria has to be preferred to generate a list of recommended items changes with respect to what is commonly understood.

Related papers

• Beyond Positive History: Re-ranking with List-level Hybrid Feedback [49.52149227298746]
  We propose Re-ranking with List-level Hybrid Feedback (dubbed RELIFE) It captures user's preferences and behavior patterns with three modules. Experiments show that RELIFE significantly outperforms SOTA re-ranking baselines.
  arXiv  Detail & Related papers  (2024-10-28T06:39:01Z)
• Leveraging User-Generated Reviews for Recommender Systems with Dynamic Headers [5.464901224450247]
  E-commerce platforms have a vast catalog of items to cater to their customers' shopping interests. Many models have been proposed in academic literature to generate and enhance the ranking and recall set of items in these carousels. This work proposes a novel approach to customize the header generation process of these carousels.
  arXiv  Detail & Related papers  (2024-09-11T21:18:21Z)
• Ada-Retrieval: An Adaptive Multi-Round Retrieval Paradigm for Sequential Recommendations [50.03560306423678]
  We propose Ada-Retrieval, an adaptive multi-round retrieval paradigm for recommender systems. Ada-Retrieval iteratively refines user representations to better capture potential candidates in the full item space.
  arXiv  Detail & Related papers  (2024-01-12T15:26:40Z)
• Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commerce [67.6615871959902]
  We study the watchlist functionality in e-commerce and introduce a novel watchlist recommendation task. Our goal is to prioritize which watchlist items the user should pay attention to next by predicting the next items the user will click. Our proposed recommendation model, Trans2D, is built on top of the Transformer architecture.
  arXiv  Detail & Related papers  (2021-10-18T10:02:15Z)
• On component interactions in two-stage recommender systems [82.38014314502861]
  Two-stage recommenders are used by many online platforms, including YouTube, LinkedIn, and Pinterest. We show that interactions between the ranker and the nominators substantially affect the overall performance. In particular, using a Mixture-of-Experts approach, we train the nominators to specialize on different subsets of the item pool.
  arXiv  Detail & Related papers  (2021-06-28T20:53:23Z)
• Set2setRank: Collaborative Set to Set Ranking for Implicit Feedback based Recommendation [59.183016033308014]
  In this paper, we explore the unique characteristics of the implicit feedback and propose Set2setRank framework for recommendation. Our proposed framework is model-agnostic and can be easily applied to most recommendation prediction approaches.
  arXiv  Detail & Related papers  (2021-05-16T08:06:22Z)
• A Methodology for the Offline Evaluation of Recommender Systems in a User Interface with Multiple Carousels [7.8851236034886645]
  Video-on-demand and music streaming services provide the user with a page consisting of several recommendation lists. Finding efficient strategies to select which carousels to display is an active research topic of great industrial interest. We propose an offline evaluation protocol for a carousel setting in which the recommendation quality of a model is measured by how much it improves upon that of an already available set of carousels.
  arXiv  Detail & Related papers  (2021-05-13T13:14:59Z)
• Dynamic-K Recommendation with Personalized Decision Boundary [41.70842736417849]
  We develop a dynamic-K recommendation task as a joint learning problem with both ranking and classification objectives. We extend two state-of-the-art ranking-based recommendation methods, i.e., BPRMF and HRM, to the corresponding dynamic-K versions. Our experimental results on two datasets show that the dynamic-K models are more effective than the original fixed-N recommendation methods.
  arXiv  Detail & Related papers  (2020-12-25T13:02:57Z)
• A Real-Time Whole Page Personalization Framework for E-Commerce [13.254747746069139]
  E-commerce platforms contain multiple carousels on their homepage. Items within a carousel may change dynamically based on sequential user actions. We present a scalable end-to-end production system to optimally rank item-carousels in real-time on the Walmart online grocery homepage.
  arXiv  Detail & Related papers  (2020-12-08T19:08:41Z)
• Carousel Personalization in Music Streaming Apps with Contextual Bandits [2.305378099875569]
  We model carousel personalization as a contextual multi-armed bandit problem with multiple plays, cascade-based updates and delayed batch feedback. We empirically show the effectiveness of our framework at capturing characteristics of real-world carousels by addressing a large-scale playlist recommendation task on a global music streaming mobile app.
  arXiv  Detail & Related papers  (2020-09-14T16:20:34Z)
• Controllable Multi-Interest Framework for Recommendation [64.30030600415654]
  We formalize the recommender system as a sequential recommendation problem. We propose a novel controllable multi-interest framework for the sequential recommendation, called ComiRec. Our framework has been successfully deployed on the offline Alibaba distributed cloud platform.
  arXiv  Detail & Related papers  (2020-05-19T10:18:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.