Engineering Serendipity through Recommendations of Items with Atypical Aspects

• URL: http://arxiv.org/abs/2505.23580v1
• Date: Thu, 29 May 2025 15:53:21 GMT
• Title: Engineering Serendipity through Recommendations of Items with Atypical Aspects
• Authors: Ramit Aditya, Razvan Bunescu, Smita Nannaware, Erfan Al-Hossami,
• Abstract summary: We introduce the new task of engineering serendipity through recommendations of items with atypical aspects.<n>We describe an LLM-based system pipeline that extracts atypical aspects from item reviews, then estimates and aggregates their user-specific utility.<n>We show that serendipity-based rankings generated by the system are highly correlated with ground truth rankings.
• Score: 0.5892638927736115
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A restaurant dinner or a hotel stay may lead to memorable experiences when guests encounter unexpected aspects that also match their interests. For example, an origami-making station in the waiting area of a restaurant may be both surprising and enjoyable for a customer who is passionate about paper crafts. Similarly, an exhibit of 18th century harpsichords would be atypical for a hotel lobby and likely pique the interest of a guest who has a passion for Baroque music. Motivated by this insight, in this paper we introduce the new task of engineering serendipity through recommendations of items with atypical aspects. We describe an LLM-based system pipeline that extracts atypical aspects from item reviews, then estimates and aggregates their user-specific utility in a measure of serendipity potential that is used to rerank a list of items recommended to the user. To facilitate system development and evaluation, we introduce a dataset of Yelp reviews that are manually annotated with atypical aspects and a dataset of artificially generated user profiles, together with crowdsourced annotations of user-aspect utility values. Furthermore, we introduce a custom procedure for dynamic selection of in-context learning examples, which is shown to improve LLM-based judgments of atypicality and utility. Experimental evaluations show that serendipity-based rankings generated by the system are highly correlated with ground truth rankings for which serendipity scores are computed from manual annotations of atypical aspects and their user-dependent utility. Overall, we hope that the new recommendation task and the associated system presented in this paper catalyze further research into recommendation approaches that go beyond accuracy in their pursuit of enhanced user satisfaction. The datasets and the code are made publicly available at https://github.com/ramituncc49er/ATARS .

Related papers

• Interactive Visualization Recommendation with Hier-SUCB [52.11209329270573]
  We propose an interactive personalized visualization recommendation (PVisRec) system that learns on user feedback from previous interactions.<n>For more interactive and accurate recommendations, we propose Hier-SUCB, a contextual semi-bandit in the PVisRec setting.
  arXiv  Detail & Related papers  (2025-02-05T17:14:45Z)
• How to Surprisingly Consider Recommendations? A Knowledge-Graph-based Approach Relying on Complex Network Metrics [0.2537383030441368]
  We propose a Knowledge Graph based recommender system by encoding user interactions on item catalogs. Our study explores whether network-level metrics on KGs can influence the degree of surprise in recommendations. We experimentally evaluate our approach on two datasets of LastFM listening histories and synthetic Netflix viewing profiles.
  arXiv  Detail & Related papers  (2024-05-14T09:38:44Z)
• Review-based Recommender Systems: A Survey of Approaches, Challenges and Future Perspectives [11.835903510784735]
  Review-based recommender systems have emerged as a significant sub-field in this domain.<n>We present a categorization of these systems and summarize the state-of-the-art methods, analyzing their unique features, effectiveness, and limitations.<n>We propose potential directions for future research, including the integration of multimodal data, multi-criteria rating information, and ethical considerations.
  arXiv  Detail & Related papers  (2024-05-09T05:45:18Z)
• Rethinking the Evaluation of Dialogue Systems: Effects of User Feedback on Crowdworkers and LLMs [57.16442740983528]
  In ad-hoc retrieval, evaluation relies heavily on user actions, including implicit feedback. The role of user feedback in annotators' assessment of turns in a conversational perception has been little studied. We focus on how the evaluation of task-oriented dialogue systems ( TDSs) is affected by considering user feedback, explicit or implicit, as provided through the follow-up utterance of a turn being evaluated.
  arXiv  Detail & Related papers  (2024-04-19T16:45:50Z)
• SBTRec- A Transformer Framework for Personalized Tour Recommendation Problem with Sentiment Analysis [6.753123338256321]
  SBTRec is a BERT-based Trajectory Recommendation with sentiment analysis. It learns users' preferences and satisfaction levels from reviews and comments about different POIs. It achieves an average F1 score of 61.45%, outperforming baseline algorithms.
  arXiv  Detail & Related papers  (2023-11-18T13:30:01Z)
• Extraction of Atypical Aspects from Customer Reviews: Datasets and Experiments with Language Models [0.7234862895932991]
  We introduce the task of detecting atypical aspects in customer reviews. To facilitate the development of extraction models, we manually annotate benchmark datasets of reviews in three domains. We evaluate a number of language models, ranging from fine-tuning the instruction-based text-to-text transformer-T5 to zero-shot and few-shot prompting of GPT-3.5.
  arXiv  Detail & Related papers  (2023-11-05T16:15:50Z)
• Impression-Aware Recommender Systems [53.48892326556546]
  We present a systematic literature review on recommender systems using impressions.<n>We define a theoretical framework to delimit recommender systems using impressions and a novel paradigm for personalized recommendations, called impression-aware recommender systems.
  arXiv  Detail & Related papers  (2023-08-15T16:16:02Z)
• Topic-Level Bayesian Surprise and Serendipity for Recommender Systems [0.0]
  A recommender system that optimize its recommendations solely to fit a user's history of ratings for consumed items can create a filter bubble. One approach to mitigate this undesired behavior is to recommend items with high potential for serendipity. We propose a content-based formulation of serendipity that is rooted in Bayesian surprise and use it to measure the serendipity of items after they are consumed and rated by the user.
  arXiv  Detail & Related papers  (2023-08-11T20:05:01Z)
• Explainability in Music Recommender Systems [69.0506502017444]
  We discuss how explainability can be addressed in the context of Music Recommender Systems (MRSs) MRSs are often quite complex and optimized for recommendation accuracy. We show how explainability components can be integrated within a MRS and in what form explanations can be provided.
  arXiv  Detail & Related papers  (2022-01-25T18:32:11Z)
• SIFN: A Sentiment-aware Interactive Fusion Network for Review-based Item Recommendation [48.1799451277808]
  We propose a Sentiment-aware Interactive Fusion Network (SIFN) for review-based item recommendation. We first encode user/item reviews via BERT and propose a light-weighted sentiment learner to extract semantic features of each review. Then, we propose a sentiment prediction task that guides the sentiment learner to extract sentiment-aware features via explicit sentiment labels.
  arXiv  Detail & Related papers  (2021-08-18T08:04:38Z)
• PURS: Personalized Unexpected Recommender System for Improving User Satisfaction [76.98616102965023]
  We describe a novel Personalized Unexpected Recommender System (PURS) model that incorporates unexpectedness into the recommendation process. Extensive offline experiments on three real-world datasets illustrate that the proposed PURS model significantly outperforms the state-of-the-art baseline approaches.
  arXiv  Detail & Related papers  (2021-06-05T01:33:21Z)

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.