Evaluating narrative-driven movie recommendations on Reddit

Eberhard, Lukas; Walk, Simon; Posch, Lisa; Helić, Denis

doi:10.1145/3301275.3302287

Cited by 16 publications

(15 citation statements)

References 21 publications

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“…The empirical analysis we present in this paper is an important step towards a better understanding of the shopping patterns and preferences of customers in different settings. This, in turn, builds the foundation for further research and several practical applications in RFID-based environments that require accurate user models, such as narrative-driven [8] and context-aware [1] product recommendations.…”

Section: Introductionmentioning

confidence: 90%

Mind the Gap: Exploring Shopping Preferences Across Fashion Retail Channels

Wölbitsch¹,

Hasler²,

Walk³

et al. 2020

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization

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Over the course of the last decade, online retailers have demonstrated that knowledge about customer preferences and shopping patterns is an important asset for running a successful business. For example, customer preferences and shopping histories are the foundation for recommender systems that support the search for relevant products to buy online. With the increasing adoption of modern technologies, traditional retailers are able to collect similar data about customer behavior in their stores. For example, smart fitting rooms allow to track interactions of customers with products beyond the scope of a traditional retail store. In this paper we explore how customers of a large international fashion retailer buy products online and in brick-and-mortar stores, and uncover significant differences between the two domains. In particular, we find that online customers frequently focus on buying products from one specific category, whereas customers in brick-and-mortar stores often buy a more diverse range of product types. Further, we investigate products that customers take into fitting rooms, and we find that they frequently deviate from, and complement purchases. Finally, we demonstrate how our findings impact practical applications, illustrated using recommender systems, and discuss how shopping baskets from different domains can be leveraged.

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Section: Introductionmentioning

confidence: 90%

Mind the Gap: Exploring Shopping Preferences Across Fashion Retail Channels

Wölbitsch¹,

Hasler²,

Walk³

et al. 2020

Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization

Self Cite

View full text Add to dashboard Cite

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“…Intelligent systems that can augment and enhance human understanding often require large amounts of human-generated text data generated in a social context. Social media data collected by Pushshift has been used already by researchers in computational linguistics and natural language processing [51,54,70,78,122,129,131], recommender systems [21,38,66,69], intelligent conversational systems [1,59,80], automatic summarization [120], entity recognition [37], and other fields associated with the development of systems that can sense, reason, learn, and predict.…”

Section: Author Flair Textmentioning

confidence: 99%

The Pushshift Reddit Dataset

Baumgartner¹,

Zannettou²,

Keegan³

et al. 2020

Preprint

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Social media data has become crucial to the advancement of scientific understanding. However, even though it has become ubiquitous, just collecting large-scale social media data involves a high degree of engineering skill set and computational resources. In fact, research is often times gated by data engineering problems that must be overcome before analysis can proceed. This has resulted recognition of datasets as meaningful research contributions in and of themselves.Reddit, the so called "front page of the Internet," in particular has been the subject of numerous scientific studies. Although Reddit is relatively open to data acquisition compared to social media platforms like Facebook and Twitter, the technical barriers to acquisition still remain. Thus, Reddit's millions of subreddits, hundreds of millions of users, and hundreds of billions of comments are at the same time relatively accessible, but time consuming to collect and analyze systematically.In this paper, we present the Pushshift Reddit dataset. Pushshift is a social media data collection, analysis, and archiving platform that since 2015 has collected Reddit data and made it available to researchers. Pushshift's Reddit dataset is updated in real-time, and includes historical data back to Reddit's inception. In addition to monthly dumps, Pushshift provides computational tools to aid in searching, aggregating, and performing exploratory analysis on the entirety of the dataset. The Pushshift Reddit dataset makes it possible for social media researchers to reduce time spent in the data collection, cleaning, and storage phases of their projects.

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“…In this setting, users do not ask for recommendations directly, but rather have a more natural conversation with a Wizard, and receive recommendations based on this discussion. Previous approaches such as the "hierarchy of recommendation goals" (Kang et al, 2017) and "narrative-driven recommendation" (Bogers and Koolen, 2017;Eberhard et al, 2019) are not applicable under these conditions.…”

Section: Introductionmentioning

confidence: 99%

You Sound Like Someone Who Watches Drama Movies: Towards Predicting Movie Preferences from Conversational Interactions

Volokhin¹,

Ho²,

Rokhlenko³

et al. 2021

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Langua

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The increasing popularity of voice-based personal assistants provides new opportunities for conversational recommendation. One particularly interesting area is movie recommendation, which can benefit from an open-ended interaction with the user, through a natural conversation. We explore one promising direction for conversational recommendation: mapping a conversational user, for whom there is limited or no data available, to most similar external reviewers, whose preferences are known, by representing the conversation as a user's interest vector, and adapting collaborative filtering techniques to estimate the current user's preferences for new movies. We call our proposed method ConvExtr (Conversational Collaborative Filtering using External Data), which 1) infers a user's sentiment towards an entity from the conversation context, and 2) transforms the ratings of "similar" external reviewers to predict the current user's preferences. We implement these steps by adapting contextual sentiment prediction techniques, and domain adaptation, respectively. To evaluate our method, we develop and make available a finely annotated dataset of movie recommendation conversations, which we call MovieSent . Our results demonstrate that Con-vExtr can improve the accuracy of predicting users' ratings for new movies by exploiting conversation content and external data.

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Evaluating narrative-driven movie recommendations on Reddit

Cited by 16 publications

References 21 publications

Mind the Gap: Exploring Shopping Preferences Across Fashion Retail Channels

Mind the Gap: Exploring Shopping Preferences Across Fashion Retail Channels

The Pushshift Reddit Dataset

You Sound Like Someone Who Watches Drama Movies: Towards Predicting Movie Preferences from Conversational Interactions

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